If you’re interested in building an e-commerce business in 2024, there’s no avoiding the 500lb gorilla in the room: Amazon. Whether you want to or not, you’ll need to compete with them for a share of almost any market segment you target. The problem is that competing with them in scale, reach, and resources is impossible. So, if you want to stand a chance of succeeding, you need to learn how to do more with less.

Fortunately, there’s a guiding principle you can adhere to that will point the way toward the right strategy. It’s that your customers won’t care how you deliver their desired products and experience—they only care that you do. You don’t need massive warehouses, thousands of customer service reps, or your own logistics business. Instead, you can make strategic investments to mimic those capabilities in other ways. Here’s where to start and a few areas to target as you get your e-commerce venture off the ground.

Deliberately design your brand around your niche

The first thing you will need to do is to figure out how you want to brand your e-commerce site. Once you select a niche, you can work backward to design your brand around it. As you do, remember that your brand is the first thing any potential customer will see. Therefore, you need it to communicate your value proposition in a language your target audience understands. To strike the right note, it’s a good idea to research branding trends and see if any overlap with your target audience. You can always evolve your brand later as customer tastes change.

Begin with a flawless shopping experience

One of Amazon’s most significant weaknesses is that it tries to be all things to all people. As a result, the shopping experience on the platform is hit-or-miss, depending on the product you’re looking for. Additionally, they let 3rd-party sellers pay for sponsored placement, making shoppers’ searches an often frustrating experience.

As an e-commerce startup, you don’t have to try and sell every kind of product. So, you can spend the time and money necessary to refine the shopping experience for your niche. Using an e-commerce-focused website builder to create your initial site is OK. It will save you money you can then spend on thoroughly testing the shopping experience. Focus-group usability testing is essential and will let you hone that experience to perfection.

You should also pay careful attention to your site’s checkout process flow. That’s one of the common weaknesses that tends to doom e-commerce startups. Focus on eliminating as many purchase barriers as possible. You want customers to go from adding a product to their cart to completing their purchase as quickly as possible.

Finally, you must provide a post-sale experience that’s at least as good as what Amazon offers. That begins with timely shipping notifications and allowing customers to track product deliveries. Fortunately, multiple shipping software platforms let you add that functionality without building the infrastructure and spending a fortune.

Go all-in on customer service automation

Your customer service operation will be the second-most important element of your business’s customer experience. However, you won’t launch your business with a large customer service staff unless you’re working with a deep-pocketed angel investor. The good news is that your customers don’t have to know that. To ensure they don’t, you should go all-in on automation to deliver an excellent customer service experience.

Start by deploying front-line customer service chatbots to weed out and satisfy simple customer inquiries. You can back that up with an automated customer service email system to ensure quick first response times for all queries. Then, consider contact center outsourcing to offer live agents to customers requiring more complex assistance. As long as you can resolve customer issues without long wait times, the scale of your operation will be indistinguishable from that of Amazon. The critical difference is that your operation will be orders of magnitude cheaper.

Don’t skimp on marketing

Although you’ll need to keep a tight lid on spending as you launch your business, be sure not to let that extend to your marketing efforts. As a rule of thumb, you should expect to spend at least 30% of your projected revenue on customer acquisition. As a niche retailer, you must lean heavily into building word-of-mouth momentum. That means creating an organic social presence and connecting with thought leaders in your niche. Doing that will turn your earliest customers into evangelists for your brand.

It is also a good idea to try and infuse all of your marketing efforts with data-driven insight. For example, you can use predictive marketing tactics to capitalize on anticipated customer behaviors. That’s one of the ways Amazon got to its position atop the global e-commerce market. And they’ve even turned their expertise into a product you can use to grow your business. It’s called Amazon Personalize, and you can use it to build a product recommendation engine that rivals the original. You can even plug it into your marketing processes to create personalized emails, social posts, and other customer-specific marketing materials.

Leverage freelancers for necessary skills

Finally, it is vital to embrace the importance of expertise early on in your e-commerce business journey. It’s self-defeating to try and do everything yourself; typically, it only ends in a costly lesson in futility. No matter your experience level, you will require subject matter experts in several disciplines to help you. The best way to acquire that help is by leveraging freelancers. You can even turn over entire processes to freelancers via knowledge process outsourcing. It’s an excellent way to get critical business processes up and running while avoiding the typical pitfalls new businesses often encounter. Plus, it will help you minimize costs while you do it.

Don’t try to be the next Amazon

Above all else, you should try to avoid the temptation to try and turn your business into the next Amazon. Recent history has no shortage of startups that tried to do that and failed. After all, there’s a reason that only large companies like Walmart, Target, and Rakuten have managed to go toe-to-toe with them and survive.

That isn’t to say that you should give up on growth. If you take a slow and steady approach and learn from your successes and failures, your e-commerce business will grow. And in the long term, you’ll have built a solid brand that may one day lay claim to the e-commerce crown, even if that day’s not today.

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The unfolding Temu-Shein lawsuit is drawing attention in the global retail sector, raising questions about its potential impact on industry standards and practices. This lawsuit, characterized by serious allegations from both parties, could have far-reaching consequences for competitive strategies and regulatory frameworks in online retail. As the case progresses, stakeholders and observers are keenly watching to see how it might influence business conduct and governance in this rapidly evolving market.

What triggered the Temu-Shein lawsuit?

The legal conflict between Temu and Shein was sparked by intense competition in the fast-fashion online retail market, where both companies vie for dominance. This rivalry intensified as both companies aimed to capitalize on the lucrative U.S. market, adopting aggressive marketing and operational strategies. The situation escalated when allegations of unethical business practices and intellectual property violations began to surface, leading to legal action.

How did the rivalry between Temu and Shein escalate into a lawsuit?

The rivalry between Temu and Shein escalated into a lawsuit when Shein accused Temu of engaging in practices that allegedly undercut Shein’s market position and violated various legal norms. As competition heated up, Shein alleged that Temu not only copied their business model but also engaged in direct actions that infringed upon Shein’s operational space and intellectual property. These accusations were serious enough to move the dispute from the commercial arena into the courtroom.

What specific allegations has Shein made against Temu in their lawsuit?

In their lawsuit, Shein has levied several serious allegations against Temu. Shein accuses Temu of being “an unlawful enterprise built on counterfeiting, theft of trade secrets, infringement of intellectual property rights, and fraud.” Specifically, Shein alleges that Temu has exerted control over its sellers to an extent that it dictates product listings and pricing, encourages infringement of intellectual property rights, and even prevents sellers from removing their products from Temu’s website after they have acknowledged such infringement. These allegations paint a picture of Temu not just as a competitor but as a company that systematically engages in unethical and illegal practices to gain market advantage.

How has Temu responded to the accusations from Shein?

In response to Shein’s accusations, Temu has taken a defensive stance, countering with its own allegations and legal actions. Temu has denied the claims made by Shein and accused Shein of similar unethical practices, including attempts to sabotage Temu’s business relationships with manufacturers and influencers. Temu’s counterclaims also suggest that Shein has engaged in misleading tactics aimed at damaging Temu’s reputation and market position. This tit-for-tat legal battle indicates that both companies are prepared to vigorously defend their business practices and reputations through legal means.

What are the broader implications of the Temu-Shein lawsuit for the global retail market?

The Temu Shein lawsuit could have significant implications for the global retail market, particularly in the fast-fashion and e-commerce sectors. This legal battle highlights the intense competition and the lengths to which companies might go to secure a competitive edge, raising questions about the sustainability and ethics of current business models in this industry. Moreover, the lawsuit could prompt regulatory scrutiny of business practices in e-commerce, potentially leading to stricter regulations on intellectual property rights, seller management, and competitive conduct.

Enhancing e-commerce logistics with AI

How might this lawsuit affect other players in the online retail sector?

The outcome of the Temu Shein lawsuit could set important legal precedents that affect other players in the online retail sector. If Shein’s allegations lead to a decisive legal victory, it might embolden other companies to pursue legal action against competitors for similar practices, leading to an increase in litigation within the industry. Conversely, if Temu successfully defends itself, it could deter such legal challenges in the future. Additionally, the lawsuit could encourage online retailers to scrutinize and possibly revise their policies on intellectual property, supplier relations, and competitive strategies to avoid similar legal challenges. This could lead to broader changes in how companies operate within the fast-paced and often murky waters of global online retail.

Media coverage of the Temu Shein lawsuit also has played a critical role in shaping public perception of both companies. As reports highlight the detailed accusations and counterclaims, they not only inform the public but also influence consumer sentiment towards both brands. Media portrayals that focus on allegations of unethical practices, such as counterfeiting and intellectual property theft, can tarnish brand reputations and affect customer loyalty. Furthermore, the coverage tends to magnify the dramatic aspects of the legal battle, possibly leading to a more critical view of the fast-fashion industry as a whole. This ongoing media scrutiny emphasizes the stakes involved and could sway public opinion significantly, depending on the narrative that gains prominence.
Following the resolution of their legal dispute, both Shein and Temu may need to reconsider and adapt their business strategies significantly. Firstly, there could be a push towards greater transparency in their operations and supply chain management to rebuild trust and ensure compliance with international trade and intellectual property laws. This might involve more rigorous oversight of product sourcing and selling practices to prevent any future legal challenges.

Both companies might invest more in innovation and original design to differentiate themselves from each other and from other competitors in the market. Such strategic shifts would not only aim to mitigate the damage from the lawsuit but also to position both companies better against potential future challenges in a highly competitive industry.

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In the digital age, where authenticity and ownership are paramount, the concept of cryptographic invisible signatures has emerged as a powerful tool. While most commonly associated with protecting digital art and media, this technology is now revolutionizing the world of physical products, particularly in the fight against counterfeiting.

This is not a “victimless crime,” either, as some would suggest. According to Frontier Economics, the global value of counterfeiting and piracy to be close to US$2.8 trillion by 2022, and net job losses will be between 4.2 to 5.4 million.

A Brief History

Invisible watermarking, the digital precursor to modern cryptographic invisible signature technology, has been employed in various forms since the late 20th century. Initially used to protect copyrighted images and videos, it involved embedding subtle data within the content itself, undetectable to the human eye but easily identifiable by specialized software. This technique served as a deterrent to unauthorized use and a means to trace the origin of copied material.

The evolution of digital watermarking led to its application in diverse fields, including audio recordings, software, and even printed documents, and the technique – which can fall into both the cryptographic and the steganographic fields – has even been used by spies and hackers to transfer secrets or deliver malicious payloads. 

While the digital world plays well with cryptographic techniques, using these techniques in the “real world” is a significant challenge.

The Rise of Cryptographic Invisible Signatures for Physical Products

In recent years, a new player has entered the arena of cryptographic invisible signatures: Ennoventure. The company has adapted cryptographic principles to create invisible signatures for physical products, particularly packaging.

“Adapting our cryptographic invisible signatures to physical packaging was an exciting challenge,” Padmakumar Nair, co-founder and CEO at Ennoventure, said. “Key hurdles included ensuring adhesion to diverse materials, signature resilience during handling and transit, scalability without disrupting production, user-friendly scanning, compliance with regulations, and effective communication of the technology’s benefits.”

Ennoventure’s technology addresses a long-standing problem in the packaging industry: the pervasive issue of counterfeiting. Fake products not only erode brand trust and cause financial losses but also pose significant risks to consumer safety, especially in industries like pharmaceuticals, automotive, and food.

The Power of Invisible Cryptographic Signatures

Unlike traditional security measures such as holograms or barcodes, which can be replicated, invisible cryptographic signatures offer a higher level of security. These signatures are embedded directly into the product’s packaging or label during the manufacturing process, making them virtually impossible to duplicate.

“The technology utilizes a combination of unique identifiers and encryption algorithms, ensuring that each product carries a distinct and verifiable signature,” Nair said. “This signature can be easily scanned using a smartphone, providing consumers and retailers with instant verification of authenticity.”

Ennoventure’s solution goes beyond mere authentication. It offers a comprehensive suite of features, including supply chain tracking, data analytics, and consumer engagement tools. This holistic approach empowers brands to protect their products, engage with customers, and gain valuable insights into consumer behavior.

Overcoming Industry Challenges

While the benefits of invisible cryptographic signatures are clear, Ennoventure acknowledges the challenges of adoption within the packaging industry.

“The packaging industry’s hesitance to adopt invisible cryptographic signatures stems from several factors,” Nair said. “Lack of awareness and understanding of the technology, misconceptions about high upfront costs and integration complexities, concerns about signature durability across various materials, regulatory compliance difficulties, and the need for reliable verification methods all contribute to this reluctance.”

Ennoventure has taken proactive steps to address these concerns. Its technology is designed to be easily integrated into existing manufacturing processes without requiring significant investments or disruptions. They also emphasize education and outreach to raise awareness of the technology’s benefits and dispel misconceptions.

Real-World Impact

Ennoventure’s technology has already made a significant impact in various industries and for consumers alike.

“For brands, it protects products from counterfeiting, safeguards revenue, preserves brand reputation, aids in regulatory compliance, and strengthens supply chain security,” Nair said. “For consumers, it instills confidence in product authenticity, especially for critical items like medicines, food, and cosmetics. It empowers consumers to make informed choices and stay aware of the risks of counterfeit goods.”

Ennoventure’s technology has been implemented in real-world scenarios with measurable impact. For a global agrochemical brand, it facilitated a scalable WhatsApp-based authentication system for farmers, significantly reducing counterfeiting in rural supply chains. For a global agro-industrial conglomerate, it enabled quick smartphone authentication without additional costs, decreasing counterfeit rates. For a leading FMCG brand, it allowed for early detection and prompt intervention, preventing the spread of counterfeit goods.

Looking ahead, Ennoventure is committed to continued innovation and expansion. Its focus on research and development ensures that its solutions remain at the forefront of anti-counterfeiting technology. 

“We plan to expand our services across industries and regions, invest in R&D to enhance our solutions and develop new features, and continue our commitment to staying ahead of the curve in the fight against counterfeiting,” Nair said.

A New Era of Product Authentication

The journey of invisible signatures, from safeguarding digital art to protecting tangible goods, demonstrates the remarkable evolution of security measures. Ennoventure’s groundbreaking application of cryptographic invisible signatures to physical packaging marks a pivotal moment in the fight against counterfeiting. By addressing industry challenges head-on and delivering a user-friendly, scalable, and effective solution, Ennoventure is not only empowering brands to safeguard their products and reputations but also giving consumers the confidence to make informed choices.

As this technology continues to evolve and expand its reach, the implications for a safer and more transparent marketplace are profound. The era of invisible signatures is here, and it’s poised to redefine how we authenticate and trust the products we encounter in our everyday lives.

In the age of digital transactions and contactless payments, many have declared the humble paper check as an obsolete relic of the past. However, this claim is far from accurate. Despite the rise of electronic payment methods, checks continue to play a crucial role in various financial transactions, and their demise has been greatly exaggerated.

The enduring importance of checks

While it’s true that the usage of checks has declined over the years, they remain an essential payment method for many individuals and businesses. Checks offer several advantages that make them indispensable in certain situations:

• Accessibility: Not everyone has access to digital payment methods or feels comfortable using them. Checks provide a familiar and straightforward way for individuals of all ages and backgrounds to conduct financial transactions.

• Recordkeeping: Checks offer a tangible paper trail, making them valuable for record-keeping purposes. This feature is particularly important for businesses that need to maintain detailed financial records for accounting and tax purposes.
• Security: Contrary to popular belief, checks can be a secure payment method when used correctly. They require physical signatures and can be traced back to the issuer, providing an additional layer of security compared to some electronic payment methods.
• Flexibility: Checks can be used for various transactions, from paying bills and rent to making large purchases or settling legal obligations. Their versatility makes them a valuable tool in different financial scenarios.

Addressing common myths

Despite their continued relevance, several myths persist regarding the use of checks. Let’s address some of the most common misconceptions:

• Checks are obsolete: As discussed earlier, checks remain an essential payment method for many individuals and businesses. While their usage may have declined, they are far from obsolete and continue to serve important functions.
• Checks are inconvenient: While checks may require more effort than some digital payment methods, they are not necessarily inconvenient. For those who are familiar with the process, writing and depositing checks can be straightforward and efficient. Additionally, with the advent of mobile banking apps, depositing checks has become more convenient than ever.
• Checks are unsafe: When handled properly, checks can be a secure payment method. They require physical signatures and can be traced back to the issuer, making them less susceptible to certain types of fraud compared to electronic payments. However, it’s important to follow best practices, such as safeguarding checks and promptly reporting any lost or stolen checkbooks.
• Checks are expensive: While checks may involve some processing fees, the costs associated with their use are often minimal, especially for individuals and small businesses. Many financial institutions offer free or low-cost check-writing services, making checks an affordable payment option.

In certain situations, individuals may need to cash a check but lack proper identification, and one common issue that arises is how to cash a check without ID. This can be a challenge, as most financial institutions require a valid ID to cash checks as a security measure. However, there are alternative methods that can be explored, such as having the check endorsed by the issuer or providing other forms of identification like a utility bill or credit card statement.

The future of checks

While digital payment methods continue to gain popularity, it’s unlikely that checks will disappear completely anytime soon. They serve specific purposes and cater to various demographics and industries that still rely on paper-based transactions.

However, financial institutions and payment processing companies still need to adapt and offer innovative solutions that bridge the gap between traditional and modern payment methods. This could include enhancing mobile check deposit capabilities, streamlining check processing, and improving security measures to further bolster the reliability and convenience of check-based transactions.

Ultimately, the decision to use checks or digital payment methods will depend on individual preferences, circumstances, and the specific requirements of each transaction. By understanding the myths and realities surrounding checks, consumers and businesses can make informed choices and leverage the payment methods that best suit their needs.

Featured image credit: Freepik

The ability to track parcels in real-time is emerging as a game-changer for online retailers. As consumers increasingly expect transparency and control over their shopping experience, real-time parcel tracking has transitioned from a mere feature to a fundamental necessity for E-commerce. From enhancing customer satisfaction to optimizing operational efficiency, the adoption of tracking technology is reshaping the landscape of online retailing.

At the heart of the real-time tracking revolution lies the evolving expectations of modern consumers. In an era where instant gratification is the norm, shoppers demand visibility into the whereabouts of their orders from the moment of purchase to delivery. According to a report published by Bettermile in collaboration with Consumer Code, a staggering 64% of consumers expect real-time tracking to be available for every parcel they receive. This growing demand underscores the pivotal role of real-time tracking in meeting customer expectations and delivering a seamless shopping experience.

One of the primary benefits of tracking for online stores is its ability to instill confidence and trust among customers. By providing real-time updates on the status of their orders, online retailers can alleviate concerns about order accuracy, delivery delays, and package security. Clear and consistent communication throughout the shipping process reinforces customer confidence and fosters loyalty, ultimately driving repeat purchases and positive word-of-mouth referrals.

Moreover, real-time tracking offers online stores a competitive edge in an increasingly crowded marketplace. In today’s hyper-competitive e-commerce landscape, where differentiation is key, the availability of real-time tracking can serve as a powerful differentiator for online retailers. By offering a superior delivery experience characterized by transparency, reliability, and responsiveness, online stores can attract and retain customers in an ever-evolving market environment.

Beyond its impact on customer satisfaction, real-time tracking also yields tangible benefits for operational efficiency and cost management. By providing real-time visibility into the movement of parcels throughout the delivery network, online retailers can not only optimize route planning but also minimize delivery times and reduce operational costs. With access to granular data on delivery performance and customer preferences, online stores can make data-driven decisions to streamline their logistics operations and enhance overall efficiency.

The implementation of real-time tracking technology also opens up new opportunities for innovation and value creation in the e-commerce ecosystem. From personalized delivery options to enhanced customer engagement initiatives, online retailers can leverage real-time tracking data to develop targeted marketing campaigns, optimize inventory management, and deliver tailored shopping experiences. By harnessing the power of real-time tracking, online stores can unlock new revenue streams and drive business growth in a dynamic and competitive marketplace.

However, while the benefits of real-time tracking are undeniable, online stores must navigate challenges and considerations in its implementation. From data privacy and security concerns to integration with existing systems and processes, online retailers must carefully evaluate the technical, operational, and regulatory implications of adopting real-time tracking technology. By addressing these challenges proactively and investing in robust infrastructure and partnerships, online stores can maximize the value of real-time tracking while mitigating potential risks and vulnerabilities.

In conclusion, the adoption of real-time tracking is rapidly becoming a must-have for online stores seeking to thrive in today’s digital economy. From enhancing customer satisfaction and loyalty to driving operational efficiency and innovation, real-time tracking offers online retailers a strategic advantage in an increasingly competitive marketplace. By prioritizing transparency, reliability, and responsiveness in their delivery operations, online stores can differentiate themselves, delight customers, and position themselves for long-term success in the ever-evolving world of e-commerce.

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Looking at a T-shirt or sneakers, no one would think that these lightweight items could be comparable in terms of their impact on the planet’s ecology to smoking metallurgical plants, cars, or flaming torches over oil wells. However, any doubts about this are immediately dispelled when you consider the scale of the lightweight industry. Not everyone among the 8 billion inhabitants of the Earth has a car, but everyone wears some clothing, with the vast majority being shoes. The volume of the global fashion industry’s production is estimated at 100 billion items per year.

“One can only hope that the best practices of the most environmentally advanced global brands will gradually be adopted in developing countries, driven by the growing awareness of consumers.”

Danil Kontsevoy, co-founder and CEO of Digit Trading LLC and Biky Bikes Corp.

Looking at a T-shirt or sneakers, no one would think that these lightweight items could be comparable in terms of their impact on the planet’s ecology to smoking metallurgical plants, cars, or flaming torches over oil wells. However, any doubts about this are immediately dispelled when you consider the scale of the lightweight industry. Not everyone among the 8 billion inhabitants of the Earth has a car, but everyone wears some clothing, with the vast majority being shoes. The volume of the global fashion industry’s production is estimated at 100 billion items per year.

What’s even worse is that the wealthier a country is, the more often people buy new clothing and discard the old. Each year, up to 85% of purchased items end up in landfills, as, on average, a person wears an item for less than a year. The weight of discarded clothing each year, by the most modest estimates, is about 50 million tons.

The World Economic Forum provides the following data: in 2014, people purchased clothing 60% more often than in 2000, yet wore it half as frequently. European fashion houses offered two collections per year at the beginning of the 21st century. In 2011, it was five. Now, brands release 12, 16, and even 24 drops annually. Due to consumers’ constant desire to buy new clothing, textile production and consumption volumes are rapidly increasing. From 2000 to 2015, annual clothing production doubled, while usage (the number of times it was worn) decreased by 36%. By 2030, global consumption will increase by another 63%, from 62 million to 102 million tons.

According to the United Nations Conference on Trade and Development (UNCTAD), the fashion industry uses around 93 billion cubic meters of water annually. This amount is sufficient to meet the needs of five million people, and currently, about half a million tons of microfibers are dumped into the ocean each year, equivalent to 3 million barrels of oil. According to the Ellen MacArthur Foundation, in 2015, the ecological footprint from textile material production amounted to 1.2 billion tons of CO2. This amount exceeds the carbon dioxide emissions from all international air travel and maritime shipping.

The most common pair of sneakers consists of 65 parts. Among the materials used are synthetic rubber, nylon, and plastic. Synthetic rubber takes up to 150 years to decompose, while other materials can take up to 700 years!

At the same time, there are already enough solutions worldwide that would allow the fashion industry to become more environmentally friendly. These solutions can be divided into two major groups related to changing the technologies of clothing and footwear production and consumer behavior.

The implementation of these solutions faces serious obstacles. From an economic standpoint, eco-friendly technologies often cannot compete with traditional ones, and changing human behavior is very challenging, especially when people are required to make additional efforts. However, some optimism is inspired by the fact that human consciousness is gradually changing. Overall, the trend towards closer interaction between brands and consumers in the coming years will reach a new level. Audiences become critically concerned about the values that a particular brand conveys. A quarter of respondents in a 2021 McKinsey survey in the UK stated that their clothing purchase decisions were influenced by sustainable development principles. In India, 94% of consumers are willing to pay more for “ethical” products, and half of the youth in China aim to buy as few items from the fast fashion category as possible.

Thus, consumer behavior will push manufacturers to implement eco-friendly solutions in production. And new technologies are being developed or already being adopted in large quantities. This primarily involves the use of eco-friendly materials, including those derived from recycled waste. Some manufacturers use materials such as paper fiber, coffee grounds, oil from used coffee beans, and even wildflowers in clothing production. Similarly, mushroom-based fabric has a texture resembling natural leather but reduces the number of farms and accelerates production. Materials based on seaweed are also promising, being highly resistant to external influences and easily recyclable. This is biodegradable and low-toxicity textile. For example, Tommy Hilfiger’s collection includes hoodies made with seaweed-based material. By the way, there are very interesting projects for producing clothing from natural fish scales. Hugo Boss and H&M already use Piñatex fiber made from pineapple leaves.

In the textile dyeing process, chemicals and heavy metals are usually used, but in scientific laboratories, harmless bacteria have been created through genetic modification capable of dyeing fabrics in different colors depending on their species.

Recycling technologies inspire great hope, i.e., the production of clothing and footwear from recycled waste. For instance, the brand Adidas created sneakers from recycled ocean debris. The collection is partially made from recycled wool and polyester, as well as organic cotton. Mango, like H&M, recycles used items that can be brought to collection points in the brand’s stores. Collina Strada, Chopova Lowena, and Bode use fabrics left over from other productions or sew ready-made and no longer needed items.

From denim scraps, which involve the extensive use of water and chemicals in production and dyeing, one can create new trendy clothing. And instead of a car, more environmentally friendly modes of transportation can be used. For example, a boys and girls bike from Biky Bikes is a plus for the health of the younger generation. In some cases, discarded items such as ropes, parachutes, ocean debris, worn-out car tires, old denim, bicycle tires, banner scraps, hazmat suits, gas mask parts, and even coffee grounds serve as raw materials in the eco-friendly fashion industry. After processing, a new fabric is obtained without compromising material quality, and various accessories and footwear are also crafted.

Changes in consumer behavior are worthy of a separate discussion, but it is essential to emphasize a very significant aspect. A crucial path towards making consumption more eco-friendly involves not discarding worn clothing and footwear but passing them on to those willing to continue wearing or repairing them. To achieve this, there is a need to develop redistribution services for second-hand clothing and footwear, with clothing and footwear manufacturers themselves participating – and already participating – in their creation.

Major companies such as Lululemon, Patagonia, and Dr. Martens have begun offering clothing resale through their own and partner services. The premium brand clothing online store Farfetch announced plans to acquire the B2B resale technology platform Luxclusif, while the marketplace Amazon collaborates with the reseller What Goes Around Comes Around, selling used luxury brand handbags. At the same time, industry giants H&M, Zalando, and Uniqlo are expanding their clothing repair services, promoting conscious and eco-friendly consumption.

According to experts surveyed by McKinsey, consumers are currently shifting towards lower-priced brands and actively seeking sales. Against this backdrop, the second-hand segment is expected to grow, and the demand for clothing resale platforms is projected to increase.

One can only hope that the best practices of the most environmentally advanced global brands will gradually be adopted in developing countries, driven by the growing awareness of consumers.

Featured image credit: Glenn Carstens-Peters/Unsplash

The way we pay for our favorite fast food meals may be experiencing a major shift, with restaurants embracing fast food restaurants surge pricing.

The concept – already familiar to ride-share users – means the price you pay for a burger, fries, or frosty may depend on the time of day and other demand factors.

This strategy, also known as dynamic pricing, fluctuates prices based on demand. Your lunchtime burger might cost more than the same burger in the afternoon, affecting your consuming habits. And that’s a valuable data for fast food restaurants to study on!

Fast food restaurants surge pricing concept is something never heard of

The fast food restaurants surge pricing concept is something never heard of, but Wendy’s plans to explore dynamic pricing made major headlines. Evidence suggests other big-name fast-food companies are also testing this model in select locations. Restaurants, pubs, and even sports stadiums are adjusting prices based on demand to maximize profits.

Restaurants see benefits in surge pricing. Higher prices during peak times boost revenue. Additionally, increased prices could subtly shift some customers towards slower hours, improving kitchen efficiency.

The data collected from fast food restaurants surge pricing can provide valuable insights into customer behavior and preferences.

What is the surge pricing meaning?

Surge pricing, or dynamic pricing, means that prices aren’t fixed. Instead, they fluctuate based on factors like:

• Time of day: Peak lunch and dinner hours might see higher prices
• Weather: A heatwave could drive up drink prices
• Local events: Prices may increase when there’s a concert or sporting event nearby
• Overall demand: If a location is particularly busy, prices could rise to slow down order volume a bit

Although it may seem purely profit-making, companies like Uber and Lyft have long used this concept to better analyze user patterns.

But why the fast food sector?

• Understanding demand patterns: Surge pricing would give fast-food restaurants detailed data on when their locations experience peak demand. This could be influenced by factors like time of day, weather, local events, or even competing restaurant promotions. This data is crucial for making informed business decisions
• Targeted staffing and inventory: Armed with demand information, restaurants could optimize their staffing schedules, ensuring enough employees are present during peak periods while reducing labor costs at slower times. They could also more accurately forecast inventory needs to prevent shortages of popular items
• Pricing optimization: Data insights would allow restaurants to fine-tune their dynamic pricing algorithms. They could analyze how customers respond to different price adjustments, ensuring the increases maximize revenue without drastically suppressing demand
• Identifying new opportunities: Detailed customer data could reveal gaps in the market. A restaurant might discover an untapped demand for late-night meals in a specific area, or spot a trend of increased burger orders during specific weather conditions. This could guide menu expansions or local promotions

Like ride-sharing companies, fast food restaurants could also use surge pricing data to send tailored discounts. They could incentivize customers to order during traditionally slow periods or target individuals who frequently order high-demand items during surge times, offering them slightly lower prices

Not really well-received

Consumers, however, have mixed feelings about fast food restaurants surge pricing decisions. Many dislike being ‘penalized’ for eating during popular times and demand transparency around price fluctuations. Aggressive pricing models could lead to customer boycotts and negative press.

Andi on X, for instance, has criticized fast food restaurants surge pricing decisions in a rather ironic way:

Starting tonight, I too will be implementing surge pricing. If the kids need me after 9 o’clock, it’s gonna cost extra.

— Andi (@smiles_and_nods) February 27, 2024

The success of surge pricing in fast food hinges on its implementation. Clear communication about pricing shifts and reasonable price increases are key. The use of digital menu boards and sophisticated algorithms make this strategy easier for restaurants to manage.

Even with surge pricing, savvy consumers have ways to save. Dining during off-peak hours, taking advantage of app offers, and seeking out special deals can help keep costs down, ultimately utilizing Fast food restaurants surge pricing decisions for their benefits.

Featured image credit: Vectonauta/Freepik.

As a seasoned consumer, I’ve encountered my fair share of customer service nightmares that have left me questioning the very fabric of corporate empathy. Whether it’s airlines that won’t accept you paid for extra services and make you pay again at check-in, products that don’t work as advertised and need returning, or getting refunds on food that was never delivered, I’ve been subject to some of the worst customer service I’ve ever received over the last year.

Allow me to share my personal journey through the treacherous landscape of customer service, where nightmares often outweigh pleasant dreams, and the one Finnish startup that provided solace.

The Customer Service Outsourcing Maze

One of the first challenges I faced was the prevalence of companies outsourcing their customer service to various teams, each handling different channels like social media and the company website. It’s like dealing with a hydra – cut off one head and two more sprout in its place. These fragmented services often left me feeling like a lost traveler, desperately seeking assistance in a foreign land.

To make matters worse, these outsourced teams seemed disconnected from the decision-making powerhouse within the company. My pleas for a resolution often echoed through a labyrinth of scripted responses, leaving me in a loop of frustration. The lack of a direct line to someone with the authority to make decisions only intensified the nightmare.

The Dreaded “NoReply” Email

In the digital age, companies have embraced automation, leading to the rise of the dreaded “noreply” email address. My inbox became a graveyard for one-way communication, filled with automated responses that did nothing to address my concerns. It felt like I was shouting into the void, with my cries for help falling on deaf, automated ears.

AI is Making Things Worse

In my journey through the customer service maze, AI emerged as a potential solution. However, it became evident that while AI has strengths, it may not be the ultimate answer for the challenges ahead. In fact, for those companies that don’t embrace genuine customer service, it’s just another tool to reduce costs and push customers to the point where they give up on their claims.

AI struggles to replicate human empathy, which is crucial for handling complex and emotional customer issues, and it can’t yet handle complex scenarios that demand human critical thinking and creativity. While there have been considerable advancements in natural language processing (NLP) and natural language understanding (NLU), AI grapples with nuances in human language, risking misinterpretations.

Customers seek solutions, human interaction, and reassurance, areas where AI may feel cold and automated. While AI enhances certain aspects of customer service, a balanced approach combining AI efficiency with human empathy and adaptability is essential for a truly exceptional customer experience. In the ongoing journey through customer service complexities, it’s evident that the future lies in a harmonious collaboration between AI and the invaluable human touch.

Better Business? Maybe Not

Some friends told me to go through the Better Business Bureau (BBB) to resolve my US-based issues, but I found it ineffective and troublesome.

Firstly, it operates on a voluntary membership model, meaning not all businesses are listed, and accreditation is optional. The pay-to-play aspect, where companies pay fees for accreditation, has raised concerns about potential biases in ratings. The BBB’s subjective rating criteria, lack of regulatory power, and inconsistent handling of complaints are additional factors to consider. 

With the rise of online review platforms, consumer reliance on the BBB should be accompanied by an awareness of its limitations and the broader landscape of available information.

NoNoNo: A Finnish Savior in the Darkness

In my quest for resolution, I stumbled upon Tampere-based startup NoNoNo, a service dedicated to recovering refunds and compensation on behalf of frustrated consumers. I no longer had to endure the pain of navigating customer service mazes or wrestling with automated email systems. NoNoNo became my advocate, sparing me the agony of handling disputes myself.

I spoke to CEO and founder Jaakko Timonen to gather his thoughts on the current situation and why consumers like me are disenchanted. 

“I created NoNoNo because there are many businesses that have made it too hard for customers to get human customer service,” Timonen said. “I think that C-level executives are too focused on short-term financial results. Technology has made focusing on savings and implementing AI and bots attractive instead of investing in human customer service that would pay off in the long run.”

A Call for Change

As consumers, we deserve better. The nightmares of navigating through disconnected customer service channels and facing the cold shoulder of automated replies should not be the norm. Companies must reevaluate customer service strategies, fostering transparency, accessibility, and empathy.

While some businesses have embraced customer-centric models, others still have a long road ahead. Until then, startups like NoNoNo provide a much-needed lifeline for those drowning in customer service despair. It’s time for companies to wake up from the nightmare and realize that a satisfied customer is the best business strategy for retention and repeat purchases.

This article original started in ArcticStartup and is reproduced with permission

Generation Alpha (Gen A), the first cohort entirely immersed in the digital world, emerges. This group, currently navigating the complexities of a climate crisis and a pandemic, has an unprecedented ability to spend money at a younger age than their more experienced predecessors. Born entirely in the 21st century, the eldest of Generation Alpha are around 13 years old, while the youngest are expected to arrive within the year.

Generation Alpha will have over 2 billion members

As the only generation with its roots fully in the 21st century, Generation Alpha, spanning from 2010 to 2024, is poised to become the largest in history, surpassing 2 billion individuals, according to social researcher Mark McCrindle, who not only named “Generation Alpha” but also set its chronological boundaries. Predominantly the offspring of millennials, their immediate forerunners, Generation Z, are projected to surpass Baby Boomers in the workforce by 2024, as noted by Glassdoor.

The formation of Gen A is ongoing, and the concept of a “generation” is continually evolving. While the full extent of Alpha’s influence remains uncertain, the signs of their significant impact are already too substantial to overlook.

For thousands of years, real-world experiences have been crucial for the development of the human brain. However, in the past few decades, there’s been a significant shift, with children increasingly engaging with digital devices for education, social interaction, and play. This raises a critical question: does this digital immersion enhance their cognitive abilities and intelligence, or does it, as some suggest, impede their developmental potential?

Gen A: Your new generation name

The digital world for Generation Alpha is vastly different from that of previous generations. The oldest members of this generation were born in the same year the iPad was launched, earning them the nickname “iPad kids.” Unlike the millennials who grew up with a pre-algorithm Facebook, centered around personal networks, Gen A is growing up with TikTok, a platform that broadens their exposure to a diverse range of content and creators. MaryLeigh Bliss, Chief Content Officer at YPulse, highlights this by saying, “Anyone can go viral at any moment.”

Millennial parents are introducing smartphones to their children around the age of 9. According to YPulse data, 79% of these parents report their children use social media, and 44% say their kids watch video content on smartphones weekly. Bliss observes, “They’re having a media-centric childhood in a way that is different because of the kinds of media they’re interacting with from incredibly young ages.”

Moreover, the presence of artificial intelligence has been a constant in the lives of Generation Alpha. From voice assistants like Siri and Alexa in their homes to educational tools like ChatGPT in schools, they have always known a world where AI and human experience intertwine. Mark McCrindle, a social researcher, aptly summarizes this by stating, “Alpha have only ever known a world of the blurring of AI and the human.” This seamless integration of technology sets Gen A apart, shaping a unique childhood experience.

The impact of COVID-19 on Generation Alpha

The COVID-19 pandemic stands as a defining event for Generation Alpha, fundamentally reshaping their interaction with the world. The pandemic has normalized online interactions for these young individuals, with many experiencing virtual schooling and adapting to parents working from home. However, this shift has not been without its challenges.

Educational benchmarks have seen a notable decline since 2020. There’s been a decrease in test scores across various subjects, coupled with a rise in student absenteeism. Tori Cordiano, a child and adolescent psychologist, points out the broader implications on social development. She notes, “Many of them were not in school at all in person, and many of them took much longer to come back consistently. We’re now seeing the holdover effects.” This lack of regular social interaction has impacted their ability to make friends and adapt to new environments, as Cordiano further explains, “They just haven’t had as much practice.”

On the flip side, Gen A has become adept at forming online connections. According to YPulse, 43% of millennial parents report their children participating in virtual playdates or engaging with friends in digital spaces, like Minecraft, beyond just Zoom calls. Cordiano holds a hopeful view, suggesting these online interactions could “translate into meaningful, ongoing and hopefully in-person relationships.” This duality of Generation Alpha’s experience – the struggle with traditional social skills and the proficiency in digital communication – highlights the complex nature of growing up in the midst of a global pandemic.

New consumer habits of Gen A

As Generation Alpha matures, their consumer behavior and financial autonomy are becoming increasingly significant. Brands have already begun to target this demographic with specialized marketing strategies. Jennifer Mapes-Christ, a market researcher at The Freedonia Group, notes the shift in approach: companies are engaging children on platforms like TikTok and YouTube, often using influencers. “It allows different types of people to see themselves in the products in a way they maybe didn’t before,” she explains. This approach reflects a deeper understanding of Generation Alpha’s diverse and evolving preferences.

Despite many in this group not yet reaching legal working age, they are already exhibiting financial independence. With the advent of payment apps, debit cards, and driving services tailored for youth, Gen A is navigating an environment where financial decisions and purchases are increasingly within their grasp. This emerging financial liberty marks a significant shift in how this generation interacts with the consumer world.

Generation Alpha and environmental awareness

Born during some of the hottest years on record, Gen A has a unique relationship with the environment and climate change. The National Oceanic and Atmospheric Administration noted that 2010, the year when this generation began, tied as the warmest year on record at the time. Fast forward, and the concern for climate change has only intensified, with 2023 poised to set new temperature records.

Tori Cordiano observes an increasing anxiety among youth regarding social issues, including climate change. She points out, “Kids are having a hard time disconnecting from torrents of information, causing a higher risk for burnout for the things that are important to them.” This heightened awareness is evident in a YPulse survey, where 87% of 13-15-year-olds agreed it is their generation’s responsibility to prevent further climate deterioration.

Generation Alpha demonstrates a deep engagement with social and political issues. Research from McCrindle’s firm reveals their concern for ending racism and alleviating poverty, irrespective of their personal experiences. Mark McCrindle summarizes this sentiment, “Alphas bring a sense of empathy because they are connected globally to the issues of their world.” This global connection and awareness position Gen A as a generation deeply intertwined with the pressing social and environmental issues of their time.

Digital development dilemma

In light of these considerations, it begs the question: Are we witnessing a transformative shift in cognitive development with Generation Alpha’s immersion in digital environments, or are we overlooking potential developmental pitfalls? As this generation navigates a world where digital and real-life experiences intertwine, the long-term implications on their intellectual and social growth remain an open, intriguing question.

Stuff Your Kindle Day, a notable event on Amazon, is swiftly gaining momentum as the go-to occasion for acquiring a vast array of free e-books, meticulously organized by preferred book retailers and romance sub-genres.

When is the Amazon Stuff Your Kindle Day?

With the anticipation building for the next Stuff Your Kindle Day, it’s crucial to mark your calendars. This exceptional chance to enrich your Kindle library occurs just four times annually. The scheduled dates for 2023 are March 31, June 30, September 20, and December 27.

How to do Stuff Your Kindle Day?

Participating in Stuff Your Kindle Day is straightforward and accessible to everyone, not just Amazon Prime members or Kindle Unlimited subscribers. It’s also not limited to those owning a Kindle device. Here’s how you can participate:

For Kindle users:

• Use your web browser to navigate to the Amazon website.
• Look for the free Kindle books offered as part of the Stuff Your Kindle Day event and select the ones you want.
• Once you ‘purchase’ these free books, they will appear in your Kindle app Library, as your Amazon account is synced with the app.

It’s important to note that this event is inclusive, allowing participation regardless of whether you own a Kindle, Nook, Kobo, or use ebook readers like Apple Books or Google Play​​.

Is this offer exclusive to Kindle users?

Stuff Your Kindle Day extends far beyond just Kindle users. For those who prefer using Nook or Kobo e-readers, or if you’re inclined towards the Kobo, Apple Books, or Google Play apps for ebook reading, “Stuff Your E-Reader” pages are also available. These pages offer thousands of free books compatible with all major e-reader platforms. New to the world of ebooks? Setting up an Amazon account is your first step. With the Kindle App, available on any device, you can easily access these free books. And for our Canadian readers, a similar process applies with Kobo! If you’re an Apple or Google Play user, you might already have an ebook app pre-installed on your phone or tablet.

What is the duration for reading the books after downloading them?

Wondering about the longevity of these downloads? The best feature is their permanence. Once downloaded to your Kindle or the Kindle App, these books are yours to keep indefinitely.

Am I eligible to participate in Amazon Stuff My Kindle?

For those with Kindle Unlimited, there’s more good news. You can absolutely participate in Stuff Your Kindle Day without tapping into any of your twenty Kindle Unlimited borrow slots. Let’s clarify the synergy between the Kindle store, the Kindle app, and Kindle Unlimited. They all work in conjunction, allowing you to augment your Kindle library permanently with a plethora of books, all without affecting your Kindle Unlimited borrowing capacity.

What is the Kindle Store?

The Kindle Store is essentially Amazon’s online hub for ebooks. Here, readers have the option to purchase books, securing them permanently, or to borrow books through a subscription service known as Kindle Unlimited. These Kindle ebooks are accessible on dedicated Kindle devices or through a compatible app installed on smartphones or tablets.

A frequent inquiry pertains to the Kindle app’s functionalities – specifically, why it only allows borrowing of Kindle Unlimited books. The reason lies in the policies of the Apple App Store and Google Play Store, which would claim a portion of the ebook sale as an ‘in-app purchase’. This arrangement could potentially erase Amazon’s profits on some books due to the royalty structure.

Kindle Unlimited in crisis: Amazon struggles with influx of AI-generated books

What is the process for purchasing books to read in the Kindle app?

Wondering how to purchase ebooks for the Kindle app? You can buy them to own forever through your phone’s web browser, be it Safari on an iPhone or Chrome on an Android. Keeping logged into Amazon both in the browser and the app is a practical approach. Any purchases made on Amazon’s website within the Kindle Store will then automatically synchronize with your app after a simple one-click action.

Many people find it more convenient to make these purchases on a computer, particularly during events like Stuff Your Kindle Day. It’s an efficient way to rapidly acquire a variety of free romance novels and other genres, enhancing your reading collection significantly.

Is it possible to acquire romance novels at no cost?

Absolutely, you can indeed “buy” romance novels for free in the Kindle Store. This feature is one of the most appealing aspects of the Kindle Store – it’s not limited to paid books. A wide selection of free books is also available. When you choose ‘BUY’ on a free book, as opposed to ‘BORROW’ within Kindle Unlimited, that book becomes yours permanently, at no cost.

Is there a limit of 20 books for the Kindle Unlimited library?

Regarding Kindle Unlimited, it’s true that you can only have 20 books in your Kindle Unlimited library at any one time. However, it’s important to note that this is just a segment of your entire Kindle library. Your Kindle library’s capacity extends far beyond the confines of Kindle Unlimited’s 20 borrow slots. You have the freedom to purchase (including free purchases) an unlimited number of Kindle books.

These books are stored indefinitely in your digital library by Amazon and can be synchronized with any device registered to your account, including the Kindle app on your phone. This feature significantly broadens your reading options, allowing you to amass a diverse collection of ebooks accessible across all your devices.

Featured image credit: @felipepelaquim/Unsplash

Merchandising has always been a cornerstone of the retail industry. However, with the advent of technology, traditional business methods have undergone a significant transformation.

The retail landscape is evolving, and staying competitive requires a smart, efficient, and data-driven approach. Retail merchandising software is a technological solution designed to help retailers streamline their processes, enhance productivity, and drive sales growth.

What is merchandising software?

Retail merchandising software is a comprehensive tool that aids in accurate product tracking, optimal pricing, effective display planning, and seamless communication among staff across multiple locations. Often, these software solutions are inclusive of retail workforce management functionalities.

These tools are designed to alleviate common retail challenges by:

• Enabling easy scheduling and project tracking
• Replacing traditional spreadsheets with precise inventory details
• Improving compliance with planograms
• Integrating with other systems to accurately track past sales performance

In essence, retail merchandising software provides retailers with the means to deliver the best possible store experience for customers, from team management to inventory fulfillment.

How does retail merchandising software work?

Retail merchandising software guides the workflow from inception to completion, enhancing consumer experience and boosting revenue in the process. Here’s how a typical workflow, augmented by retail merchandising software, might look like:

• Strategy Support. The ideal retail merchandising software should align with the merchandising strategy. Look for tools that support in-store execution while providing comprehensive reporting and analysis. This way, you can measure outcomes and carry out merchandising work efficiently.
• Execution. Retail execution software simplifies communication and task delegation within teams, improving communication with leadership and among team members.
• Coordination. After assigning tasks to teams, third-party vendors receive workflow notifications, allowing for seamless integration of different stakeholders in executing the initial strategy.
• Real-time Tracking. Using retail merchandising software, teams can track completed tasks in real time. This allows retailers to monitor team progress and make necessary adjustments.
• Analysis. After implementing the strategy, retail merchandising software can collect and analyze performance data. Managers can easily conduct retail audits, track real-time sales, and analyze workflows. The software also identifies workforce inefficiencies, highlighting areas for improved labor scheduling and more effective execution.

Major benefits of retail merchandising software

The adoption of merchandise planning systems can bring a multitude of benefits to retailers. Here are some of the most significant advantages:

• Boosts Sales Performance. Retail merchandising software helps ensure flawless retail strategy execution across all sales channels, directly contributing to increased sales.
• Enhances Team Productivity. With real-time tracking and task management, retail merchandising software can significantly boost team productivity. It empowers retail managers to track teams in real-time across territories and place the right employee on the job.
• Streamlines Operations. Retail merchandising software can streamline nearly every retail process, from task management to execution verification measures. It rolls several tools into one easy-to-use platform, driving operational excellence across teams and locations.
• Facilitates Real-time Reporting. With retail merchandising software, managers can access real-time reporting that provides insight into mileage and completed tasks. This high visibility of each step ensures errors won’t fly under the radar.
• Improves Customer Experience. By providing store teams with precise estimates on order fulfillment and re-stocking, inventory management features within merchandising software can create a fuss-free in-store purchasing experience for customers.

Essential features of retail merchandising software

When considering merchandise planning software, it’s important to look for key features that can significantly enhance your retail operations.

• Scheduling and Workforce Optimization. Scheduling and workforce optimization features enable managers to staff each location and accurately adjust as needed in real time.
  Task Audits. Task audit features allow for task completion and inventory auditing via a robust cloud-based platform.
• Time and Mileage Tracking. Time and mileage tracking features provide more accurate assessments of time-on-task for employees and their stores.
• Enhanced Communications Tools. Effective communication is key in any retail operation. Look for software that offers dedicated support for communications to ensure no missed signals.
• Analytics Tools. Analytics tools can provide comprehensive, actionable reports, offering insights into how a location is doing historically or how to solve problems.

The future of retail merchandising software

As technology continues to evolve, so too will retail merchandising software. With the rise of artificial intelligence and machine learning, we can expect to see even more advanced features and capabilities in the future.

Artificial intelligence and machine learning are set to revolutionize retail merchandising software. These technologies can provide predictive analytics, automate routine tasks, and offer personalized recommendations, among other benefits.

As retail operations become more complex, integrating with other systems will become increasingly important. This will allow for a more seamless flow of information and a more comprehensive view of operations.

Data is becoming increasingly important in retail, and this trend is set to continue. Retail merchandising software will likely offer more advanced data analysis capabilities, allowing retailers to make more informed decisions.

Conclusion

Retail merchandising software can help retailers streamline operations, boost sales, and deliver superior customer experiences. Choosing a software solution that aligns with your needs can transform your retail operations and set your business up for success. Whether you’re a small retailer or a large enterprise, investing in retail merchandising software is a step in the right direction.

Featured image credit: Artem Beliaikin/Unsplash

One of the world’s most popular eCommerce platforms is Magento. This makes it a good choice for many different businesses. Finding the perfect Magento development company for your business can be challenging due to its popularity and the abundance of service providers. To help you with this decision, here are a few tips to consider:

The importance of choosing the right Magento development company

Selecting the top Magento development companies is of paramount importance for the success of your e-commerce venture. The company you choose will play a significant role in shaping your online store’s performance, user experience, and overall functionality. A competent Magento development partner can tailor the platform to suit your specific business needs, providing customizations, integrations, and features that align with your objectives. On the other hand, a subpar choice may result in delayed projects, cost overruns, and substandard outcomes, jeopardizing your online reputation and potential revenue. The right Magento Development Company brings expertise, experience, and technical proficiency to the table, ensuring a smooth development process and delivering a high-quality e-commerce store that resonates with your target audience. Thus, investing time and effort in selecting the right partner is an essential step toward achieving sustainable growth and long-term success in the competitive world of e-commerce.

Use GetTrusted to find the right company

If you’re in search of the Best Magento Development Company, look no further than GetTrusted. This innovative marketplace is designed to assist clients in finding reliable IT outsourcing companies and agencies. Unlike traditional platforms, GetTrusted takes a different approach by handpicking the TOP-3 companies in each niche, ensuring that you get access to the best performers in the industry. To maintain the highest quality standards, GetTrusted exclusively works with established companies and avoids individual freelancers. Both clients and vendors undergo a thorough verification process, guaranteeing that only trusted clients and IT companies are part of this platform.

Using GetTrusted is an effortless experience for clients seeking Magento development services. All you need to do is fill out a simple quote form, detailing your project requirements and goals. Once the information is submitted, GetTrusted’s team works diligently to connect you with the most suitable IT companies that precisely match your needs. By streamlining the process and facilitating direct communication, GetTrusted ensures a seamless experience for clients, making it an ideal choice for finding the right Magento Development Company.

Evaluating the experience and expertise of the company

The best Magento development company will have extensive experience in building eCommerce websites. They should be able to provide references from previous clients, and they should also be able to show you examples of their work so that you can see for yourself how well-qualified they are. A good place to start is by looking at their website, social media accounts, portfolio, and any awards or recognitions they may have received over time (this shows that others recognize their expertise). If possible speak with some former customers who used this company’s services; ask them about their experience working with them so you can get an insider’s perspective on what it’s like working with them on an ongoing basis as well as get advice about which software/tools are best suited for your particular needs.

Evaluate the experience and expertise

As you look to find a Magento development company, it’s important to evaluate the experience and expertise of the team. A good place to start is by looking at their past work on Magento projects.

• Does the company have a team that has worked on Magento projects before? By considering the level of comfort and familiarity a Magento development company has with the platform, you can gauge their expertise and understanding of its capabilities and limitations. This insight is valuable in determining their suitability for your project.
• Is this team familiar with your industry? If so, they may be able to offer more insight into how best to use features like Category Management or Product Bundling (or even something like Sales Orders).
• Are their developers experienced in building eCommerce websites specifically for businesses like yours? They should understand exactly what kinds of business goals are involved in order for them to build something that meets those needs without creating problems later down the road (like poorly designed product pages).

Review the portfolio and past projects

Once you have shortlisted a few companies, it’s time to review their portfolios. By reviewing the company’s past projects and assessing its performance in those endeavors, you can gather valuable insights into its capabilities and reliability. This information will give you a clear understanding of the company’s track record and help you make an informed decision about choosing the right Magento development partner. The best way to do this is by reading client reviews and checking out portfolios that showcase their work.

Additionally, you should also look at the details of each project such as budget and timeline so that you understand better how much money was spent on each project, what kind of deadlines were involved in delivering it within those timelines, and whether there were any issues during the development process which impacted delivery date or quality of codebase (if applicable).

Compare pricing and budget considerations

Choosing the right Magento development company is a complex process, and it’s important to do your research. Comparing pricing and budget considerations will help you find the right price for your budget, which in turn will help you find the right company at that price.

• Compare pricing: You can compare prices by asking each company for their rates or by looking at their websites–or both! If they don’t list their rates online, ask them to send over an estimate so that you know what kinds of services they offer and how much those services cost. This will give you an idea of how much each option costs before making any decisions about hiring a developer.
• Find out about financing options: Most Magento development companies offer payment plans or other financing options so that businesses can pay as they go instead of having all expenses upfront (which may not be possible). This can make it easier on both parties involved because small businesses don’t have large amounts lying around just waiting for projects like this one; however, some might prefer paying everything upfront because then there’s no risk involved later down line if something goes wrong with project milestones not being met correctly due solely because funds weren’t available when needed most urgently during those first few months after launch date had already passed

Check for industry certifications and partnerships

You should check for industry certifications and partnerships. It is important to find out if the company you are working with has any industry certifications, such as those offered by Magento. The value of such partnerships cannot be understated, as they offer an extra layer of trust and legitimacy when it comes to choosing a company that will build your website.

Some examples of these certifications include:

• Magento Certified Developer (MCD)
• Magento Certified Developer Plus (MCDP)

Conclusion

In conclusion, we hope that these tips will help you find the right Magento development company for your needs. When it comes to choosing the right Magento development company, conducting thorough research and evaluating all available options is crucial. While the process may appear overwhelming initially, with patience and effort, you can make a well-informed decision that will yield worthwhile results. Remember, taking the time to find the right company will ultimately lead to a successful and satisfactory partnership.

Featured image credit: Unsplash

E-commerce in Turkey can be divided into two eras: the time before Trendyol and the time after. This company is majority-owned (86 percent, to be exact) by the Chinese e-commerce giant Alibaba. Once Trendyol and Alibaba joined forces in 2018, they quickly became Turks’ go-to online shopping destination across almost all product types.

Before we dive into the captivating twists and turns of the business landscape, which could rival an episode of “House of Cards”, let’s examine some statistics that shed light on the current market situation. According to the HSBC Global Research report (2021), Turkey’s e-commerce market is a bit like a two-horse race, chiefly controlled by Trendyol and Hepsiburada.com. While Hepsiburada.com tops sales in electronics, Trendyol leads in several categories, including fashion, cosmetics, and personal care. It is worth noting that Amazon was the newest player in the market at the time of the report and is on track to become a serious contender in a short time.

Trendyol’s stranglehold on the market was so unyielding that it nudged eBay, the first major international entrant in Turkey’s e-commerce market with a local player’s (GittiGidiyor) acquisition, towards the exit. Adding to eBay’s troubles, Amazon launched an ambitious entry into the scene, bringing along its array of Prime arsenal. These factors – Trendyol’s dominance and Amazon’s aggressive entrance – ramped up the pressure on eBay. By 2022, eBay had decided to shut down GittiGidiyor, one of Turkey’s longest-standing e-commerce platforms, which had been in operation for nearly two decades. eBay, which had acquired the site in 2016, bowed out six years later, citing the fiercely competitive climate.

These developments solidified Trendyol’s status as the e-commerce leader in Turkey, reinforcing its already formidable position. However, there were murmurs of change and regulation back in the capital city, Ankara. Such a development could potentially pose a serious challenge for this dominant platform. And this is where things started to get complicated…

The breeze of change

Last July, Turkey introduced a new e-commerce bill that revolves around anti-trust measures, and both the opposition and ruling parties agreed on it. However, a few weeks later, the main opposition party, Republican People’s Party (CHP), unexpectedly filed a case with the Constitutional Court, aiming to invalidate certain parts of the previously supported law. At the same time, something interesting happened with journalists who had initially backed the law. Suddenly, they changed their minds and started writing against it in a unified way.

The situation became intriguing when Tuncay Mollaveisoğlu, the Editor-in-Chief of Cumhuriyet, a prominent newspaper in Turkey, declared that his article highlighting the media’s change of heart would not see the light of day in the same publication he was running. We will delve into this matter more later on, but media ombudsman Faruk Bildirici believes that this shift in the media’s perspective on the regulation can be attributed to the influence of Trendyol. While Bildirici’s allegations encompass numerous media outlets and journalists, the attention is primarily on the opposition newspaper Cumhuriyet. He claims the newspaper took 500.000 TL (around $25,000 at the time) to publish articles against the regulation as other news outlets and individual reporters did too for various sums.

Mollaveisoğlu claimed he knew of a purported bribery scandal before his tenure but was obstructed from investigating those involved. It’s worth emphasizing that his latest article concerning this matter was not published in his newspaper; he had to publish it via Twitter. As of yesterday, he has been dismissed from his position at the newspaper due to this.

Hiring the inspector who oversees you

Trendyol’s ascent has been nothing short of impressive, marked by a meteoric rise in trade volume. Yet, lurking beneath its shiny facade lie certain blemishes on its track record. Cast your gaze back to 2021, a time when the whispers of monopolization allegations grew louder. During this turbulent period, the company made a rather eyebrow-raising move. In a curious display of audacity, Trendyol enticed a prominent figure from none other than the Competition Authority itself—the institution tasked with keeping a watchful eye over the entire e-commerce sector.

This bold maneuver did not go unnoticed, triggering the scrutiny of the Competition Authority. Their piercing gaze probed deep into Trendyol’s practices, particularly their alleged meddling with its marketplace’s listing algorithm. Allegations had it that Trendyol had surreptitiously manipulated the algorithm in some product categories, surreally tilting the scales in favor of its own products in its marketplace—a sly maneuver that left its competitors/clients who are listing products on the platform teetering on the precipice of disadvantage.

In a rattling turn of events, it was revealed in March of the previous year that Trendyol resorted to advertising tactics that embellished reality. The regulatory body caught wind of this deceptive ploy, where Trendyol cunningly created advertisements that projected a misleading impression of generous discounts. The Turkish Competition Authority swiftly intervened, delivering a resounding blow by imposing a hefty fine of approximately 1.9M TL (around $128,000) upon Trendyol.

Now, fast-forward to the present, where the echoes of these allegations have reached the hallowed halls of justice. As the legal proceedings unfold, it is anticipated that the ripples of repercussion will reverberate not only through the media but also across the e-commerce sector, forever altering the industry’s landscape. Brace yourselves, for the repercussions are far from over.

Featured image created with Midjourney

Unfortunately, it took only five days after the end of 2022 for the “Amazon layoffs 2023” headline to surface. We already know that great tech expansion took a hammering with a layoff spree in 2022 and it seems like tech layoffs will continue in 2023. Amazon CEO Andy Jassy has stated that the corporation will reduce its workforce as the global economic outlook worsens.

“Today, I wanted to share the outcome of these further reviews, which is the difficult decision to eliminate additional roles. Between the reductions we made in November and the ones we’re sharing today, we plan to eliminate just over 18,000 roles.”

-Amazon CEO Andy Jassy

Amazon layoffs 2023: What is going on?

Amazon’s layoffs of 18,000 workers are the biggest in the history of the technology sector, which has been actively shrinking since last year. CEO Andy Jassy explained the layoffs in a blog post, blaming the unstable economy and the rapid growth of the company over the past few years. Wage workers in the warehouse will be spared from layoffs. The company’s focus will be on its administrative staff.

Affected employees will begin receiving notices from the corporation starting on January 18th.

According to Jassy, Amazon’s long-term prospects would improve as a result of the layoffs because of the company’s improved ability to control expenses. He acknowledged that the layoffs were a “tough decision” and said, “We don’t take these decisions lightly or underestimate how they could effect the lives of individuals who are impacted.”

In the early stages of the pandemic, Amazon’s sales skyrocketed as people flocked to the website to stock up on necessities. However, this year the business is seeing a decline in demand due to rising prices and a return to in-store purchases.

Jassy tried to put a positive spin on the large layoffs in a blog post announcing them by saying, “Amazon has weathered uncertain and difficult economies in the past, and we will continue to do so.” In extended trading, its shares gained 2% after the layoff news.

Inflation at a 40-year high and a global pandemic both contributed to a slowdown in sales last year and Amazon was just the latest Big Tech business to feel the effects.

As consumers revert to their behaviors from before the epidemic and the macroeconomy worsens, many of these previously unstoppable tech companies are now suffering from whiplash and laying off thousands of staff

Amazon will still be paying severance and is required by law to provide certain notices regarding major layoffs.

“We are working to support those who are affected and are providing packages that include a separation payment, transitional health insurance benefits, and external job placement support.”

-Amazon CEO Andy Jassy

On the same day that Amazon announced its layoffs, commercial software behemoth Salesforce revealed it would be cutting around 10% of its workforce or about 8,000 jobs.

 “The pandemic’s boom times made the company hire overzealously. And now that the there has been a pullback in corporate spending, the focus is on cutting costs.”

-Salesforce Co-CEO Mark Benioff

Current Amazon employee count in 2022

You may be wondering how many people will be left when Amazon lays off 18,000 people. Let us remind you that Amazon is one of the largest technology companies in the world.

With over 1.5 million employees (including warehouse workers), Amazon is the second-largest private employer in the United States after Walmart.

Biggest tech layoffs in 2022

Major layoffs have been reported by Facebook owner Meta, Twitter, Snap, and others in recent months, a surprising reversal for a sector that had witnessed huge growth for more than a decade.

These are just some of them:

• Meta layoffs 2022: 11,000 employees
• Snap layoffs 2022: 6,000 employees
• Getir layoffs 2022: 4,480 employees
• Twitter layoffs 2022: 3,700 employees
• Bytedance layoffs 2022: 3600 employees
• Salesforce layoffs 2022: 2,100 employees
• Stripe layoffs 2022:1,100 employees
• Coinbase layoffs 2022: 1,100 employees
• Microsoft layoffs 2022: 1,000 employees
• Netflix layoffs 2022: 450 employees
• Tesla layoffs 2022: 229 employees

It is important to take precautions, and layoffs are not the only option. It’s terrible news for the industry overall that some businesses have halted or stopped hiring. These are the tech companies that have slowed or frozen hiring in 2022:

• Microsoft
• Nvidia
• Lyft
• Uber
• Salesforce
• Meta
• Spotify
• Google
• Apple

The tech industry has lost almost 150,000 jobs since 2022. For detailed information about tech layoffs in 2022, go to our report.

Dataconomy Wrapped 2022: The answers to your burning questions

Will tech layoffs continue?

For Amazon, the epidemic was a major benefit to its economic line, with online sales surging as consumers avoided in-store shopping and the need for cloud storage expanded with more businesses and governments transferring activities online. And it has resulted in Amazon adding hundreds of thousands of jobs over the course of the last few years. Now, the effects of the pandemic are waning and the emerging economic crises are forcing even the largest tech companies, including Amazon, to downsize.

Over the past ten years, tech companies have experienced rapid expansion and exorbitant spending. However, given the impending global recession, which might be significantly longer and harsher than many think, Silicon Valley companies that announced large layoffs this week could be a leading indicator for the economy.

Tech companies may need to slow down their recent expansion and spending boom in favor of cost-cutting measures when possible due to the changing economic climate.

AI in retail industry is rapidly transforming the customer journey and experience.

The fundamental steps of shopping have mostly stayed the same throughout the years: enter a store, choose the ideal item, and pay for it. With personalization, automation, and more efficiency, artificial intelligence has the ability to revamp the conventional shopping experience totally.

AI in retail industry: The transforming force

The retail sector has been going through a digital shift for a while now. Every branch of retail companies has seen a rise in speed, efficiency, and accuracy, largely because of sophisticated data and predictive analytics technologies that support businesses in making data-driven business choices.

Without the internet of things (IoT) and, most significantly, artificial intelligence, none of those insights would be feasible. Businesses now have access to high-level data and information that can be used to improve retail operations and create new business prospects thanks to AI in retail. In fact, it’s predicted that over the course of three years, AI in retail will increase sales by $40 billion.

The importance of AI in retail

Physical stores are still the king of retail, but they must operate in very competitive markets. Similar to physical stores, digital stores compete in a market where they can easily access their rivals. Retailers can use AI to transform the shopping experience for customers and gain the competitive edge they need to remain relevant.

By using AI-driven demand forecasting to optimize inventory, retailers can maintain agility while enabling personalization.

The market size of AI in retail

AI In Retail Market Size, Share & Trends Analysis Report of Grand View Research forecasts that:

“The global AI in retail market size was valued at USD 5.79 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) exceeding 23.9% from 2022 to 2030. The growth is fueled by factors such as the constantly rising number of internet users, smart devices, the need for surveillance and monitoring at a physical store, and government policies toward digitization. AI in the retail industry revolves around how corporations have operated over the past few decades. Big data analytics and AI are crucial to digital business; they have the capabilities to transform everything from customer experience to business operations.”

The future of AI in retail

Retailers who want to maintain their competitiveness need to look no further than AI. By 2023, 83% of businesses are expected to be utilizing AI, and those that don’t run the risk of losing irreparable market share to rivals.

Navigate through the rough seas of retail with business intelligence as your compass

Many sectors use the term “artificial intelligence,” but few people understand what it implies. When we refer to artificial intelligence (AI), we are referring to a number of technologies, such as machine learning and predictive analytics, that can gather, process, and analyze vast amounts of data and use that data to predict, forecast, inform, and assist retailers in making precise, data-driven business decisions.

By utilizing cutting-edge AI analytical capabilities to transform unprocessed data gathered from the IoT and other sources into useful insights, these technologies are even capable of acting autonomously. Additionally, AI in retail makes use of behavioral analytics and consumer intelligence to improve a variety of customer care touchpoints and gain insightful knowledge about various market demographics.

Benefits of AI in retail

Although the benefits of AI in retail industry are unpredictable today, we can list the areas where it is most effective:

Loss prevention

AI has grown to be a crucial part of loss prevention, especially in the context of self-checkout technology. For instance, AI is included in the scanning and video systems at self-checkout stations to learn more about how theft occurs. The AI is intended to detect any unusual transactions and even notify your personnel if any shoplifting occurs in real-time.

But keep in mind that a reliable shop security system also safeguards its property without jeopardizing client privacy. Authentication only logs the data of authorized personnel who have chosen into the system, as opposed to recording information on alleged shoplifters without customers’ permission. By doing this, you can protect your customers’ privacy while also maximizing the security of your brick-and-mortar store and reducing your financial losses.

Automation

Automating tasks that were previously carried out by human employees is one of the biggest ways AI is changing the retail sector. This enables your staff to focus less on tedious tasks and more on resolving challenging customer issues. And doing this right away can enhance the customer experience, which will boost sales and profitability.

Payment automation eliminates boring paperwork

Market demand analysis

You can more easily predict the future market and customer demands with AI, which will help you better serve your customers’ needs. For instance, using AI, you can forecast customer behavior by looking at past consumer behavior patterns. Additionally, you can learn which areas of your physical store attract customers’ attention the longest.

AI can also give you data on the number of website visitors and user demographics if you operate an online business rather than a physically accessible store. After that, you can use this information to inform your marketing choices. For instance, the information you gather can assist you in customizing your upcoming marketing campaign to best suit the wants and needs of your target audience.

Supply chain operation

You may also use AI to improve the supply chain for your store. For instance, this technology can analyze your previous customers’ purchasing trends and notify you immediately when you are about to run out of a particular product. This is important because if your customers don’t feel confident that you’ll always have the products they require, they might patronize a different business. And that results in lost sales, which hurts your bottom line.

AI can also forecast when a particular product will be in higher demand throughout the year. As a result, you can start stocking up now rather than rushing to secure and display these popular seasonal items when customers start asking about them.

Improving CX

Finally, AI is revolutionizing the retail sector by making it simpler for you to maintain a positive client experience. For instance, chatbots that use AI can guide customers through stores and even make highly individualized product recommendations.

Customers don’t even need to look through your entire physical store or website to find what they’re looking for with a chatbot. Additionally, your chatbot can suggest products that pair well with one another, such as particular outfits and shoes or particular cheeses and wines.

If you sell apparel, you can even utilize AI to let customers input pictures of the ensembles they want, and the system will then propose matching items from your site. As a result, clients will continue to come back to you season after season for their apparel needs. This will immediately improve the customer experience.

AI in retail examples

Below we detailed four great examples of AI in retail projects:

Walgreens’ project of AI in retail industry: Flu spread tracking

If the flu is not appropriately treated, it can be uncomfortable, inconvenient, and even fatal. People can take action to maintain the health of their families if they have the appropriate information. Walgreens tracks the spread of the flu using information from the number of antiviral medications it fills at more than 8,000 stores. Customers can use the online interactive map to find out how widespread the flu is in their area, and Walgreens can use it to stock up on more flu-related products in infected areas. AI is empowering both the store and its customers.

Taco Bell’s initiative of AI in retail industry: Enabling customers to place taco orders on the go

There is no time to waste if you want tacos. The first restaurant to provide online food ordering with AI was Taco Bell. Customers can text or speak their order with ease thanks to Tacobot’s integration with Slack. Even customized and large group orders are possible with the bot. After every order, the bot responds with witty comments in classic Taco Bell fashion.

Walmart’s step of AI in retail industry: Developing robots to scan shelves

One of the biggest retailers in the world, Walmart, intends to employ robots to help monitor those lengthy aisles. In dozens of its stores, Walmart is testing robots that can scan shelves. The robots search the shelves for missing items, which need to be restocked or have their price tags adjusted. Human employees can now spend more time interacting with customers and making sure there aren’t any empty shelves thanks to the robots.

Enabling customer data compliance with identity-based retention

Olay’s project of AI in retail industry: Personalized skincare experience

Olay consumers can receive tailored skincare care with the aid of AI without needing to visit a dermatologist. Customers can use Olay’s Skin Advisor to take a selfie of their unadorned face and have the app utilize artificial intelligence to determine their true skin age. The app assesses skin health and offers personalized skin care regimen recommendations for problem areas.

North Face’s take on AI in retail industry: Finding the optimal coat for customers

Not sure which coat to purchase? North Face can support you there. The business uses IBM Watson’s cognitive computing technology to enquire about the wearer’s plans for wearing the coat and their activities. With the aid of that data, North Face is able to provide customers with tailored advice on the best coats for their activities.

Conclusion

AI is reshaping the retail industry for the better. Implementing AI improves your store’s ability to make wiser decisions, increase sales, and eventually improve customer retention. Therefore, there has never been a better time to start integrating AI into your regular tasks.

As you consider how to incorporate AI into your retail establishment, keep in mind the numerous advantages of the technology that were already mentioned. With the right AI tools, you can be well on your way to achieving a completely new level of business growth in the months and years ahead.

This interview, called “How tech will revolutionize retail,” published by McKinsey in 2021, still sheds light on the future of AI in retail.

Made.com administration process explained. After the company’s shares were suspended on Tuesday, online furniture retailer Made.com took a step toward going into administration. It has ceased taking new orders, and management has warned that cash reserves may exhaust if further money cannot be raised.

Made.com is expected to be the first significant retailer to go out of business due to the stress the cost of living crisis puts on household budgets. What has happened to Made.com? Is Made.com going bankrupt? How is Made.com’s share price affected? How to get a refund from Made.com? The answers and the story of the fallen pandemic furniture star Made.com are explained in this article.

The Made.com administration process starts

After failing to find a buyer for the business, online furniture retailer Made.com has disclosed plans to appoint administrators, potentially threatening up to 700 jobs. The company declared that its operating arm Made.com Design (MDL), had submitted a notice to employ PricewaterhouseCoopers (PwC). Although they acknowledged that there were no certainties, the group stated that PwC would continue to try to achieve a sale of the company.

The company revealed last week that attempts to find a buyer for the company during rescue negotiations had so far failed. It has suspended taking new orders, and management has issued a warning that cash reserves may exhaust if further money cannot be raised.

“In light of MDL’s requirement for further funding, and in order to preserve value for its creditors, the board of MDL took the decision on 26 October 2022 to temporarily suspend new customer orders. Made has now been notified that the board of MDL has resolved to file notice of its intention to appoint administrators, with a view to appointing Zelf Hussain, Peter David Dickens and Rachael Maria Wilkinson of PricewaterhouseCoopers LLP as administrators of MDL.”

Made.com

Made.com made a statement announcing the halt to share trading. It also stated that administrators would be appointed, indicating that the company is not yet in administration but is moving in that direction.

The decision allows the corporation 10 days to find new investors or sell all or a portion of the business.

What happened to Made.com?

Brent Hoberman, a co-founder of Lastminute.com, and Chinese businesswoman Ning Li founded Made.com, which went public in London last year with a valuation of £775 million. Made.com has offices in China, Vietnam, London, Paris, Berlin, and Amsterdam. How is the multi-national about the collapse, and how did the Made.com administration process start?

The home furnishing market is facing a crisis as cash-strapped Brits reduce their expenditure on expensive products due to the high cost of living. It happened just after Camden-based Eve Sleep, a manufacturer of mattresses, entered administration following a “heartbreaking” decision to end the look for a rescue takeover.

The company’s sales increased during the Covid outbreak as consumers increased their purchases of furniture and other home items because they were forced to stay at home and could only shop online.

Sales increased 30% year over year to $315 million in 2020 and 63% to £110 million in the first three months of 2021.

Due to the company’s expansion, it was listed with a valuation of £775 million on the London Stock Exchange in June of last year.

The store issued a warning in May that problems with the supply chain may cost them £5 million in revenue this year and up to £35 million in 2022. The business had forecast a profit as recently as March.

In September, Made announced it was for sale, but the company acknowledged that it could not find a buyer last week as it suspended consumer purchases and halted returns and refunds.

The company was about to put in administrators from the accounting firm Pricewaterhouse Coopers when the trade of its shares on the London Stock Exchange was suspended. Since the year’s beginning, company shares have fallen by 99%. While adding that there is no guarantee a transaction can be achieved, Made stated that Pricewaterhouse Coopers would continue trying to obtain a sale of the company.

The firm employs about 700 people, but it is already laying off about a third of them as it scrambles to cut costs in the wake of economic difficulties. Since then, Hoberman and Li have departed the company.

How Made.com share price affected?

At the time of writing, Made.com share price is around 0.52 GBX. But let’s take a closer look at what happened. Made.com administration rumors highly affected the shares.

Made being listed at a value of £775 million on the London Stock Exchange in June of last year.

In 2020, the company’s sales increased by 30% year over year to £315 million. They then increased by 63% in the first quarter of 2021, reaching £110 million.

The situation is different nowadays. As the company prepared to engage Pricewaterhouse Coopers administrators, trading in its shares on the London Stock Exchange was suspended today. Since the beginning of the year, company shares have fallen by 99%.

Is Made.com going bust?

After attempts to find a buyer fell through, the online furniture store Made.com stopped accepting new orders, putting the company at risk of collapse. If the situation continues like that, Made.com will collapse.

Made.com investor relations

You can contact Made.com investor relations by mail.

• investor.relations@made.com

For more information, go to Made.com.

What if I’ve ordered from Made.com and my item hasn’t arrived?

According to Citizen Advice, you do not necessarily have the right to a refund if you purchased an item from a store before it closed.

However, there are several ways to get your money back if you ordered something and it never showed up.

Customers should initially attempt to contact the business or its designated administrators and request the purchased item or a complete cash refund.

How to get a refund from Made.com?

There are still ways to get your money back if you can’t reach the business or they don’t reply to your request for a refund.

If you paid by credit card

Section 75 of the Consumer Credit Act would apply if you used a credit card to purchase the item.

This implies that your card company is equally liable for refunding you if you use your credit card to pay for a significant transaction and something goes wrong, such as the products not arriving or the store closing.

To file a claim, get in touch with your credit card company—its customer service phone number should be your initial port of call—and let them know you intend to do so in accordance with Section 75.

It should then email you a claim form, which you can complete and use to submit your application to your provider.

Your card company can ask you for proof, such as a receipt or a report attesting to the item’s defect from you.

If you paid by debit card

Do not become alarmed if you did not use a credit card to purchase the item. Chargeback policies would apply if you made the purchase using a debit card.

You can use a chargeback to get your money back for products and services you didn’t get that you paid for with a debit card or a credit card for purchases under £100.

Claims must be filed within 120 days of the purchase, and you must contact your card issuer to begin a chargeback.

If you paid using buy now, pay later

If you used a buy now, pay later company to purchase an item, you should first get in touch with them to see if there is a procedure you can follow to get your money back.

Customers who shop in this industry are not afforded the same safeguards as those who use credit or debit cards because it is mainly unregulated.

What is Made.com?

Furniture and home decor are designed and sold online by the London-based British e-commerce company MADE.COM. Ning Li, a seasoned businessperson, and Brent Hoberman launched the company in 2010, along with Julien Callède and Chloe Macintosh.

It operates in seven European markets, including the UK, Ireland, France, Belgium, Germany, Austria, the Netherlands, Switzerland, and Spain, and has offices and warehouses spread throughout Europe and Asia.

Data breaches and hacks are today’s biggest problems. Check out the latest data breaches and hacks before we continue: CHI Health data breach, Facebook data breach, Uber security data breach, American Airlines data breach, Medibank cyber attack, and Binance hack.

Take a closer look at how data breaches effects companies: Equifax data breach settlement & T-Mobile data breach settlement

Are you looking for the best business analytics and business intelligence solutions in retail? Well, it is not surprising. According to Fortune Business Insights, the retail business intelligence market is anticipated to grow at a CAGR of 17.7% from 2018 to 2028, reaching USD 18.33 billion.

The retail sector has historically been slower than other sectors to adopt new technologies, and this trend continues with the adoption of BI technology. BI software for financial reporting and consolidation, customer intelligence, regulatory compliance, and risk management has advanced significantly in several areas, such as financial services. Retailers are, however, catching up swiftly and starting to understand the various BI applications for their particular industries.

What is retail business intelligence?

A retail business intelligence (BI) system is used to gather, process, and analyze data about the retail industry to deliver pertinent insights to:

• Improve client retention and customer satisfaction.
• Plan and optimize your assortment.
• Marketing campaign planning.
• Find new prospects for sales, etc.

Considering retail firms manage a tremendous amount of data, from supplier data to customer buying behavior, employee information to inventory details, every interaction and data point offers a possibility to make your retail business more successful and lucrative.

Retailers can employ a variety of various solutions to enhance their BI. Business analytics and business intelligence solutions in retail consist of:

• Data mining
• Text analytics
• Predictive analytics
• Prescriptive analytics
• Dashboards
• Visualizations

Business analytics and business intelligence solutions in retail need crucial integrations. You require necessary integrations to comprehend how clients act, how different sales channels perform, and whether marketing efforts are successful:

• Customer relationship management (CRM) software
• Ecommerce platform
• Point of sale system
• Customer service software
• Marketing campaign management software
• Inventory management software
• Supplier management software

After integrating these systems into your ecosystem, the benefits of business analytics and business intelligence solutions in retail will appear.

Check out the effect of machine learning in retail

Benefits of business analytics and business intelligence solutions in retail

What are the benefits of Business analytics and business intelligence solutions in retail? These are some of the best benefits of intelligent systems in retail:

• Find emerging trends
• Improve business operations
• Identify customer locations
• Enhance efficiency in the supply chain
• A better understanding of consumer demands
• Improve merchandising
• Improve inventory management
• Optimize store floor plans
• Competitor tracking in social media
• Informed decision making
• More effective marketing

Let’s take a closer look at the advantages of business analytics and business intelligence solutions in retail. How analytics is helpful for a retail business?

Finding emerging trends

BI will become even more crucial in recognizing new and developing customer patterns, enabling organizations to adjust without facing significant challenges.

Retailers can use business intelligence to find industry-specific trends that need to be considered.

Improve business operations

Retail companies can better manage their operations by using BI solutions. It aids in their monitoring of company activities. This enables quick repairs to be made in the event of mistakes.

For example, a retail business can utilize the BI tool to handle late deliveries and determine their cause. The company’s operations might be greatly improved with this knowledge.

Identify customer locations

Businesses may see where customers are physically located (states, cities, etc.) and how they found their websites and products, such as through recommendations or email marketing.

Enhance efficiency in the supply chain

Supply chains have become more complex as retailers take on additional merchants and start selling more of their own brand products.

Retail business intelligence can help you make sense of the data collected from your daily operations.

This enables retailers to develop more accurate forecast models and to pinpoint the main logistical bottlenecks that the supply team has to address to satisfy organizational KPIs.

A better understanding of consumer demands

Retailers must be able to adapt to the constantly shifting preferences of today’s customers to compete in the market, from desiring more socially and morally responsible products to needing bulk purchases completed quickly.

Businesses can acquire meaningful insights to map changes in client demand and modify their strategy with an efficient business intelligence solution.

Improve merchandising

Retailers can determine which products are selling well and which are not using BI. Making judgments regarding what to stock in stores and how to price things will be easier with the help of this information.

Improve inventory management

Retailers can use BI to determine whether an item is in limited supply so they can replenish it before customers start shopping elsewhere. It can also be used to keep an eye on when things are about to expire, so they aren’t thrown out too soon.

Check out how machine learning can drive retail success

Optimize store floor plans

A floor plan that may entice customers to shop for longer is one of the key reasons retailers use BI. Businesses should select a floor layout that makes shopping easy for customers.

Retailers can verify whether the chosen floor layout is adequate for the floor size and product types with BI software. They can use BI solutions to analyze various data sets (such as the number of stops made, the length of visits, etc.) and suggest a floor plan that will make it simple for customers to identify the products they want.

Competitor tracking in social media

Businesses can track KPIs, look at item scores, and monitor social media sentiment using retail business intelligence. Merchandisers may also utilize this information to track sales and the performance of certain products, businesses, and brands.

Informed decision making

Businesses can use BI to combine many data sources for a complete picture of what is happening throughout their organization. Enhanced BI solutions also enable this process across intricate franchise networks. This allows better-informed decision-making processes and contributes to developing a consolidated, holistic vision.

More effective marketing

Retailers can determine which marketing strategies are effective and which are not using BI. This information can boost sales, generate revenue, create new campaigns, and determine where to spend money on marketing.

What are business intelligence challenges?

Business analytics and business intelligence solutions examples

You understand how business intelligence helps retail companies collect and evaluate business data from across the organization so they may make wise decisions. So, what are examples of analytics used in retail sales?

Let’s look at some real-life business analytics and business intelligence solutions examples to show how this business solution is the retail industry. These are some of the biggest companies that use retail business intelligence:

• Amazon
• Starbucks
• Walmart

Let’s explore how the retail industry uses business intelligence.

Amazon

The business employs business analytics to promote items, make logistical business decisions, and personalize product suggestions.

The main factor behind the efficient operation of Amazon’s extensive supply chain is in-depth data analysis.

Starbucks

Starbucks forecasts what products and promotions a customer is likely interested in using a retail business intelligence software. The business lets clients know about the deals it thinks they’ll want to take advantage of.

Starbucks can enhance sales volume and bring in current consumers more regularly thanks to this approach.

Walmart

The retail behemoth uses BI technologies to understand how internet behavior affects in-store and online activities.

Using BI techniques, Walmart can comprehend the buying habits of its clients. For instance, they can learn how many customers used the Walmart app or website to search for a specific product before purchasing it on the same day. They can identify the busy times of the week and the user exit points.

Check out why business intelligence is a must in modern business

Best business analytics and business intelligence solutions in retail

The followings are some of the best business analytics and business intelligence solutions in retail:

• Alteryx

Alteryx

The self-service data analytics software provider Alteryx focuses on data blending and preparation. Users may clean, organize, and analyze data with Alteryx Analytics in a repeatable procedure.

Business analysts find this tool especially helpful when connecting to and purifying data from data warehouses, cloud apps, spreadsheets, and other sources. The platform provides capabilities for various analytical tasks (predictive, statistical, and spatial) to be performed inside a single user interface.

Amazon Web Services

Amazon QuickSight is a cloud-based business intelligence tool with embedded machine learning that is serverless and embeddable. It is some of the most known business analytics and business intelligence solutions in retail.

The tool enables the creation and publication of interactive BI dashboards that support natural language querying. It doesn’t require any infrastructure and can scale automatically to thousands of users. The pay-per-session pricing model promoted by QuickSight allows customers to only pay when users access dashboards or reports. You can use any device to access a dashboard.

IBM

Under two separate product lines, IBM provides a broad range of BI and analytic capabilities.

Users of the Cognos Analytics platform can access data to build dashboards and reports because it is an integrated self-service solution.

Incorporating automatic pattern recognition, natural language inquiry and generation support, and advanced analytics capabilities, IBM Watson Analytics provides a machine learning-enabled user experience. Both on-premises and as a hosted solution through the IBM Cloud, IBM’s BI software is available for deployment.

Microsoft

Microsoft is a significant player in business intelligence and analytics. Power BI, the company’s main product, is a cloud-based service provided by Azure Cloud.

On-prem capabilities are also available for individual users or when power users are creating intricate data mashups using internal data sources. Because users can create dashboards, prepare data for analysis, and uncover data using the same design tool, Power BI is exceptional.

The platform’s active user community helps expand the tool’s functionality and interacts with Excel and Office 365.

MicroStrategy

MicroStrategy combines self-service data preparation and visual data discovery in an enterprise BI and analytics platform. To connect to any enterprise resource, including databases, mobile device management (MDM) systems, enterprise directories, cloud apps, and physical access control systems, MicroStrategy offers native drivers and out-of-the-box gateways.

Its embedded analytics tool allows MicroStrategy to integrate various other websites and software programs, including chatbots, CRM tools, portals, and voice assistants like Alexa.

Oracle

A wide variety of BI and analytics solutions are available from Oracle, and they can be used either on-premises or in the Oracle Cloud.

The company’s Business Intelligence 12c solution includes conventional BI capabilities. With Oracle Data Visualization’s more sophisticated features, users can automatically visualize data as drag-and-drop properties, charts, and graphs. The program also enables users to save pictures of analytical moments in real-time through story points.

Salesforce

Depending on the position, sector, and features offered, the Salesforce Einstein Analytics platform has various variants. It is some of the most used business analytics and business intelligence solutions in retail.

Users can respond to inquiries using the product’s automatic data-finding capabilities that leverage clear and intelligible AI algorithms. Users can also modify analytics to fit their use case and strengthen findings with exact advice and detailed direction. With third-party apps, configurable dashboards, and customizable themes, Einstein also enables the creation of sophisticated experiences.

SAP

The enterprise and business-user-driven editions of SAP’s BI and analytics solutions are extremely comprehensive.

BusinessObjects Cloud and BusinessObjects Enterprise are cloud-deployed versions of the company’s flagship BI portfolio built on top of the SAP HANA Cloud. Additionally, SAP provides a range of conventional BI features for reporting and dashboards. The BusinessObjects solution houses the vendor’s data discovery capabilities, while the SAP Lumira tool set offers extra capability, such as self-service visualization.

Sisense

Organizations can easily extract business insight from complicated data of any size or format, thanks to Sisense. Without scripting, coding, or help from IT, consumers may aggregate data and discover insights in a single interface thanks to the solution. It is some of the best business analytics and business intelligence solutions in retail.

Additionally, it has extensive analytical capabilities, including a dashboard and visualization front-end. Organizations that want to evaluate significant amounts of data from many sources should use Sisense.

Tableau Software

Tableau is regarded as the key participant in the market and provides a comprehensive visual BI and analytics platform. The three primary distribution channels for the company’s analytic software portfolio are Tableau Desktop, Tableau Server, and Tableau Online.

Tableau is accessible on-premises or in the cloud and links to hundreds of data sources. Users may see and share data using Tableau Public, and the provider also provides embedded analytics tools.

Tellius

Tellius is a platform for AI-driven decision intelligence that enables quick data insights. The business uses automation and augmentation to speed up customers’ time to insight.

Users of the Tellius Platform can query their company data, examine trillions of records, and derive automated insights by combining AI and machine learning with a search interface for ad hoc exploration. Live Insights, which provides AI-guided insights from cloud data warehouses without relocating data, was just introduced by the firm.

Is artificial intelligence better than human intelligence? Check out the cons of artificial intelligence

Retail BI implementation cost

The price of a retail BI installation project, which includes creating an OLAP cube, self-service reports, dashboards, and a central data warehouse with data marts for storing retail data, may be as follows (Software license fees are not included):

• $80,000-$200,000 – for retail companies with 200-500 employees
• $200,000 – $400,000 – for retail companies with 500-1,000 employees
• $400,000 – $1,000,000 – for retail companies with 1,000+ employees

What is retail analytics software?

Retail software is computer software that is usually downloaded through the Internet and installed on PCs after 2005. (also known as cloud-based).

In the past, this software was distributed using tangible data storage media that was sold to end users, but today, very few businesses still distribute their software via tangible media. Usually, restricted licenses (like EULAs) or the Software-as-a-Service (SaaS) business model are used when selling software.

What is an example of retail software?

The goods sold on IBM PCs and its knockoffs in the 1980s and 1990s, including well-known programs like Lotus 123, Word Perfect, and the different components of Microsoft Office, are the most well-known examples of retail software.

 How do build the best business intelligence strategies?

What are the types of retail data analytics?

There are four different kinds of retail data analytics, which is crucial in giving modern merchants critical knowledge on running their companies.

The retail data analytics types are as follows:

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
• Prescriptive analytics

Conclusion

To ensure that retailers can take advantage of business intelligence and business analytics, choosing and applying the appropriate BI tool is crucial.

Businesses and franchises may turn data-driven insights into successful outcomes by combining data with a centralized view, establishing and tracking KPIs, and using easy-to-customize dashboards.

Do you know business intelligence analyst, data architect, cloud computing, and data engineer jobs are hot and on the rise?

• As per Lloyds Bank data, in the early stages of the epidemic, 65% of face-to-face payments were done using contactless payment via debit cards in June 2019, but by June 2022, this had increased to 87%.
• According to figures released in March by the banking sector trade association UK Finance, about £166 billion was spent in the UK using contactless technology in the previous year, compared to £80.5 billion in 2019.
• According to Lloyds, 95% of restaurant bills in the UK are paid via contactless technology, which includes mobile wallets, and 83% of purchases at supermarkets are contactless.

According to data from Lloyds Bank, Covid-19 significantly changed how face-to-face payments are made, with nearly 90% of transactions now being contactless.

Contactless payment satisfied the need to socially distance

In June 2019, when the pandemic was just starting, 65% of face-to-face transactions were made using contactless debit cards, according to data from a UK bank; by June 2022, however, this number had risen to 87%.

In June 2020, though, contactless debit cards were used for 72% of face-to-face transactions, and in June 2021, that number increased to 83%, according to the bank.

Contactless cards were initially made accessible in the United Kingdom in 2007. There was a £10 spending restriction at the time. This cap was raised to £30 by 2020 but has experienced major increases during the epidemic. It was raised to £45 in April last year and is now £100.

Gabby Collins, payments director at Lloyds Bank said, “The convenience of a contactless payment is clear when you look at the growth in this type of payment over time, with 87% of face-to-face debit card transactions now made using the technology.” 

Customers can set their spending restriction up to £100 using Lloyds’ mobile app. Since its introduction in 2021, this service has been utilized by about 800,000 bank customers.

The Covid-19 pandemic accelerated the use of contactless technology. When the epidemic hit, individuals were advised to restrict physical contact, including currency usage. Because, unlike mobile phone payment applications, most individuals already used payment cards, contactless payment technology was a suitable alternative for cash. This prompted groups such as the elderly, who are notoriously sluggish to adopt new technology, to embrace it.

As per figures released in March by the banking sector trade association UK Finance, about £166 billion was spent in the UK using contactless technology in the previous year, compared to £80.5 billion in 2019.

Based on the latest UK payment markets 2022 research, the pandemic had a revolutionary influence on the payments industry, accelerating the continuous drop in the use of cash payments while also slumping the use of debit cards following years of growing usage.

Three Trends in E-commerce Payments to be Concerned About

“It also led to changes in the payment types used. People made greater use of contactless payments, online banking, and mobile wallet channels, largely at the expense of cash payments,” said the report summary document.

According to Lloyds, 95% of restaurant bills in the UK are paid via contactless technology, which includes mobile wallets, and 83% of purchases at supermarkets are contactless.

When looking for the most relevant products and the best prices, shoppers don’t – surprisingly – have many options available to them. Depending on which research you believe, around 38-44% of product searches begin on Amazon, and another 40% or so start with a search engine. Mostly Google, of course.

 Along with other marketplaces, social media, and retailer websites, it can be difficult to work out who has the best deal for that cherished consumer good you crave.

Enter Vetted (formerly Lustre), which announced last week the launch of its AI-powered product search engine, alongside a $14 million Series A investment round led by global software investor Insight Partners. The round included participation from existing investors Index Ventures, Bling Capital, and Golden Ventures, as well as angels including Shiva Rajaraman, the former VP of Commerce at Meta.

Vetted claims to enable shoppers to effortlessly discover the brands and products most recommended for their needs, and it does so based on reviews from platforms such as Reddit, YouTube, and other reputable expert sites. 

“We spend over $100 billion shopping online, yet getting the best value has become an overwhelming and frankly anti-consumer experience,” Stuart Kearney, co-founder at Vetted, said. “Shoppers shouldn’t have to spend hours sifting through indistinguishable products littered across thousands of ad-infested sites loaded with fake reviews and unreliable information. That’s why we’re building Vetted. Our users get a smart guide aligned with their best interests, transforming e-commerce into the simple and trustworthy experience everyone wants – especially today, when every dollar counts.”

Vetted uses machine learning that the startup says replicates a shopper’s research process. It automatically vets trustworthy product data and analyzes everything from price history, ideal use cases, and reviewer consensus to rank the best products according to their relevance.

Vetted’s search results also show users why a given product was selected, making it easy for consumers to purchase with more knowledge and confidence. Vetted has made its solution available as a browser extension, enabling shoppers to use its research engine in their laptop browser.

“Over 330,000 shoppers already trust Vetted, with users buying our recommendation in a given category 70 percent of the time,” Hanna Jung, VP of marketing at Vetted, said. “They’re also asking for help beyond our initial focus on consumer electronics. With this additional funding, we’re excited to dramatically expand our product and retailer coverage to further empower shoppers across all their shopping needs.”

“We are thrilled to support Stuart and the Vetted team on their mission to provide a shopping experience where consumers find the products best suited to their needs with minimal effort and maximum confidence,” said Thilo Semmelbauer, Managing Director at Insight Partners. “The opportunities for disruption in e-commerce are endless, and Vetted is well positioned to be a dominant player.”

As Lustre, Vetted won a 2021 Webby award for the best shopping app and is available through its website and browser extension. How does it make money? As reported by TechCrunch, affiliate links to storefronts are one way, but other revenue streams are still being clarified. 

Kearney is clear, however, that it won’t monetize user data. “The only data we do collect are completely anonymized, aggregated, and related to general search performance to ensure we’re being helpful to our users,” he told TechCrunch.

This article was originally published in Grit Daily and is reproduced with permission.

The Artificial Intelligence Call Center era has begun. Call centers have long used cutting-edge technology, from call routing systems that sent consumers to the first available agents to interactive voice response (IVR) systems that millions of customers interact with today.

With artificial intelligence (AI) gaining traction in the early 2000s, call centers gained numerous new capabilities to improve the customer experience. AI’s capacity to lower operational expenses, personalize the customer experience, provide actionable insights, and enhance agent efficiency are just a few of the ways AI is transforming how customer service organizations operate nowadays.

According to Gartner, AI-powered chatbots will handle 20 percent of customer service calls in 2022. This frees live agents of a lot of stress and time. AI technology is also being used to assist contact center staff. Agents may know who’s on the line, what they require, and how to provide it before they start talking with the customer on the phone.

With the wide availability of cloud services and machine-learning tools, call centers have been given more powerful new AI capabilities to improve customer service in all forms. According to a study, executives cite client experience as their top reason for investing in AI, while cost reduction has dropped to second place.

Despite the hype, few businesses have utilized AI’s abilities in contact center operations. However, adoption rates are anticipated to rise quickly in the coming years due to the COVID-19 epidemic forcing many call centers to convert suddenly to a remote-work model. Many will not return to pre-pandemic conditions. According to Markets and Markets data, the market for call-center AI technology is anticipated to expand from $800 million in 2019 to more than $2.8 billion by 2024.

How does AI improve customer experience?

Call centers are one of the most demanding environments for artificial intelligence because they need to handle a high volume of requests while adhering to strict SLAs. Artificial intelligence call center technologies’ impact on customer service can help customer support agents work more productive, have engaging and personally gratifying conversations, and reduce time spent on simple interactions. AI can increase customer engagement, promote brand loyalty, and boost retention. Although it isn’t a substitute for humans, AI improves efficiency and takes over routine assignments such as responding to frequently asked questions – off the plates of your customer service teams.

AI can also combine other technologies such as machine learning, deep learning, and natural language comprehension to break down communication barriers and automate customer interactions. Conversational chatbots and product recommendations based on customer behavior data are two well-known applications of AI. But the application possibilities are endless.

Here’s a rundown of how AI is revolutionizing the call center—and redefining customer experience:

Predictive Routing

Skills-based routing took off at the call centers in the 1990s, a software that linked a basic customer profile with an agent possessing the appropriate talents, such as product knowledge or sort of help required. Nowadays, AI is extending on the same concept with predictive behavioral routing. Predictive behavioral routing uses artificial intelligence call center techniques and analytics to match callers to customer personality models, which are then used to route calls to agents who can best serve those personalities.

Chatbots

Chatbots are great solutions to answer customer client inquiries. AI-powered bots start conversations on websites and mobile apps, providing customers with answers to frequently asked questions or assisting them through the purchasing or application process. Customer support personnel can devote more time to more complex jobs since they don’t handle general queries. Even if chatbots cannot resolve a problem, they may still direct consumers to the most practical assistance, such as a human expert or a knowledge base article.

AI-Powered Agents

One of the most popular artificial intelligence call center tools, AI-enabled assistants aren’t only providing customers with the information they require; in the background, they’re also feeding human agents intelligent data and analysis to generate better, faster results without consumers being aware. Virtual assistants can analyze spoken or written comments from customers to determine what they’re attempting to accomplish. Then, instead of recommending solutions to the client, they suggest a few options for the agent, who may use her human abilities, such as sensing and responding to emotions, to choose the best option.

AI can help sales teams make more informed judgments to boost client loyalty and satisfaction. Companies increasingly apply machine-learning technologies to transform hundreds of data types, such as a person’s frequency of requesting assistance or uttering phrases like “I’m canceling my account” into overall consumer risk scores. When the scores reach certain levels, the system sends recommendations for custom deals like rebates, discounts, or other perks.

Emotional Intelligence AI

Emotional intelligence is another type of artificial intelligence call center technology that can analyze customer feelings during a conversation. When a customer is irritated, their voices may arise, and there might be a long silence in the conversation. This type of AI has been trained in various languages and cultural settings, allowing it to be used in countries with diverse linguistic and cultural traditions. It employs a tone of voice and language pace analysis to determine the caller’s mood.

The AI will also evaluate how often an agent interrupts a client and the tone of voice of both the customer and support representative. It will then provide live feedback (through pop-up messages) to the employee to have insight into how the consumer feels while the call is in progress.

Analytics

Artificial intelligence call center technologies are utilized to provide complete statistics on call times, first resolution, and other information. AI-powered tools can highlight trends and access consumer data that can help managers assess whether consumers have a positive or negative experience. AI can give more well-rounded analytics than a human customer support manager because it measures consumer sentiment, tone, and personality.

Retail, particularly supermarket automation, continues to move faster than ten packets of toilet rolls when a lockdown is announced. It won’t surprise anyone that AI is at the forefront of the solutions changing the way we shop, including computer vision.

We’ve seen AI-powered solutions that remove the need for cashiers in supermarkets and grocery stores before. Some use CCTV, barcode scanners, or beacons and sensors, and others require proprietary technology, but another intriguing solution has entered the market.

Flow (formerly WalkOut) comes as a device store owners can retrofit onto any shopping cart. Using AI, machine learning, edge computing, and high-precision cameras, it identifies each item placed into or removed from the cart with incredible accuracy.

“Flow is a standalone retrofitted cart with four cameras that stream video directly to the computing unit that is installed on the cart,” Assaf Gedalia, CEO at Flow, told me. “The device then can differentiate between the items inserted into the cart by recognizing its packaging. This computing unit also helps make the carts autonomous units that are non- reliant on internet or Wi-Fi.”

Yep. You read that right. Flow doesn’t rely on an internet connection to work, so when the zombie apocalypse happens, and the communication systems go down, at least you’ll still be able to buy some Pop-Tarts.

Seeing Flow in action, it’s easy to be impressed with the system’s use of computer vision and how well it captures the exact product being put in the cart, even when the same brand differentiates products with only the slightest text or image difference.

“Thanks to the advanced computer vision technology, our solution can catch every single item placed in or taken out of the cart,” Gedalia said. “This encompasses any sized item, and we provide alerts to the groceries staff about suspicious behavior and which cart said activity is coming from. Items are instantly shown on screen as soon as they enter the cart and subtracted from the total if they are removed from the cart before the final tally.”

One perennial problem with supermarket automation solutions has been assisting the customer when things are working correctly. How do staff know when there’s a problem so they can help and check the consumer’s purchases?

“We have a system that is called Store Control,” Gedalia said. “This is a monitoring tool that the grocery staff has access to, and they can perform different actions through. For example, they can check a purchase, see what products are in what carts, and help with troubleshooting remotely. There is also a help button on the cart to allow the shopper to call for assistance.”

Another area where Flow helps shoppers is with recommendations, special offers, and coupons.

“Flow is integrated into the retailer’s product base, and once the retailer tags a product with a discount or special price, that discount will appear on the shopper’s screen as well,” Gedalia said. “The same goes for any store coupons or special offers. Flow notifies shoppers based on their location in the store – if they are standing by a yogurt on a two-for-one deal, the cart will notify the shopper. Besides what the store offers, our solution will also provide the shopper with information based on the many data points we analyze from our carts. This allows us also to offer personalized recommendations like complimentary ingredients, warn of products that don’t fit a shoppers personal dietary needs, or suggest a good wine to pair with the steak they just picked up at the butcher section.”

So what’s next for Flow and supermarket automation?

“Flow has already accomplished a lot in just one year, with several grocery store implementations,” Gedalia said. “We believe that our smart cart solution will expand beyond the shoppers’ experience but also change the way store pickers build their carts for customers, indicate stock levels of products for employees with shelf alerts, and overall expand our mission to improve the in-store shopping experience for shoppers, employees, and management.”

This article originally appeared on Grit Daily and is reproduced with permission.

Foot traffic is one of the most helpful types of data for brick-and-mortar businesses to collect. Tracking how many people enter, where they go and when they leave helps understand customer behavior, assess performance, and optimize store layouts.

Businesses today have a wide array of technologies to choose from to collect foot traffic data. However, this wasn’t always the case. Monitoring foot traffic is an old practice, far outdating digital data itself, and many of its most radical innovations are fairly recent.

Manual Counting

The oldest form of collecting foot traffic information is the same as most data collection forms: manual entry. Mechanical counting tools emerged as early as the nineteenth century, with several inventors seeking patents for simple counting devices in the mid-1800s.

These handheld tools provided a more reliable measurement than counting in your head, but they still rely on manual operation. They’ll only record another count if you press the button. Still, these devices’ simplicity has helped them remain popular today, with stores placing employees with a hand counter by the door to determine occupancy.

Cameras

Foot traffic tracking transitioned to digital data with the advent of digital cameras. Using camera data to monitor people who enter, leave and move around a space removed the need for manual tracking. These records also provide context for foot traffic, not just simple occupancy figures.

Camera data can still be a helpful resource today with the help of machine vision. Amid the COVID-19 pandemic, businesses discovered they could monitor social distancing with machine vision algorithms that analyze video footage. Similar systems can analyze this data to determine customer behavior, like how they interact with various displays.  

Infrared Sensors

A more streamlined approach to collecting foot traffic data is with infrared sensors. These systems use an infrared beam to register customer movements, counting each time the beam breaks from someone passing through it. More advanced versions can even determine the direction of travel, showing if someone is entering or exiting.

Infrared data can provide real-time, reliable information, and it’s often affordable to implement. They also don’t capture people’s likeness like cameras do, which helps protect customer privacy. However, it doesn’t provide context by itself, so what you can glean from it is limited compared to some more advanced options.

Thermal Sensors

A similar alternative is to use thermal sensors. Instead of using a simple infrared beam, these devices track heat signals to monitor foot traffic. They register each customer’s heat signature as they pass through an area and provide more context than when they enter and leave.

Temperature readings can show where people gather, indicating high-traffic areas that may need reorganization. Businesses can also use them to monitor for unusually high temperatures that could indicate sickness. They can then recommend health testing, inform people of possible disease exposure, or more.

Smart Beacons

Today’s most advanced foot traffic data collection method is the smart beacon. These devices use wireless signals like Bluetooth or Wi-Fi to connect to people’s smartphones. If businesses have beacons throughout an area, they can learn what products customers look at, how they moved throughout the store, and more, not just their location.

Since beacons connect to phones, they can also connect foot traffic data to people’s browsing history and shopping habits in some circumstances. Given this wealth of information and opportunity, it’s clear why experts predict beaconing to be a $25 billion industry by 2024. However, this data does bring more security and privacy risks that businesses must consider.

Foot Traffic Data Collection Has Come a Long Way

Foot traffic data can be a precious resource to retailers and other businesses. As the tools to gather this information become more complex, its potential keeps expanding. With many of these technologies only gaining mainstream appeal within the last ten years, groundbreaking solutions may have yet to emerge.

Data is an indispensable resource for retailers. Today, most retail businesses understand that they must capitalize on digital data, but fewer know how to make the most of it. If these companies hope to reach their full potential, they must improve their data management.

Data management is often a struggling point for businesses. In a 2017 study, only 3% of companies’ data met basic quality standards, and 47% of newly created records contained at least one critical error. Retailers are rushing to take advantage of data, but poor management is holding back their results.

Improved data management can help retailers see better returns from these operations. Here are seven opportunities for these businesses to benefit from better data management.

1. Increasing Marketing Personalization

Personalized marketing is one of the most common uses of data in retail. Retailers could significantly improve these efforts by putting more emphasis on the management side of data. For example, enriching their first-party information with data from third-party sources can help target consumers on a more specific level.

Data enrichment can reveal more about the users retailers are trying to target with personalized marketing. They can then reach out to them through multiple channels, emphasizing those they use the most. This will make these efforts more effective, leading to higher conversion rates.

2. Preventing Mistargeting

Similarly, better data management can improve personalized marketing by minimizing errors. Mistargeting is a shockingly common issue, with 96% of surveyed consumers receiving mistargeted information or promotions. Retailers can avoid these mistakes by using more thorough data cleansing and enrichment.

Before using data to customize marketing campaigns, retailers should check it against other sources to verify its accuracy. Removing questionable or unverifiable information will prevent mismarketing, create more relevant messages, and keep consumers engaged.

3. Minimizing Supply Chain Costs

Another area of untapped potential for data in retail is supply chain management. Just as retailers must cleanse and verify consumer data in marketing, they must ensure supply chain data is accurate before acting on it. Better accuracy and organization in these data sets can lead to significant savings.

For example, fuel is often one of the highest fleet expenses, and fuel savings rely on many factors. Poor or misleading data about regional fuel costs, engine efficiency, or route travel time can lead to higher fuel consumption. Spending more time and effort ensuring this data is accurate will help minimize these expenses.

4. Improving the Accuracy of e-Commerce Listings

Better data management can also improve the functionality of retailers’ e-commerce sites. Poor data management can lead to errors like incomplete information on product listings or inaccurate inventory figures. These errors can then impact shoppers, making them less likely to return after a negative site experience.

Before listing new items on their online store, retailers should look over the data to ensure it’s complete. Similarly, they should clean and organize inventory data to ensure it updates in a timely manner, reflecting actual stock numbers. Even small changes like this can have a considerable impact.

5. Avoiding Stock Shortages

Keeping accurate inventory records is essential for more than just maintaining functional e-commerce sites, too. Retailers who improve their inventory data management can prevent shortages by gaining a better understanding of their needs.

Data cleansing and organization will provide more transparency over retailers’ supply of various items. Retailers can then enrich these records with sales data to predict what customers want before these trends shift. They can then adjust inventory levels accordingly to meet changing seasonal demand, minimizing waste.

6. Appealing to Socially- and Eco-Conscious Consumers

Better data management improves visibility across retailers’ processes by compiling and structuring otherwise vast, unnavigable data sets. When retailers improve their internal transparency, they can then become more transparent with consumers. This will help appeal to eco-conscious or socially-minded customers.

Data management makes it easier to track where parts and products come from or how much waste a company generates, for example. Retailers can then communicate this information to consumers to demonstrate their social or environmental governance. This transparency will build trust and can improve sales.

7. Complying With Data Privacy Regulations

As retailers collect more data, data privacy regulations become a more relevant concern. At least 21 states have proposed privacy legislation that retailers may have to comply with, requiring more insight into their data operations. This compliance will be far easier with better data management.

Deduplicating, cleansing, and organizing data will make it easier to provide any necessary documentation to authorities. Similarly, it can offer more insight into retailers’ data operations, showing whether and how they need to adjust to remain compliant. As these regulations become more common and strict, this will become a crucial consideration.

Better Data Management Is Key to Retail Success

Data can be a retailer’s most valuable resource if they can use it properly. Better data management will unlock data’s full potential, improving retail operations across multiple fronts.

These seven areas aren’t the only opportunities retailers have to benefit from better data management, but they are among the most impactful. By addressing data management in these areas, retailers can experience considerable improvements.

Data science is now essential to e-commerce success. Targeting the right audience through advertising platforms is highly necessary to boost online sales as customers only want to look at relevant products or items they need. Artificial intelligence (AI), with the assistance of machine learning (ML), helps determine the target audience based on customer preferences and past browsing data, which help bring potential buyers and score inbound sales. 

Similarly, suggesting the right products to customers on a platform also helps bring in more sales. E-commerce services like Amazon and Alibaba use data science to power predictive recommendations which help in suggesting various products that users will like. 

For advertising products on platforms like Facebook and Google that act as mediums through which e-commerce companies can run ads, there is heavy dependency on data science to show relevant ads to potential buyers. For instance, when users search for specific products on Google, it would show relevant ads for the same product from different companies.

The accuracy of AI in determining potential buyers for specific products goes a long way in suggesting to them the product they would need immediately, resulting in immediate predicted sales. Without this, the chances of buyers stumbling upon the product they would definitely like and buy are relatively lower unless they are actively looking for a product.

Data Science in E-commerce

Data science powers predictive forecasting using various data sources, such as the historical data of sales, economic shifts, customer behavior, and searches. This empowers e-commerce companies by promoting relevant products to potential buyers. Machine learning (ML) and artificial intelligence (AI) make it possible to provide shoppers with predictions based on what they like even before deciding to look for a product or if they need something in particular.

ML and AI get this done by analyzing the behavioral trends of customers and creating a relation between the past purchases. Customer sentiment analysis plays a significant role in identifying future sales prospects and the target audience, enabling direct marketing tactics and sales promotions.

Data science plays a significant role in investigating trends and discovering patterns in customer behavior and brand sentiments.

Analysts can use data science to analyze purchase patterns and develop strategies to increase sales and effectively stock the inventory. Businesses can further utilize data analytics to predict sales and demand, which helps companies make better decisions to advertise or stock up on specific products.

How is Data Science Boosting Sales in E-commerce?

There are many ways in which data science is boosting sales in the e-commerce domain. Some of these are: 

Recommendation Systems:

Data science powers recommendation systems that are entirely based on the past data of users alongside the heavy use of ML and AI to help e-commerce services give more relevant and accurate recommendations. This works like a charm and seems almost to recommend products that users will always wish to buy or at least show interest in. This translates to increased sales by producing the right product in front of the right buyer.

Recommendation systems are personalized according to customers and modeled with the help of user information, such as products a user is buying and pages a user is clicking on. Amazon’s recommendation system and Amazon Personalize have helped improve sales; both are an integral part of Amazon’s armory, which now controls 40% of total US e-commerce revenues.  Notably, according to Barilliance, product recommendations account for up to 31% of eCommerce site revenues.

Customer Feedback Analysis:

Data science allows e-commerce companies to work on their shortcomings by collecting the relevant feedback for each product or service and then taking action based on the collective analytics. Methods such as sentiment analysis and brand image analytics help companies understand what a customer or the target audience requires, increasing sales significantly.

E-commerce giants and startups use NLP or natural language processing, text analysis, text analytics, and computational linguistics to power analytics of this kind.

Inventory Management:

Data science allows established e-commerce companies and startups to manage their inventory more effectively. This also indirectly helps them not waste capital on unpopular products which are not selling well and have no need for restocking. Since e-commerce companies work with tons of customers and thousands of products daily, advanced data science is highly necessary to conduct accurate inventory management and predictive forecasting for future requirements.

Room and Board used predictive analysis to get around 2900% return on investment.

Customer Experience and Customer Service:

Data science helps ease and improve customer experience by automating a lot of functionalities and making regular things hassle-free with the help of feedback and analytics. These implementations can range from automated experiences to easier navigation.

As per reports, around 80% of customers are of the opinion that customer experience is also important and helps them come back to a specific site. In addition, determining preferences via social media can also improve customer service, and recommendations as many millennials and Gen Z have discovered products via social media platforms like Instagram.

ML is especially useful in customer service as it leads to better IVR and chatbot services which help solve customer issues more effectively with time.

Tools like Sentiment Analysis are quite good at understanding customer experience and helping companies retain them.

Does data science help e-commerce companies advertise better?

Yes, data science helps in advertising analytics as well. Also, advertising platforms run on AI and ML, using data science to perform various functions like audience targeting through behavior and other factors, such as demographics. Notably, data science allows e-commerce companies to run relevant advertising campaigns. 

How is machine learning used in online sales?

Machine learning promotes online sales in various ways, from virtual assistants to personalized recommendation engines. For example, ML helps convert more browsers or prospects into immediate buyers with the help of customized recommendations increasing the chances of conversion. Also, it helps in gathering new customers based on historical data. 

In Conclusion

Data science arms e-commerce giants with the power to reach out to their customers and provide them with a personalized experience.

This is quite certainly leading to an enhanced shopping experience for customers and increasing online sales for many e-commerce companies.

Data science has proved itself to be highly useful to gather customers as well as increase profits. 

Without a doubt, publishers are well-placed to harness the relationship with their audience – possessing the means to collect and build the strong first-party data sets required to deliver personalized experiences and power various revenue streams.

But, as the industry moves further away from cookie-based targeting – and Google speaks out against alternative ID solutions for cross-site tracking, limiting the ability to scale – publishers must find new ways of boosting and communicating the value of their inventory while also ensuring monetization strategies align with the user experience.

This is where artificial intelligence (AI) comes in, playing a key role in achieving this balance.

Opportunities aren’t disappearing; they’re just different

Today’s advertising landscape is increasingly complicated. Most digital ad spend goes to targeting and retargeting specific individuals, which relies on consistent visibility and computability of identity. Google’s move has put this approach on the ‘endangered’ list and will likely add to existing fragmentation. Constructing addressable identifiers was already difficult – with users spread across laptops, mobile, CTV, and other smart gadgets – but now, brands will also have to switch between different technologies and systems when using Google or the open web.

On the publisher side, this will affect personalization strategies as a means to deliver value, both from a content and advertising perspective. However, it also offers publishers an opportunity to play a more central role in providing access to addressable audiences for advertisers looking to optimize ad spend through content-rich experiences.

By using AI technology, publishers can facilitate the data onboarding process and match brands’ first-party data with their own addressable audiences with a higher accuracy rate than other non-AI tools. When applied in conjunction with cleanroom technology, this provides a privacy-safe and publisher-controlled space for data collaboration that matches audiences on a similarity-base, enabling incremental reach in private marketplaces.

AI offers a route to effective reach enhancement

The two core aces publishers hold are, of course, content and consent. Producing engaging content helps win user engagement and loyalty, while user-centric consent increases the chances of building trust and gaining permission to collect and use much sought-after first-party data. On this basis, publishers are in a good position to build on the foundation of their first party-data strategy to deliver basic reach for known, logged-in users.

The issue, however, lies with the limitations of consented data. Not all users will be willing to share data. In fact, it’s widely considered that just 2-10% of consumers share details such as age and gender.

To sustain optimal reach, publishers will therefore need to explore options beyond the log-in walls. Those keen to keep content as openly available as possible will likely turn to using the data processing and enhancement capacity of AI to build on first-party data strategies. High on the list of uses is predictive modeling, powered by machine learning. By taking consenting user attributes as an analytical base, it allows for the accurate extension of addressable reach – in line with customized and verifiable accuracy rates set by each publisher – even when deterministic data is lacking.

For instance, when used in tandem with real-time contextual data, AI can drive impression-level targeting without user-level data. With every use case, the main appeal is that an emphasis on inferred— not declared — characteristics keeps privacy front and center, enabling personalized experiences and targeting without hindering the user experience.  

An example of how this could work in the real world is with recruitment data. Publishers with recruitment advertising departments can harness tools to integrate data from job seekers to display highly targeted ads to relevant candidates. AI can then be used to scale reach, expanding the audience based on the initial recruitment data to reach other statistically relevant consumers without impacting the user experience.

What next for the industry?

Gazing into the collective industry crystal ball is never easy, but there are signs of which way the winds are blowing. For instance, the latest proposal to emerge from Google’s Privacy Sandbox initiative, FLoC, suggests the use of machine learning analysis to create a cohort-based approach to targeting.

For publishers previously wary of AI-assisted audience syndication, this could be good news: allowing them to build stronger ties with advertisers and pave the way to scale audiences. Setting aside the debate around whether or not FLoC will be anti-competitive, there is no denying that it will likely drive further development of machine-learned segmentation and personalization, which is a good move for the industry.

In a continuously changing industry, AI ultimately provides an opportunity for publishers to be optimistic about their ability to balance personalized experiences with privacy-first monetization strategies. The advanced solutions offered by AI empower publishers to forge their own path and equip them with the tools required to show that they are not merely providers of first-party data but lynchpin to scalable privacy-safe solutions.

As consumers across the globe become increasingly aware of their digital identity and personal data rights and further regulations take hold, it’s unsurprising that Google has announced it will not be replacing third-party cookies with identifiers and email addresses.

Advertisers now need to look for new ways to engage valuable customers on a one-to-one basis. Digital targeting and measurement strategies that the industry has grown up around will need to be rebuilt for a privacy-first world.

This is both a challenge and an opportunity for the industry – to champion privacy while finding new and innovative ways to provide marketers and consumers with relevant, targeted ad experiences. The industry needs to determine the best path forward and partner to develop strategic identity solutions, enabling publishers to maximize the value of their first-party data, help advertisers meet their business goals, and build consumer trust in digital advertising.

A new vision for a new digital identity ecosystem

Collaboration between partners within the digital identity and advertising ecosystem is now more important than ever. Suppose advertisers want to increase the effectiveness of their campaigns across the whole of the Internet. In that case, they need to be working with partners who can join up these conversations without operating a walled garden. Greater collaboration is also vital for local premium publishers to continue developing creative, engaging content for consumers, which is the foundation of their ongoing success.  

The central principle of navigating this changing landscape is for the digital advertising industry to understand where it goes with respect to identity, and it needs to do that with consistency. This means how it will handle identity in the face of the death of third-party cookies, the rise in regulation, and the evolving ways that it is buying and selling advertising today.

Increasing regulation around data privacy – such as the GDPR in Europe – has been one of the biggest drivers for change in our industry. So, advertisers will want to work with companies adhering to data regulations and encouraging transparency within the supply chain. On top of that, many brands will need to feel a sense of ‘safety through familiarity.’ When discussing compliance, it helps to work with a partner with similar challenges, protocols, and internal processes. For example, a bank or a telecommunication company is going to want partners that can demonstrate their security frameworks meets the country’s data privacy standards, as well as your company’s individual privacy standards.  

With cookies, these have been relied on for a very long time, yet we’ve seen over the past year or two that we can generate brilliant performance leveraging solutions that do not rely on this. However, as things stand, there isn’t one silver bullet to identity or one single solution, and it won’t be solved for some time. What needs to be done now is to take a very deliberate multi-pronged approach to solve identity. While first-party data goes some way to achieving this, brands can get market-leading performance and competitive advantage even by just using strong and innovative contextual solutions. It’s important for brands not to stand still at this point; testing innovative new solutions will mean you’re well equipped to deal with what comes next. 

Adopting new models to meet changing needs

For publishers, this means that they need to look at how they can use their proprietary assets to evolve their business models and package and sell their inventory in a way that best meets the needs of the buyer in our rapidly changing digital advertising landscape.

Developing different ways to generate and acquire authenticated first-party data will be one key area of focus for publishers. Many are already doing that as they look to build out subscriber bases. This means that if a person uses their email address every time they visit a site, the publisher can use it as a persistent identifier. From here, they can start to build a profile of that user and what their interests are. By better understanding individual users, publishers’ inventory becomes more valuable to advertising partners, as they can effectively target specific audience profiles. 

Alternative ways that publishers can use their assets, such as building up contextual solutions. The ability to build contextual profiles has advanced greatly since the early days of simply placing adverts for mortgages in financial publications. Today there is much more accurate contextual information about specific articles, so publishers should be looking at utilizing this. Today you can even use contextual solutions to match the sentiment of a piece; for example, if you’re a brand selling retro cameras, you can target context that generates the feeling of nostalgia. 

In the future, publishers will need to consider device-based advertising. If we consider the devices that will support advertising or do already support advertising, very little of that is cookie-based anyway. A raft of different devices will come into play here, such as smart speakers, CTV, and even wearable tech. None of this will be dependent on a cookie, so there needs to be continued investment in exploring these areas and the new audiences they offer. 

With the right data protection, privacy controls in place, and the right partners on board, it remains possible to provide consumers with critical choices and insight into the value exchange of advertising and content. By these means, we can also ensure that we enable publishers and marketers to achieve the required outcomes. At this point in time, the worst thing you can do is stand still and wait for something to happen around you. Your audience is still there online, so it’s important that you take all the steps necessary to continue connecting with them.

Data monetization can be an effective tool for helping companies and sectors boost profits and keep consumers happy. Here are five of the industries that data monetization strategies could benefit the most.

Music

The music industry experienced a prolonged period of upheaval due in large part to streaming services’ popularity. The shift to streaming and away from physical music initially caught many industry executives unprepared, making them scramble to deal with the change.

They’re more accustomed to it now, especially since the COVID-19 pandemic helped more people get acquainted with streaming concerts. Grammy-winning artist Brandi Carlile held several ticketed live streamed concerts, with proceeds going to her crew members who were out of work due to the pandemic. Carlile also chose several charities to support with the generated income.

Perhaps an artist keeps track of the number of viewers for a live stream or the percentage of people who buy tickets several days or weeks before an event. Then, it’s easier to determine whether internet-based concerts could prove profitable.

Also, streaming service Spotify offers a significant amount of data to artists who use the platform. For example, musicians can see stream count updates of new releases for the first week of their availability. The number updates every two seconds to give artists accurate perspectives.

Spotify also shows how listeners come across tracks, whether by discovering them through playlist mixes or other means.

Automotive

Today’s automobiles are getting progressively more advanced, and that typically means they collect more data that companies can monetize.

Statistics also indicate that many consumers don’t mind if car manufacturers gather data from them. A 2020 McKinsey & Company study revealed that 37% of consumers would switch to car brands that offered enhanced connectivity.

One data monetization possibility is to track trends related to certain models, color choices, or other features in particular markets. Then, manufacturers could ensure dealerships have the cars that are most likely to sell.

A General Motors representative confirmed that the data it collects generally relates to a car’s location, driver behavior, and vehicle performance. However, they said that the company couldn’t link much of the data to particular individuals.

Brands aiming to roll out successful data monetization strategies should safeguard against privacy violations. If consumers feel companies know too much, they could show progressive unwillingness to use data-sharing features.

Retail

There’s growing interest in data monetization across industries. One study found that more than 91% of executives polled noticed increases in related investments. For example, if a company representative purchases a data analytics platform subscription, they could see insights that might otherwise get overlooked.

The retail sector is an industry with tremendous potential to benefit from data monetization. For example, a brand could track how many e-commerce shoppers redeem a discount code associated with a particular social media campaign. Such statistics help determine whether the effort got the desired results.

Alternatively, physical store data monetization could involve tracking the busiest shopping hours. Perhaps a manager realizes many people leave without buying after seeing crowded store areas or long lines. If so, the solution could be to staff more employees to cope with increased demand.

A typical data monetization challenge happens when brands collect too much information, and there is not enough time to analyze it thoroughly. Thus, retailers seeking to maximize their benefits should choose a few desired goals and determine what kind of data is most helpful in achieving them.

Healthcare

People in the healthcare industry are well-accustomed to using available data to make the most appropriate care decisions. A patient’s lab results or vital signs often dictate which treatments to provide and when. However, organizations can also use data to support profitability.

One example is to explore the issues behind missed appointments. When people don’t show up, that problem prevents a facility from opening the slot to someone ready and willing to take it. A closer look at the data might indicate that most no-show patients assert they did not know they had appointments scheduled.

A text message that automatically adds a person’s appointment to their digital calendar would reduce the issue. Additionally, a data monetization strategy may indicate that many patients could get the necessary care outside of real-time visits.

New Mexico’s Presbyterian Healthcare Services began using an asynchronous communication system several years ago. In 2020, staff members fielded 50,000 low-acuity care queries, each taking an average of two minutes to complete. Patients usually got responses to their text-based content within 15 minutes.

This approach highlights some possible metrics to track during a data monetization effort. For example, how long do patients wait for answers? What percentages of cases can providers tackle without in-person or video-based visits?

Marketing

Data monetization is already a common practice in the marketing sector. However, research indicates the trend will continue.

A January 2021 study indicated that 88% of marketers intend to prioritize gathering and storing first-party data. Although 58% of respondents considered it a high priority, 30% noted it was their utmost concern over the next 6-12 months.

However, the company that conducted the study indicated the growing importance of zero-party data. First-party data comes from customers’ interactions but often gets collected in the background. Zero-party data is information that those people intentionally give to businesses.

Monetizing data can improve marketing outcomes in numerous ways. Many companies look at data while planning campaigns or choosing which advertising channels to use for particular audiences.

Marketing professionals can also apply data analytics to determine which outreach methods will likely resonate most with specific audiences. While working out a strategy, company representatives should assess known challenges and how increased information could overcome them.

Data Monetization Makes Sense

These are some of the sectors most likely to profit from data monetization initiatives. However, other industries could see similarly positive outcomes, especially if representatives take care to ensure the data’s reliability.

International Data Privacy Day is almost here. January 28 is a chance for all of us to raise awareness, remind ourselves of our commitments to data privacy, and ensure we know data protection best practices.

Data privacy (sometimes called “information privacy”) is a subset of data protection that deals with the proper and correct handling of data with a strong focus on compliance with data protection regulations.

Therefore, the focus is on how data should be collected, stored, managed, and shared with any third parties and compliance with the applicable laws and regulations (such as CCPA or GDPR).

While linked to data security, it is not the same thing. Data security is concerned with the measures you take to prevent third-party access to the data you are storing.

Data privacy laws

According to the UN, 128 out of 194 countries have passed legislation to secure data and privacy protection. 10 percent of countries have drafted legislation, while 19 percent have no legislation at all.

Familiarizing yourself with the applicable data privacy laws that affect you – usually your server’s location and the location of those you are collecting data from – is important. The UN’s tracker makes it easy to see what bills have been passed in each location.

GDPR, for example, applies to any company or entity that processes personal data as part of the activities of one of its branches established in the EU, regardless of where the data is processed, or to any company established outside the EU that is offering goods or services (paid or for free) to EU citizens or is monitoring the behavior of individuals in the EU.

That’s important to remember, and it means that you need to keep abreast of your obligations regarding several different laws. While most companies will need to comply with at least GDPR and CCPA, staying compliant with the likes of PIPEDA (Canada’s data privacy legislation) and other major laws is important.

Data privacy prerequisites

Keeping on top of all of those regulations sounds daunting, but some basic prerequisites will ensure you stay on the right side of all legislation.

Beyond the requirement to keep up with the latest data privacy regulations, there are two other key elements to focus on.

One is the right of an individual to be left alone and retain control over their personal data. The second element is the necessary procedures for properly handling, processing, collecting, and sharing personal data.

The first element reminds us that as an organization, you are only borrowing the personal data of the individuals you are processing. Remember: you do not own this information.

Individuals, therefore, should always have the right to be forgotten.

So an important part of data privacy is transparency. It would be best if you showed, by openly communicating with your clients and potential customers, what you collect, why, how you’ll process that data, where you’ll process it, and whether or not third parties are involved (and gain permission for that transaction).

The good news is that transparency breeds trust, and trust is crucial to gaining a customer and keeping them. Salesforce’s State of the Connected Customer reports showed a big shift in the need for trust between 2018 and 2019. In the 2019 report, 73% of customers say companies’ trustworthiness matters more than it did a year ago, and 54% say it’s harder than ever for a company to earn their trust.

In its 2020 report, Salesforce states that nearly half of customers have stopped buying from companies because of privacy concerns.

Transparency, trust, and the ability to communicate exactly what you are doing, and how you’ll react to consumer requests will not only ensure you’re staying on the right side of data privacy legislation; it will give you a competitive advantage.

Data privacy tools

Of course, you don’t have to navigate data privacy alone. There is an ever-growing number of data privacy management, consent management, and data subject access request (DSAR) platforms available that help to keep you up-to-date and compliant.

Also, a simple search will deliver an almost infinite number of data privacy consulting firms. Of course, do all of your due diligence, and make sure you read independent customer reviews before engaging with an agency.

But the message here is clear. On this International Data Privacy Day, you’re not alone. Despite the enormity of the problem and the complexity of the solution, you can rest assured that the knowledge you need, the tools to help you, and the people that can assist are available to ensure you get it right.

It’s essential for organizations to be aware of shifts in employee spend that invite different risks. The data scientists at Oversight, an enterprise spend management software, reveal the ongoing spend shifts organizations face and how the pandemic has quickly reshaped travel and expense employee spend.

A high-level look at the changes from March into April includes:

Top 5 Employee Spend Categories in March 

1. Hotel – 51%
2. Mail/Phone Orders – 19%
3. Restaurants – 12%
4. Business Services – 10%
5. Miscellaneous Stores – 8%

Top 5 EmployeeSpend Categories in April

1. Business Services – 27% (+ 17)
2. Restaurants – 22% (+ 10)
3. Hotels – 20% (- 31)
4. Miscellaneous Stores – 17% (+ 9) 
5. Mail/Phone Orders – 14% (- 5)

As the pandemic grounded enterprise sales teams and business travelers around the country in March, the impact on T&E, especially on items like hotel rooms and air travel, was swift and linear. 

While overall spending in T&E headed predictably downward, other categories of employee spend grew, showcasing the changing dynamics of organizational spending and introducing risk that can impact cash flow. 

The Risky Rise of Gift Card Purchases and Food Delivery 

Interestingly, as airline and transportation costs plummeted, purchase activity was higher than expected in the typically high-risk categories of mail/phone orders and miscellaneous stores, exemplified by employees making purchases from retail vendors like Amazon, Best Buy and Apple. Many of these spend bursts were to support suddenly home-officed employees who had to quickly adjust to a new work environment. Separate from the need to support remote office setup has been the effort of sales and other client-facing teams to stay top-of-mind with customers during the pandemic. Organizational spend on meal delivery services, such as DoorDash, as well as gift cards are growing in the absence of face-to-face meetings. 

Notably, 37% of purchases in the restaurant spend category were attributed to meal delivery services in April. Among gift card purchases, $200 is the average gift card spend, with Starbucks and Uber Eats among most popular. 

These types of purchases present a unique challenge for finance teams tasked with ensuring that expenditures comply with policies. Frankly, these “once bad, now good” purchases are precisely the sort of line items that used to require increased scrutiny. With limited visibility into the recipient of gift cards and meal deliveries, it can be difficult to determine whether a purchase is for legitimate business purposes or personal use. 

Spending to Extend Work from Home

Most employees in professional settings in America transitioned to remote work sometime in mid-March. They did so quickly, and without long term plans in place should work-from-home arrangements extend indefinitely. So, it comes as no surprise then, that more than two months into the pandemic, a second wave of purchase activity for office supplies and equipment is sweeping across enterprises.  In fact, some high-profile companies have recently announced generous employee allowance programs to enable more robust home office set-ups. 

Miscellaneous spend is up from 8% of purchases in March to 14% of all spend in April. Among that spend is the mail/phone order purchasing, which captures transactions with online retailers such as Amazon. 

What are employees purchasing? Electronics. 46% of mail/phone order spend is attributed to electronics, office supplies and equipment. And, Amazon purchases alone account for 28% of all mail/phone order spend and 11% of all miscellaneous spend across organizations. 

The challenge is sorting the good office purchasing from the bad. Merchants included in the miscellaneous/online ordering category tend to sell a wide array of expensive items. Some Oversight customers report uncovering employee attempts to expense big-ticket items like TVs and soundbars, while others have detected extreme home office expenditures to the tune of more than $7,000. 

Buying Blind Via Third-party Payments

As detailed above, online shopping is up for organizations during the pandemic – 74% across all of e-commerce year over year, according to the Associated Press. Where things get murkier for organizational spend risk is when those purchases are made through third-party payment platforms like PayPal and Stripe. Those third-party platform purchases are up 43% year over year for April, and 28% for the quarter.  

The reality of third-party payment platforms is that they offer varying and often limited visibility into transaction data. The lack of transparency is so noteworthy that many organizations fully restrict certain third-party payment platform transactions altogether. 

Air Travel Stats: An Impending Rebound?

Lastly, the Spend Insights report showcased a potential rebound in airline spend in April after hitting a low the week of March 30. 

Organizational Airline Spend, week over week in April:

• March 30 – April 6:  – 50% 
• April 6 – April 13: + 147% 
• April 13 – April 20: + 53% 
• April 20 – April 27: + 2% 

Airline spend, of course, predicts future travel. With airline travel dollars flatlining throughout March and into early April, business life as we knew it ground to a halt. As April data shows a cautious return to future air travel, perhaps organizational metrics suggest, for the first time since the onset of the coronavirus that T&E spending, and by extension life as we once knew it, could be on the rebound. 

Not knowing what the future holds with certainty, however, it’s essential for organizations to be aware of shifts in spend that invite different risks. Spend management technology provides continuous transaction monitoring to identify and prioritize risk – even when it’s constantly changing. With technology in place, organizations can act quickly to address issues that can compromise their cost control efforts, which are now more vital than ever. 

SMEs are the unsung heroes of any economy- producing numerous jobs, the backbone to big corporates to scale up and still struggling to sustain themselves. They are unable to innovate as fast as younger companies and lack the funds that larger corporations have. 

As we enter a new decade, over half (58%) of small businesses in the UK anticipate a plateau, or for their business to struggle in the year ahead. Only one in four predict to see growth.  

A recent report by takepayments limited surveyed 1,000 small business owners and sole traders across the country including decision-makers taking a snapshot of the current UK small business landscape. Here is a look at the key challenges for small businesses in 2020 and tips on how to approach these challenges. 

Financial challenges are the biggest concern for businesses in 2020 

Small businesses listed finance and tech issues at the top of the list, followed by changes in society. Finances are a major challenge, with 63% of small business owners being “self-taught” about taxes and invoicing. When asked in more detail, pricing and cash flow came up as key worries in this area. 

1. Financial challenges 36%  
2. Technology risks 21%  
3. Societal changes 13%  
4. Talent and people 12%  
5. Supply and logistics 5%  

“Pricing structure and cash flow are both key to driving a business forward and yet for small businesses, many have had no or very little training. There are plenty of online courses available but, it’s not just training that can help. 

Technology such as Electronic Point of Sale (EPOS) systems can be beneficial in giving key insights real-time to business performance, saving time on accounts and getting a true reflection of what products and services achieve the best margin.” 

Sandra Rowley, Head of Marketing at takepayments Limited

The study revealed that 49% of small businesses have seen a decrease in consumer spending and so implementing technology to improve your business could be a smart step to help overcome a variety of finance related challenges.

Is there a small business tech gap? 

Technology-related challenges are the second biggest challenge for small businesses – 1 in 5 named it their main concern for 2020. Keeping up with technology was a key issue, as was cybersecurity, compliance and online marketing. 

All three of these areas are specialist skills and a small business won’t always have the resource available to be “experts” in each. Social media marketing, in particular, stands out as a key area where small businesses could see quick wins in 2020…   

• 45% want to try more  social media marketing but lack the knowledge and the same percentage say they are worried about keeping up with social media marketing  
• 52% say social media has helped their business grow and gain new customers, proving the potential of this marketing strategy 

Focus on sustainability is here to stay

51% of small businesses have noticed that their clients/customers are caring more about sustainability over the past year. This trend is set to grow further in 2020 and businesses need to make it a consideration in all areas, from supply chain to décor, employee wellbeing, and product packaging. 

How to cope in a cashless society? 

Currently, 42% say that a move towards a cashless society would be “bad news” for their business, despite the benefits that can come with this. This is likely because they do not have systems in place to support the trends.  

More than five million people lead a close to cashless lifestyle  according to a BBC  report and so making sure businesses are prepared for cashless technology will be crucial for the year and beyond. 

There will be plenty of change over the year following Britain’s exit from the EU, so make sure to stay up to date with news. Especially if your business is reliant on imports and exports. 42% are worried about potential changes to rules regarding this. 

Which are the industries that are likely to be impacted the most by alternative web data this year ? Here is a look.

For the past few years, financial institutions, such as hedge fund managers, have been on the forefront of harnessing the power of alternative data solutions to help drive investment decisions and strategies.  Research has found that 78% of hedge fund managers are using alternative data solutions to discover greater insights into market trends and strategy. In the early 2000s, hedge fund managers and financial industry pundits would look to more traditional data, such as SEC filings, quarterly earnings and press releases, as means to help drive investment strategy. But these days the web offers so much “alternative data” including survey data, location data, customer sentiment and social media reviews that can help offer deeper insights and market intelligence.

Taking the lead from the financial industry, in 2020, more industries from online retail to travel organizations are also looking to web alternative data solutions to uniquely combine data sets to uncover customer insights, market intel, competitive advantage and trends from the web.  

According to Opimas, as the range of use cases for web data integration rapidly increases, so has spending on alternative data –  with spending to hit nearly $7 billion in 2020. This trend has driven the need for more alternative web solutions given the complexity of creating and maintaining web data extractors and preparing data for consumption.

What is Web Data Integration?

Given that the web is an immense resource for business insights industries have been using  a process known as web scraping. Unfortunately, many organizations found that web scraping projects were complicated, labor-intensive and often required organizations to employ IT specialists or engineers to write custom software for every type of web page that they want to target. Today, new alternative web data solutions are providing a better technology to scour the Internet called Web Data Integration or WDI –a new approach to acquiring and managing web data that focuses on data quality and control.  This is an integrated process composed of the following steps:

• Identification of data sources and requirements
• Web data extraction
• Data preparation and cleansing 
• Data integration and consumption by downstream applications and business processes
• Analysis and visualization

Online Travel Industry Reaping the Benefits of Alternative Web Data

The web provides an unprecedented amount of consumer information for the myriad of travel-related businesses, from hotels to vacation rental property management.   

As most travelers have taken the booking process into their own hands thanks to technological advancements and digital trends, much of the buying experience is now through the web. Therefore, web data from these purchases and subsequent customer reviews of their travel experiences, offers a wealth of information for vacation rental companies, hotels and destinations to make more informed business decisions. 

Many property management companies who rent out vacation homes, for instance, are finding that web data provides more visibility into availability, convenient bookings and the ability to easily compare travel options. These are major factors expected to drive growth of the global online travel booking market.  

In addition, in using web data, travel companies can discover the hottest travel destinations as well as understand traveler’s origins and preferences.

The upshot is, today there is so much data available on the internet with deeper and broader insights. 

By combining the web data sets in unique ways, the travel industry leverages their own set of alternative data that can help ascertain the following:

• Metrics on vacation rentals such as occupancy, average daily rates and revenue per available night;
• How to identify the relative performance of vacation rental properties across different booking sites;
• How to gain visibility into inventory availability based on season and location;
• Where are travelers going, when are they travelling and where are they staying?
• What are the travelers’ reviews of the properties and are travelers reviewing the same property differently on different websites?

Online Retail Industry Becoming Alternative Data-Driven

Much like the travel industry, retail organizations with an online presence have much to gain from the intelligence that the web can offer. As more customers shop, review and transact online, retail organizations can gather vital consumer trends that will be key to their business strategy.

Alternative web data solutions can help retail organizations gather reliable and accurate data from any e-commerce website, which in turn can help retailers provide better customer service for online shoppers.  

In addition, alternative data is helping retailers to enhance the consumer shopping experience by using web data intelligence to suggest additional products that may be of interest to customers – usually based on data of the products already purchased. A recent study by Deloitte Digital found that 75% of customers expect brands to know their purchase history, and nearly 50% of customers “love it” when companies bring up their last interaction. 

Alternative data solutions can also help retail organizations by automatically gathering data from any ecommerce website as well as matching a company’s merchandise with competitors’ offerings by capturing data on categories, brands, prices, and other parameters. These solutions can also provide a complete analysis of all products and prices, delivering automated alerts on price changes that can be generated hourly, daily, and weekly. 

The fact is, we all live work in a digital economy where petabytes of insightful data live on the Internet.  As we enter 2020, organizations need to harness that information in order to ascertain a 360-degree view of their business ecosystem that will make customer insights and other crucial trends and information more transparent. 

As such, today alternative web data solutions are providing easier ways for organizations in multiple industries from finance to retail to procure and interpret even the most complex of analytical requirements – without it becoming too expensive or difficult to operate. For instance, some web integrations platforms allow clients to manage the web data lifecycle in-house – with access to critical developer tools as well as training and support. However, the ability and expertise of working with web data to cull and combine unique insights is not something that every organization has access to and should look to companies with that unique WDI skillset to open an additional avenue to business success.

The upcoming end-of-support deadline presents serious risks for late adopters and significant opportunity for IT professionals. Here is how MSPs can create new business by urging customers not to wait until the last minute to upgrade.

The end of support for Microsoft Exchange 2010 is approaching and MSPs must take note. The end-of-support deadline on Oct. 13, 2020, might concern many late adopters, as it will present several dilemmas for businesses who haven’t upgraded. Those risks include no more Microsoft technical assistance with documentation, phone support or general troubleshooting; no more protective updates, bug fixes and security patches to ward off ransomware or cyberattacks; and potentially severe compliance issues for enterprises running outdated Exchange software that no longer adheres to industry regulation standards.

These are serious risks and a terrifying proposition for those caught flat-footed and unprepared. But while daunting, the deadline presents an advantageous window for managed service providers and IT professionals. It allows them to not only come to the rescue, but also generate new business by assuring that their customers’ software is upgraded, secure and fully compliant. 

For those who adequately prepare ahead of the October deadline, there is much opportunity to capitalize before that happens. We’ve addressed the implications of utilizing Exchange 2010 past the end-of-support date, now let’s explore the options for a new destination and the broader service opportunity this deadline presents.

A Cloud Solution

Simply put, your first question will be, “Where are we headed?” There are two main options. The first option is to make the leap to the cloud and immediately upgrade to Exchange Online/Office 365. This cloud-based software offers an array of benefits, including automatic updates, data loss prevention and disaster recovery.

This option is opportune for clients who are ready to break from an on-prem environment. By most accounts, this is the preferred option from the Microsoft perspective, as the industry leader continues to nudge enterprises off-prem and into its cloud suite with the consistent rollout of new features and products, like the Microsoft Office suite, SharePoint and Teams. This route also affords streamlined adoption of current enhancements, as upgrading assures your company is guaranteed the latest version of Exchange, plus all the functionality and applications without maintaining on-prem hardware. Organizations are ensured their software is consistent and appropriately upgraded companywide.

Of course, businesses that take this path will be susceptible to price changes from Microsoft, as it has instituted increases in the past. But the pros vastly outweigh the cons and will lead to an enhanced work environment for your client. 

A Hybrid Upgrade

For many organizations, moving email and personal archives to the cloud may not be possible or pragmatic for a variety of business reasons. These organizations may be protective of their company’s email and want more control of their data within their own workplace system. Or they simply need to continue relying on on-prem hardware for their workflows and therefore require a hybrid upgrade. In this instance, the second option would be their preferred strategy: to upgrade to Exchange 2016/2019.

Migrating to a newer instance of Exchange keeps your company protected against the implications of an out-of-date server. Again, once Exchange 2010 enters end of support, it won’t be patched for any new viruses or security problems, leaving late adopters exceedingly vulnerable to emerging threats. The new instance of Exchange allows for boosted security and sturdy, tried and true recovery and backup options for your data. The upgrade also offers enhanced performance and manageability for end users. Depending on the organization and its size, this option may even prove more cost-effective for on-prem users, requiring a one-time expense instead of an ongoing operational rate. Should you embark upon a hybrid deployment, it may ultimately offer the best of both on-prem and cloud environments.

But take note: The move from Exchange 2010 to Exchange 2019 will require a “double-hop” migration, meaning the business will first need to migrate to Exchange 2013 or 2016. While there are a host of upgrades that need to happen in this scenario, utilizing a tool like BitTitan’s MigrationWiz helps avoid this middle step by migrating data directly from Exchange 2010 to 2019. 

A Digital Makeover

There may also be opportunity for MSPs, beyond simply updating Exchange 2010, to install and integrate a new workplace plan, especially for those who are hesitant to embrace new technologies. Exchange is only a single component here, but as significant as it is considering the impending deadline, it can potentially be leveraged toward a more transformational and holistic project to enhance a client’s digital environment.

For example, Windows 7 support ends in January 2020. This product remains popular, as Netmarketshare reported earlier this year that Windows 7 is still being used on 39% of all PCs, creating ample opportunity for migration projects. With Windows 10 now included in the Microsoft 365 bundle, a pitch could be made that the Exchange 2010 upgrade provides a good window to perform both of those migrations at once. SharePoint 2010 also presents an opportunity for IT pros. Its end-of-support deadline will follow shortly on Oct. 13, 2020, and the migration options are similar for an upgraded on-prem instance or the full transition to Office 365. The timeline is convenient for many migration projects – and underscores the importance for IT pros to act. 

Every Windows product has a finite lifecycle and dealing with it isn’t always easy. Yet this end-of-support instance is about more than just upgrading software. Perhaps most salient is that enterprises still clinging to Exchange 2010 are missing out on a host of Office 365 functionalities that hold the potential to significantly enhance their work environment. Don’t let a customer’s hesitance to change interfere with their ability to scale their business and don’t let them put their business at risk. Trusted managed service providers should leverage this major end-of-support cycle to lead their customers toward better technological functionality and service by helping them navigate an ever-evolving cloud landscape. It’s an opportune time for MSPs to revisit their client portfolio and make sure each customer is upgraded, secure and compliant. By introducing them to more comprehensive, integrated and holistic options, both IT professionals and their customers can operate more productively, collaboratively and securely. It’s best for business for all parties involved.

Banking as a Service (BaaS) is the democratisation of financial capabilities that have fiercely been protected, isolated and hidden in silos for hundreds of years by banks.

The fact that BaaS opens up banks’ capabilities and essentially empowers anyone to be able to create their own financial products, goes against every fabric of the traditional banking industry.

Disruption of Banking by Fintech

Publishing, advertising and manufacturing are just a few  industries that have been disrupted by technology. Banking is no different, high tech start-ups have managed to bring innovation into the finance industry. With the click of a button, consumers can now perform all the functions that would have traditionally required a visit to a physical branch; from checking account balances to initiating payments. 

These digital-first challenger institutions like Germany’s N26-a purely online bank that has amassed 3.5 million customers in  Europe in four years and recently launched in the US market-have posed the biggest threat to incumbents. For a while, the mood in the financial industry has been that of David vs Goliath, new tech-savvy competitor vs old school incumbent. 

For some, however, the prospect of collaboration has been more alluring. For instance, France’s BPCE purchased challenger bank Fidor Bank in 2016 fo EUR 140 million in hopes of enhancing its digital growth strategy. 

Nevertheless, two years on, the partnership is breaking up over reasons that include a culture clash. Banking as a Service, on the other hand, provides a way for banks to collaborate with third parties with less risk.

Banks open up specific functionalities such as international money transfer, Know Your Customer (KYC) or account data, and allow third parties to manipulate these functionalities to build new or related services. Therefore, making banks marketplaces or aggregators of financial solutions.

Furthermore, this open banking revolution has been exacerbated by new regulations like the Payments Service Directive II (PSD2) in Europe.

How Banking as a Service (BaaS) Works 

Take your typical bank and break it down into its various functions;  holding money, remittance processing, card and payment processing. 

Banks put in a lot of investment to build out the infrastructure that supports these functionalities, including obtaining licenses and maintaining compliance measures. Because of the bottlenecks that these represent, fintechs and non-bank institutions interested in offering financial solutions find it easier to collaborate with banks instead of building their own from scratch.

BaaS allows third parties to tap into existing banking systems through application development interfaces (APIs) that allow communication between banks’ software and the third parties’. These open APIs expose the banks’ functionalities to anyone intending to access them, which includes independent developers, fintechs, non-financial institutions like restaurants and welfare clubs; enabling them to build their own features on top of the banks’.  

On the other hand, the Banking as a Service relationship does not always work one way, banks can also tap into the unique capabilities of fintechs. For example, remittance company TransferWise’s tech works not by sending money from one country to the next but by rerouting money from a bank account within the receipt’s country so that it doesn’t have to cross the border. This makes its international money transfer service cheaper, UK’s Monzo bank partnered with TransferWise to integrate the service into its banking app.

Furthermore, as open banking becomes industry standard, you should be able to plug and play different financial capabilities like lego pieces to birth a new service without ever having to own the infrastructure behind it. For example, to cook up a PayPal-like service, you’d just plug in mobile wallet capabilities, sprinkle in a little electronic virtual card functionality and season it with Peer to peer cross-border transfer features, ideally, BaaS should make it that easy to cook up a PayPal.

Impact of PSD2 on Banking as a Service

The European Union set 14^th September 2019 as the deadline for financial companies to comply with the Payment Service Directive II (PSD2); which forces banks with online accounts to provide access to their customers’ account information to registered third parties. However, the account holder has to give consent first.

Additionally under the PSD2, a fintech company (third-party provider) can be licensed as an  Account Information Service Provider (AISP); who is permitted to access and consolidate account information from a user’s different banks accounts, or/and as a Payments Initiation Service Provider (PISP): who can initiate a payment request from a user’s bank account at their request. This broadens the range of services they can create out of the access they receive.

How Does This Affect Banking? 

Well, just imagine your favourite bank being forced to avail information to a company that can use it to launch a competing product. A great example of such a product is Mint, a financial planning an app where you can read all your information (and make payments) from different bank accounts instead of going into each bank individually. Such a service reduces the amount of contact between banks and their customers.

According to a 2018 report by Roland Berger, banks risk losing 25-40% of their income from the disruption. Additionally, banks that previously invested little in IT infrastructure will have to ramp up their budget to avail the open APIs needed to provide customer information to third parties. 

One way for banks to tackle the revenue drop will be to embrace BaaS and avail more of their capabilities to third parties under revenue-sharing deals. In such a circumstance, PSD2 will eventually become an accelerator of Banking as a Service making it a necessity rather than an option.

BaaS in Action

Notable financial institutions embracing BaaS include US bank Bancorp, which has leveraged the BaaS model to a point of supporting 75 million prepaid cards and over 100 non-bank partners who use it to provide financial services.

Fidor, a German online bank founded in 2015 supports an open banking model (Fidor Operating System), which makes it possible for developers and other banks to use its API to create services off its core functionalities. Other banks with services running off of Fidor include mobile-native bank O2-based in Germany and Netherland’s Van Lanschot Bank. 
solarisBank, a tech company that received a German banking license in 2016 also avails banking capabilities through its suite of APIs to companies that include online SME bank Penta, Insha as well as freelancers’ banker Kontist.

Another notable mention is Mastercard’s Partner Wallet API, which allows any retailer to build upon the company’s Masterpass payment network. This feature enables merchants to bring Mastercard’s in-app and website checkout security capabilities, fraud detection and authentication to their own service.  

Hopefully, after the dust has settled on PSD2, more companies will have benefited through the Banking as a Service model rather than been disrupted.

Here is how Big Data can help you in your growth strategies in ecommerce

Ecommerce is taking a bigger slice of the global retail pie. In the US, for example, ecommerce currently accounts for approximately 10% of all retail sales, a number that’s projected to swell to nearly 18% by 2021. To a large extent, the ecommerce of today exists in the shadow of the industry’s early entrant and top player, Amazon. Financial analysts predict the retail giant will control 50% of the US’ online retail sales by as early as 2021, leaving other ecommerce stores frantically trying to take a page out of the company’s incredibly successful online retail playbook.

While it seems unlikely that another mega-retailer will rise to challenge Amazon’s ecommerce business in the near future, at least 50% of the online retail market is wide open. Smaller and niche ecommerce stores have a real chance to reach specialized audiences, create return customers, and cultivate fervent brand loyalty. Amazon may have had a first-mover advantage, but the rise in big data and the ease of access to analytics means that smaller companies can find areas in which to compete and improve margins. As e-retailers look for ways to expand revenues while remaining lean, data offers a way forward for smart scalability.

Upend Your Back-end

While data can improve ecommerce’s customer-facing interactions, it can have just as major an impact on the customer experience factors that take place off camera. Designing products that customers want, having products in stock, making sure that products ship on schedule–all these back-end operations play a part in shaping customer experience and satisfaction. In order to shift ecommerce from a product-centric to a customer-centric model, ecommerce companies need to invest in unifying customer data to inform internal processes, and provide faster, smarter services.

The field of drop shipping, for instance, is coming into its own thanks to smart data applications. Platforms like Oberlo are leveraging prescriptive analytics to enable intelligent product selection for Shopify stores, helping them curate trending inventory that sells and allowing almost anyone to create their own e-store. Just as every customer touchpoint can be enhanced with big data, ecommerce companies that apply unified big data solutions to their behind-the-scenes benefit from streamlined processes and workflow.

Moreover, ecommerce companies that harmonize data across departments can identify purchasing trends and act on real-time data to optimize inventory processes. Using centralized data warehouse software like Snowflake empowers companies to create a single version of customer truth to automate reordering points and determine what items they should be stocking in the future. Other factors, such as pricing decisions, can also be finessed using big data to generate specific prices per product that match customer expectations and subsequently sell better.

Data Transforms the Customer Experience

When it comes to how data can impact the overall customer experience, ecommerce companies don’t have to invent the wheel. There’s a plethora of research that novice and veteran data explorers can draw on when it comes to optimizing customer experiences on their websites. General findings on the time it takes for customers to form an opinion of a website, customers’ mobile experience expectations, best times to send promotional emails and many more metrics can guide designers and developers tasked with improving ecommerce site traffic.

However, ecommerce sites that are interested in more than just treading water will need to invest in more specific data tools that provide a 360-degree view of their customers. Prescriptive analytic tools like Tableau empower teams to connect the customer dots by synthesizing data across devices and platforms. Data becomes valuable as it provides insights that allow companies to make smarter decisions based on each consumer identify inbound marketing opportunities and automate recommendations and discounts based on the customer’s previous behavior.

Data can also inspire changes in a field that has always dominated the customer experience—customer support. The digital revolution has wrought substantial changes in the once sleepy field of customer service, pioneering new avenues of direct communication with agents via social media and introducing the now ubiquitous AI chatbots. In order to provide the highest levels of customer satisfaction throughout these new initiatives, customer support can utilize data to anticipate when they might need more human agents staffing social media channels or the type of AI persona that their customers want to deal with. By improving customer service with data, ecommerce companies can better the entire customer experience.

Grow with Your Data

As more and more data services migrate to the cloud, ecommerce companies have ever-expanding access to flexible data solutions that both fuel growth and scale alongside the businesses they’re helping. Without physical stores to facilitate face-to-face relationships, ecommerce companies are tasked with transforming their digital stores into online spaces that customers connect with and ultimately want to purchase from again and again.

Data holds the key to this revolution. Instead of trying to force their agenda upon customers or engage in wild speculations about customer desires, ecommerce stores can use data to craft narratives that engage customers, create a loyal brand following, and drive increasing profits. With only about 2.5% of ecommerce web visits converting to sales on average, ecommerce companies that want to stay competitive must open themselves up to big data and the growth opportunities it offers.

After retailers suffered a bad year with bankruptcies, store closures and lower store footfall, we discuss why now is the time for retailers to invest in data and advanced technologies to boost consumer relations.  

Bricks and mortar retailers would sooner forget 2018. The year that brought 16 U.S. bankruptcies, falling share prices for leading European brands, and the UK’s worst festive sales in 10 years isn’t an industry high point. But it does offer a crucial lesson for those struggling in a tough climate: the need to harness digital.

While footfall in stores continues to decline, online retail continues to thrive globally, with annual sales exceeding $2.4 trillion. Clearly, web-based buying is no fad; it’s a growing market shift retailers must embrace if they want to survive. And a key requirement to ensure effectiveness will be tailored advertising that reaches the right individuals, at the ideal moment.  

The question is: how can retailers obtain the insights they need to create relevant ad campaigns that drive digital success?

Foreseeing the unpredictable

One answer lies in leveraging precious consumer data with smart technology to make sense of shopper habits. As most retailers know, customer journeys now cover a myriad of real-world and digital channels, such as websites, apps and physical stores, as well as needs and interests. So, it can be hard to track individuals, let alone establish which ads have the best chance of achieving in-the-moment impact.

Yet the good news is that advances in machine learning (ML) have made it possible to address these issues via large-scale data collection and analysis, which provides real-time insight into shopper behaviour. In particular, more autonomous branches of ML — such as Reinforcement Learning (RL) — are using data about previous consumer activity to understand individuals and predict their likely responses to specific ads.

RL: the quick-fire essentials    

In short, RL finds the best possible action in certain contexts. The basic mechanics involve training RL algorithms to master a specific type of problem solving: artificial agents must determine the ideal decision in their current state by assessing options open to them, and the positive or negative rewards they bring. Think of it as similar to a popular maze-based arcade game; the user’s goal is collating points, but during navigation they meet ghosts (bad rewards) and power boosters (good rewards) that need to be evaluated, except the RL jackpot is a key performance indicator (KPI).

How can it improve retailer fortunes?

The key benefit of RL is also its defining characteristic: adaptability. Unlike standard ML, where agents behave according to set rules for each possible scenario, RL rewards aren’t instant and the environment shifts after every action. As a result, maze boundaries aren’t fixed and agents learn how to act for themselves: using rewards to create optimal strategies for reaching a final objective. And this makes RL a perfect foundation for real-time ad targeting in today’s dynamic retail landscape.

When the huge processing ability of RL is applied to consumer insight  — including first party data about website visits, ad exposure, past purchases, brand interactions and location — retailers can immediately pinpoint behavioural patterns that inform meaningful and effective advertising. For example, analysis might show an individual has regularly shared social video ads and bought featured products, indicating this format is the most likely to be positively received and drive a sale.

Plus, RL algorithms can be trained to meet particular KPIs when targeting specific consumers, with accuracy constantly improving as positive rewards refine strategy, ad serving frequency, and format selection. Over time the information gathered can be used to predict wider trends among your target audience helping inform advertising budgets and campaign direction.

Ultimately, RL has the potential to create a more engaging and balanced advertising experience that is better for everyone. With a greater understanding of what works for individuals, and what doesn’t, retailers can fine-tune their approach to deliver ads in the right quantity and medium, and to the most appropriate channels or devices – instead of bombarding target audiences with irrelevant and disruptive messages. And in doing so they will not only forge deeper personal bonds that drive positive brand perception and loyalty, but also ensure continued success by proving they can deliver the personalisation consumers want, only when they want it.

The digital revolution has completely changed the way we buy and rent movies. Netflix, Amazon, Apple, Google and other on-demand entertainment service providers have made the brick-and-mortar video stores irrelevant.

In July 2018, in Bend, Oregon, the last standing Blockbuster store shut its doors for good. That’s quite a comedown from a decade or so ago, when the chain boasted 9,000 locations around the world. Something similar has happened to bookstores. Though many are still standing, Book World, the No. 4 chain in the category, liquidated itself last year.

Fearing a similar fate, many of the entertainment giants are consolidating. AT&T bought Time Warner. The Murdoch family agreed to sell most of 21st Century Fox and set off a bidding war between Disney and Comcast.

While digital distribution is a major impetus for this displacement, that doesn’t explain everything. The companies replacing  video stores and bookstores — Netflix and Amazon — are also known for their effective use of data. Both use consumer data to recommend new content (and products, in Amazon’s case) to consumers. Both use streaming services to ensure a steady stream of actionable customer data.

For entertainment companies, this threat encompasses more than a shift in consumer habits. To remain viable, such entertainment providers need to cultivate and skillfully use data the same way. But how to do this?

Start streaming

This fall, Warner will launch DC Universe, a  digital subscription service for fans of DC Comics.

This is the first such blockbuster from the studio. AT&T has announced that it plans to offer digital streaming for Warner Bros.’ catalog of TV shows and movies next year. Rival Disney too declared  its plans to offer a streaming service next year.

These moves could be interpreted as an acknowledgment of the primacy of streaming. Consumers spoiled by Netflix and HBO Go want unlimited access to content on the devices of their choice.

While that’s true, there’s another plot development which can’t be ignored: the use of streaming to provide a stream of actionable data and insights about viewers.

Another innovation common amongst streaming service is content recommendation engines. Netflix has claimed their recommendation engine saves the company $1 billion per year by reducing customer churn. But recommendation engines can also be used to forecast the potential home video audience for a given property.

Use data to enhance content development

American novelist, playwright, and screenwriter William Goldman once lamented that studios passed on many films that went on to be blockbusters for other studios. That led him to declare that “nobody knows anything.”

That may have been true at one point, but streaming services now provide a wealth of actionable data. By keeping close tabs on what viewers watch and categorizing that data, streaming companies can develop “taste communities.” Such communities illustrate overlaps between actors, directors and genres that would otherwise have been obscure.

Studios can use such data to develop new content. But studios have other tools at their disposal. The most tried-and-true method of enhancing an entertainment property’s performance is to look at social media. You can start to predict how a show will do by looking at the number of followers an actor has, what they say in social media and how popular they are. This is such an established fact that as far back as 2016, directors were casting movies based on the actors’ social media following.

Other data like reviews, box office performance and marketing spends can help create models that boost performance. For instance, box office revenue and Wiki edits correlate at 87%. Box office and YouTube trailer views correlate at 78%.

Finally, there’s segmentation, which divides the potential audience into likely rabid fans, people who will never watch and people in the middle a.k.a. “persuadables”. Targeted marketing and advertising via programmatic can help reach those audiences with the right messaging.

Reality check

This vast array of data is a break from the days when studio heads had to rely largely on intuition to make decisions. However, there is an element of serendipity about entertainment that will never make forecasting 100 percent accurate. Data analytics isn’t a silver bullet, but it can identify opportunities that no one else sees.

As Ted Sarandos, Netflix’s chief content officer, has said, creative direction is still based on “believing the storyteller.” Hopefully, that approach will lead to more content that surprises and delights fans rather than make them feel like they’re being marketed to. If so, that’s the kind of Hollywood ending that will please entertainment execs and fans alike.

It’s been one month since GDPR, General Data Protection Regulation, a policy set in place to address the digital age’s ever increasing flow of personal data, went into effect for European industries. GDPR is meant to give consumers more control over their personal data usage by companies, and this shift in data control is causing a ripple in European marketing strategies. Let’s look at how GDPR is impacting marketing strategy and what that means for you as a customer.

Lesser Quantity of Data, But Targeted Audience Profiles

According to Chad Wollen, CMO of Smartpipe, higher customer control of personal information restricts access to the information marketers rely on to target audiences. Wollen further says ad tech vendors may find themselves starved of data as consumers chose to opt-out of sharing their personal information. Marketers have lesser access to information about their audiences, thereby also losing information they need to target similar types of customers. Despite the loss of larger quantities of information, the quality of information will dramatically increase due to, as comments from the director of OpenX Ryan Eney points out, businesses only collecting information they need, setting time limits on data storage, and gaining a legal basis to process consumer data. CTO of Crownpeak expands on what these processing bases are, mentioning that companies need a compelling legitimate interest to process and use data. Overall the stronger customer hold on personal data creates restrictions to marketing companies building target audience profiles, resulting in less quantity of data, but more quality information and deeper consumer trust.

Deeper Consumer Relationships

Consent policies are evolving to adapt to GDPR guidelines and consequently, companies need to make sure their customers feel comfortable giving personal data to corporations. With stricter rules set in place to protect the consumer, winning over the consumer will now be the primary aim of brands, “and they will only do this if they go beyond having a legal right to operate, ensuring their data practices are socially acceptable to”, adds Chad Wollen. Consumers need to feel as though they can trust the companies with their personal information enough to grant consent to use their information for marketing and analytics purposes. Consent policies will need to evolve to include the policy guidelines of GDPR and therefore companies will need to evolve their practices to finesse the trust and establish deeper relationships with their customers.

The Future of Consumer Data   

Moving forward, marketing and compliance strategies practiced by companies will evolve around deeper customer relationships and more in-depth consent policies. Management of consent will evolve to consider the compliance and relationships of the consumer. Without these adaptive marketing measures, it will be difficult for companies to flourish in light of GDPR. As a consumer, you should expect more transparency from corporations about your data usage and more meaningful conversations and interactions from organizations to gain your trust.

Goldman Sachs predicts that a $100+ billion market opportunity for drones will exist until 2020 – and that’s not just for military usage.

The aviation sector is clearly an attractive market capable of drawing in young entrepreneurial teams from across the world, such as the German startup AIRTEAM – an aerial intelligence platform that uses drone and satellite data in order to rapidly measure, map and inspect properties – and San Francisco-based Skycart – a German startup building the UAE‘s first ever autonomous drone delivery service.

At CUBE Tech Fair, some of the world’s most forward-thinking drone experts will present user cases. We talked with one of the experts, Yariv Bash, Co-Founder and CEO of Flytrex. Flytrex is ushering in the next generation of on-demand delivery, enabling any business, from SMBs to e-commerce giants, to integrate same-day autonomous drone delivery into their offering

But what really makes Flytrex so special? And how are drones going to develop in the near future? Maren Lesche interviewed Bash to gain some insights into his motivations and the industry as a whole.

Maren: Nice to meet you, Yariv. Tell me a bit about yourself. How did you become interested in the tech industry? Did you have an idol you looked up to?

Yariv Bash: My grandfather played a crucial role in my interest in technology and aviation. When I was a child, he would buy me do-it-yourself engineering kits, and we would sit together for hours assembling engines and working on projects. He instilled a love of technology and engineering in me and played a crucial role in my career path.

How did you come up with the idea for Flytrex?

Using our respective expertise, Amit, my co-founder, and I actually initially began selling “black boxes” for drones – similar to the black boxes in commercial airlines. These boxes provided real-time geo-trafficking and flight path sharing. When we realized that drones are going to be the future of on-demand delivery, with consumer expectations for faster and faster deliveries constantly rising, we saw an opportunity. Drones could offer the perfect solution. With this in mind, we shifted direction and began focusing on developing the ideal autonomous drone delivery service that would enable any retailer to provide on-demand deliveries and compete with the e-commerce giants.

Why drones? What makes them innovative?

Widespread commercial drone use was once considered science fiction. However, as technology has improved, we have pushed the boundaries of what is perceived as possible. Drones are set to become a core component of countless industries.

In terms of on-demand delivery, drones are able to tackle several challenges simultaneously. Drones are faster and can facilitate same-hour delivery: consumers can receive items in a matter of minutes. In addition to drastically cutting delivery times, they also slash costs, as they are far more efficient to operate than delivery bikes or other last-mile services.

Moreover, as drones are 100% electric, they substantially reduce emissions. And if that isn’t enough, they are also far safer than traditional delivery options – reducing road congestion without being dependent on error-prone drivers. Our advanced navigation systems mean drones can maneuver around large obstacles such as cityscapes. As the tech improves, drone safety will also improve.

What does a drone delivery look like today?

Drone delivery today is just beginning. There are specific regions that have approved autonomous drone deliveries, and these projects are vital in demonstrating the widespread viability of commercial drones – from a regulatory perspective as well as acceptance by the general public.

In Reykjavik, Iceland, our drones fly at roughly 70 meters in the air, meaning they are out of sight to any pedestrian, and don’t add to noise pollution. The drone system allows direct delivery between two parts of the city separated by a large river, saving energy and human resources normally allocated to the circuitous ground route over a river bridge located in the south of the city.

Our system requires an employee of AHA – our partner in Iceland and a local leader in e-commerce – to simply load a package onto a drone and click a button on the Flytrex platform. The drone automatically flies off to a designated landing area close to customers. Under the current model, another AHA staff member collects the package from the drone at the gathering point, and delivers it to the customer in that neighborhood. Flytrex is improving upon this system daily and has plans to begin lowering packages via a cable directly to consumers’ backyards, making drone deliveries even more efficient. In this
model, Flytrex’s user-friendly software allows customers to track their delivery drone through an easy-to-use app.

What are some of the challenges the drone industry is expecting to face in the future?

The primary challenge is proving to regulators that widespread adoption of drone deliveries is possible. This challenge, however, also presents an important opportunity: ultimately, it will be the regulators’ stamp of approval that will help usher in the era of commercial drones.

That said, general awareness poses another challenge. People have an inherent fear of the unknown, and drones represent something that has never been done before. Getting society to accept drones as a viable form of delivery will be key to ensuring their broader implementation.

Managing drone traffic will also be an obstacle. As commercial drones become more prominent, an important milestone will be a universal air traffic management system that will keep track of all the drones in the air, and ensure they are on-course and functioning correctly.

If you could give one piece of advice to other startups, what would that be?

The sky is not the limit – think big, validate your idea, and push forward like crazy.

Thank you for a great interview Yariv!

You can hear Yariv speak at the 11:15 am panel on May 16th on the Cube Stage together with Mohammed Johmani, CEO of SPACE and Kay Wackwitz, CEO of Drone Industry Insights. If you want to attend, please use the code CTFVIP2018 for free access to the conference.

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At some point, almost every company faces questions like

• How good are the customers that we acquire?
• How do they differ from each other?
• How much can we spend to encourage their first or next transaction?

As a measure that determines the amount of profit a customer brings over the “lifetime” of engagement with the business, the customer lifetime value provides valuable insights on all of these questions.

When we analyze how generated contribution margin is distributed among the customer base, in most cases, there is a very small group of customers who are responsible for most of it. Sometimes it´s 1% of the customers generating over 10% of contribution margin. Imagine, if we knew how to acquire only those customers? Only 10% of our customer base would be needed to generate the same amount of contribution margin.

Knowing which customers to acquire and keep, and more importantly at which costs, grants a company a sufficient competitive advantage.

This means that CLV is shifting the way success is measured from evaluating a single transaction to making an overall assessment of the value of the entire relationship with your customer.

The customer lifetime value consists of two components: historical and predicted. The historical CLV is based on previous orders of a customer, while the predicted CLV is the expected future value of the customer. Since over 40% of businesses admit to struggle with any CLV calculation, we will focus here on setting a solid base for the historical CLV first. This basis can then be utilized for later predictions.

Who is it relevant for?

 Customer Lifetime Value is a key metric for any business that implies multiple and somehow frequent transactions with the same customer.

Within those companies there are multiple stakeholders that can benefit from having a Customer Lifetime Value on customer level:

• CRM managers to improve customer segmentation
• CMOs to evaluate the effectiveness of their marketing mix and budget allocation
• Management to analyze cohorts’ development
• Marketing managers to draw conclusions from demographics and the behavior of high CLV segments, as well as to understand how much money they can spend on acquiring users (as opposed to blended CACs)

What do you need to calculate historical CLV?

 As mentioned above, CLV is the cumulated profit margin of a customer (historical or expected). Why profit margin and not revenue? Simply because revenue doesn’t provide much insight on how well the unit economics is working. Some products yield low margins and we aim to identify the most profitable customers. However, as getting profit margin data per order can be challenging, calculating CLV based on revenue is also a good first step.

So, to calculate CLV for any given customer one would need to subtract all the variable costs associated with the transactions from the revenue made by it. Meaning: returns, cancellations, costs of goods sold, payment costs, shipping costs, vouchers etc. The bottom line is the value we’re looking for – and would be working with.

In the next step, we need to get all the marketing costs spent on the customer, from variable ones like AdWords and Facebook, to fix ones like cost of partnerships and collaborations. These costs then need to be distributed back to the traffic that has been generated.

For that, we need session data and a clear split between the costs of acquisition and reactivation. This split should be defined depending on the business model and acquisition goals. For e-commerce, it generally could be the first order of a customer, meaning all touch points prior to first order belong to the acquisition part of the journey, and the ones after it belong to the reactivation or retention part.
Keep in mind: the costs spent to acquire or reactivate one customer are not just the costs that successfully converted him, but also the costs of touch points from those campaigns that didn’t end in a conversion.

Since we are interested in the evaluation of individual customers and later on the prediction, we need to perform these calculations on the level of a single customer.

However, we need to differentiate between all-time CLV of a customer and CLV during a specific timeframe, like during the 30-90-180 days since the first order or initial registration. While the all-time CLV of a customer potentially is based on more data, having CLV data per time span allows us to compare the performance of different cohorts within the same durations, and reveals more about the customer base development over time.

Once this is in place, we are able to analyze the cost and value development overall as well as for particular cohorts.

What to do with the findings?

 Now, imagine that we know that the CLV for a specific customer goes up to 1.000€ within one year. What does it tell us and how can we leverage this knowledge?  First, we need to put it into the context of marketing costs and further customer attributes, to be able to act on it. Combining the CLV progression over time with the marketing costs is a good start for understanding how soon after an acquisition we will break-even with this customer or customer group.

Well, if you’re done with this, it’s already a good start, but don’t stop here: Go on with segmenting your customer data to get real actionable insights. Here are some practical use cases of Customer Lifetime Value application:

• Are we getting better at acquiring and engaging the right customers?Compare how well differently aged cohorts perform in a certain time span. Choose a duration that is relevant for your particular business model, e.g.: are customers acquired in October’17 generating more profit margin in the first 30 days than customers acquired in September’17?
• What products attract the best customers?How profitable are the customers when broken down by the product category of the first order? Can similar results be seen when analyzing what kind of customers are acquired with different SEA campaigns or keywords?
• How the CRM approach be customized for different customer types?
  For example: low CLV segments can be addressed via email, medium CLV segments will see a Facebook ad in their feed, while high CLV segments would find a personalized card in their mailbox.

When you figure out how to calculate and analyze the CLV of your customers, you lay a great foundation for your company to focus on getting the customers that have a positive impact on your bottom line. Drill them down by acquisition cohort, campaign, geography, first product category and customer segments in order to make the most out of your newly earned knowledge. In other words: eliminate the average whenever possible!

Find out more about data-driven marketing in our Project A Blog.

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The sudden evolution of the phone from a single-function device for making calls to a multifunctional pocket computer is one that’s already changed how we conduct business and live our lives a great deal. Now more than ever before, however, our behavior is also influencing this evolution to create products and services that respond to our unique needs.  As the computing power of our handheld windows to the world continues to increase, so to will our ability to instruct them to do what we need them to do. This is where business leaders like Predict.io co-founder Silvan Rath see new opportunities for phones to streamline an array of daily tasks for consumers and provide better insights for businesses. Here are some of his thoughts about phone sensors, the retail market, and what lies ahead in an era of ever more personalized computers.

What was your motivation to start Predict.io? Have things unfolded as you expected?

The market has a way of breaking things that don’t change. So we did. We initially started with a SaaS model that helps cities and transport vendors to understand mobility behavior. But then retailers, QSRs and other verticals started picking up on the value that sits in location data. Hence, we started offering what they need. What they needed was an integration-free means to target customers of their competition.

Explain the advantages of ‘hyperlocal targeting’ when it comes to retail.

Any business with brick & mortar stores desperately needs to understand the offline behavior of consumers. We enable clients to not only get information about what users do when they are online, but about all of their favorite physical stores. There is tremendous value in being able to target your competitor’s clientele.

What is one disadvantage?

Our technology is the adtech equivalent of a laser beam. You can shine very brightly into extremely dense segments. On the downside, this doesn’t give you the reach of a torch – which would be the adtech equivalent of classical segmentation. Currently, the entire market is looking to find methods that convert as well as find methods for retargeting. Targeting a competitor’s visitors can deliver the desired results for many smart retailers.

Where do you see areas of untapped potential when it comes to utilizing smartphones as tools?

The device manufacturers are currently investing heavily in on-device hardware. One example includes specialized chipsets that can run Machine Learning processes very efficiently. This is the gateway to the very near future of hyper-personalizationion. We will see a wave of companies going extinct because they misssed the chance to invest in personalization. I was born in 1980. And even I have no tolerance for untargeted ads – be it online or by post. Imagine the latest generation growing up with voice assistants which will understand all your preferences.

What do you ultimately hope to provide for an end user when you develop these new sensor-based tools?

There are many benefits of personalization beyond targeted advertising. Why do I need to buy a train ticket at the train station? Your phone already knows where you went. Why would I need to build a list of bookmarks of my favorite restaurants? My phone already knows where I regularly go. Why do I need to switch on the light every time I enter a room? The room knows I am there. The list goes on and on…

How does work on hospitality tools differ from developing a tool for banking and insurance?

The underlying challenge is the same. You need to deeply understand your customer or prospect. You need to be non-invasive but helpful. The data points that matter to each industry, however, differ widely. A restaurant would be very interested in what other places you frequent, whereas a bank would like to know if your credit card and your phone are located in the same place in order to help prevent fraud. Throughout all of these processes, we also find it important to regulate what we monitor. We are not pulling up insurance data for risk analysis. We also don’t pursue details like sexual orientation, race and other deeply personal data that could be inferred from location information.We feel it is not fair game to use location data for such purposes. The new European GDPR does provide boundaries in this area, but it doesn’t regulate everything.  That’s why we also self-regulate.

As more and more devices and digital tools do and decide things for us automatically, what types of actions do you believe people will want to continue to do, even if they theoretically could be automated?

Let’s talk about cars for a second. I don’t want to drive in traffic. But I do want to drive on a scenic road. Also, in home automation, current types automation are far from fool proof. Personally, I think there should always be a choice. The same way I can choose to cook or go out for dinner. Automation, after all, is a service.

As a research scientist at the German online retail giant Zalando, Dr. Alan Akbik is an expert in Natural Language Processing and Data Extraction. In his work for the company, which at any given moment is handling massive numbers of online transactions in multiple languages, Akbik helps unveil unique insights into the very structure of human language by observing and analyzing huge sets of multilingual text data. Here’s what he had to say about the possibilities for both business and the study of language that NLP is bringing online.

What first inspired you to pursue a career as a data scientist?

My love for human languages! In a sense, all of humankind’s knowledge is stored in written language in books, the Web, and elsewhere. Our hope is that data science – and in particular natural language processing (NLP) – can help computers and us make sense of all this textual data.

What’s a particular pattern or insight that you have uncovered while at Zalando that you think could also help companies working outside of the retail sector?

Leverage your textual data! I think many companies might be surprised by how much textual data they have available, and how much value they can get out of it.

Zalando Official Logo

At the moment, what new technology has you the most excited by its capabilities?

We are currently working with recurrent neural networks (RNNs) of all flavors that have me very excited for their language modeling and sequence labeling capabilities. I believe these techniques may – in the near future – lead to important breakthroughs in modeling and automatically capturing semantics in human language.  

What tools are you most heavily relying on in your day to day work? How do you make sense of multilingual data?

We are researching a technique called “annotation projection” that can automatically transfer NLP methods that work for one language (such as English) to another (such as German). This helps us immediately scale our NLP across the many European languages relevant for us and our customers. We even released an open source framework for this technique, called ZAP. Do try it out!

How has your time spent getting familiar with Information Extraction made you a more effective data scientist?  What skill or field of knowledge would you like to augment it with?

Information Extraction (IE) is a core task of extracting structured information from text data and therefore hugely important for data science that involves such data. I am interested in databases, machine learning and computational linguistics, because they are important fields of knowledge for IE.

What is something you know about customer behavior or the way we use language that you didn’t know before working at Zalando? Has anything surprised you?

I am (continuously) surprised by the many particularities of informal language usage on the Web, especially in the domain of fashion where new words (for trends, looks etc.) are invented seemingly every day. It shows well the creativity and enthusiasm of the fashion community and presents us with interesting research challenges for NLP and data science.

To learn more about Data Science and Zalando, get your Data Natives ticket here.

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This article was originally published on cmo.com

Every decade or so, a new, game-changing technology platform changes the way the world works. From the desktop, to mobile, and to the cloud, the landscape continues to advance. And now? We are knee-deep in the artificial intelligence (AI) revolution—one so big that it has been compared to the invention of electricity.

AI has the potential to power future innovation, especially in terms of customer experiences and the way we do our jobs.

“I like to take the term AI and flip it around so it’s IA, or intelligent assistant,” said John Bates, Adobe’s senior product manager for data science and predictive marketing solutions.

Framed that way, AI can be thought of more like a utility than a technology. Either way, now is the time to experiment and play. Start with a business problem or an unused data set, and then think about how AI could help create experiences that were never imaginable before.

The following brands did exactly that, discovering three ways AI can open the door to innovation.

1. Efficiency: Adobe Takes On The Classics
   Last year, Adobe partnered with Goodby Silverstein & Partners/MediaMonks and challenged four digital artists to re-create lost or stolen masterpieces using Adobe Stock and Photoshop:

• Indian artist Ankur Patar worked on “The Storm on the Sea of Galilee,” by Rembrandt van Rijn.
• S.-based artist Mike Campau reimagined “Cathedral Towering Over a Town,” by Karl Friedrich Schinkel.
• Ecuadorian artist Karla Cordova took on “The Wounded Table,” by Frida Kahlo.
• French artist Jean-Charles Debroize tackled “Saint Matthew and the Angel,” by Caravaggio.

Adobe Sensei’s AI and machine-learning capabilities power content understanding, search and discovery, and computational creativity in Adobe’s creative solutions. This content understanding, for example, helped the digital artists more easily find images, aesthetics, styles, faces, colors, and foregrounds/backgrounds that matched the original masterpieces.

The photo-editing process also benefitted from Photoshop’s understanding of objects and actions in pictures. For example, a face-awareness feature enabled the artists to change a smile to a frown without distorting a photo. Additional features, such as auto-fixing and editing (think: automatic red-eye detection), also optimized their workflows, as did the auto-curation of images in Adobe Stock.

2. Efficacy: A More Interactive USPS
   Efficacy comes into play in terms of how AI can enhance ideation, campaigns, services, bots, applications, attribution, and delivery.

A good example is the U.S. Postal Service, which demonstrated its interactive mailbox, the Smart Blue Box, at this year’s Consumer Electronics Show. The voice-activated mailbox uses AI to answer consumers’ questions, such as “How much will it cost to ship this package?” The package is weighed on the spot and shipped off in half the time, according to the USPS.

The Smart Blue Box serves as a great example of how AI is becoming a utilitarian product rather than just a marketing campaign.

Empowerment: Ada Puts Users In Control Of Their Health

3. To accomplish this, Ada spent five years building a database of diseases and their associated symptoms. Its app features a chatbot interface, where a person inputs his symptoms. Then through a series of questions, the app returns the probability of which disease or ailment the person might have. It also suggests whether medical attention should be sought or whether symptoms can be treated at home.

The app aims to empower patients to make more informed decisions about their health, the company’s founders said. The company now has plans for a new AI-enhanced software, dubbed “Ada2020” and funded by the European Commission, that will provide support for medical professionals during the diagnosis process.

For more insights like these, get your Data Natives ticket here.

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In a world of wide-ranging big data, analytics that have advanced so much that they can even help scouting for the NBA, and with several high-profile data breaches in the UK and the US making worldwide headlines in 2017, it is clearer than ever before that big businesses needs to embrace an intelligent future when it comes to the online world, putting security at the forefront of this approach to ensure that they can benefit from everything good that data and analytics can bring us, in the safest way possible.

Many major companies in various sectors have looked to ensure they are defending themselves against cryptoworms and big data breaches of any kind, and others have tried to make the world of online transactions safer and faster by accepting forward-thinking currencies like Bitcoin, and the world of iGaming is perhaps the best example of an industry where security has proven absolutely vital for business success.

A Competitive World

The continued growth of iGaming is staggering, and only set to continue in earnest now that legislation has relaxed rules on gambling in Japan, something that could help to further boost the projected revenues of $66.59 billion dollars globally by 2020 and reinforce the importance of the industry to the global economy. Of course, in an industry like this, the companies involved are well aware that they not only need to stay hugely competitive in a cutthroat industry when it comes to their business plans, but also to balance staying up to date with the latest technology to make sure customers can play poker games with the best technological advances like virtual reality, with doing so safely and securely.

It is this balance that is essential for iGaming success. Let’s look at it from a consumer point of view. If you consider the main appeal of real-money poker aside from the game itself, it is the promise of how securely your money will be treated if you choose to play online with a prominent brand. If you take a look at that example from 888poker, you’ll see that the opening intro on the page discusses how to stay “safe and secure” whilst playing online, before any mention of the actual games’ appeal.

The Future is Secure (or it Needs to Be)

Safety and security are clearly going to become the underlying trend of iGaming for the future, perhaps even more so than other trends inherent in iGaming, such as the huge growth of mobile gaming or jumps in development when it comes to virtual reality and live casino gaming.

If brands fail to take heed of the importance of online security and suffer a big data breach or a cyber attack that allows access to customers’ funds, then it is likely that it could be the end for that particular company as a force. As hackers continue to grow more and more sophisticated, the future of iGaming is not just about glitz and glamour, but also about the fundamental security of gamers’ money.

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The creation and consumption of data continues to rapidly grow around the globe with large investment in big data analytics hardware, software, and services. The availability of large data sets is one of the core reasons that Deep Learning, a sub-set of artificial intelligence (AI), has recently emerged as the hottest tech trend. Huge giants such Google, Facebook, Baidu, Amazon, IBM, Intel, and Microsoft are heavily investing in big data, with the acquisition of talent hot on their agenda.

Big data is continuously creating new challenges and opportunities, all of which have been forged by the information revolution. This infographic takes a look at how those in the ecommerce industry are already using data sets to introduce a new level of strategic marketing and provide better customer service experiences.

Predicting trends, optimising pricing and forecasting demand, are just some of the ways that ecommerce businesses are using data to gain a competitive advantage. The guesswork has been removed, and now ecommerce businesses can accurately make strategic decisions on how to operate their online empires.

Big data is proving to be a game-changer when it comes to retail and ecommerce. If businesses can successfully implement effective big data strategies then they will reap the rewards of better customer experiences and bigger profits. This infographic explores practical ways to introduce data solutions with simple implementation.

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As your business succeeds, there will come a point when you have to expand your market. A research by Accenture predicts that B2C ecommerce will reach $3.4 trillion globally as more people around the globe prefer purchasing online. Forrester also expects more B2B purchases to shift online as well. Because of this, you may consider going cross-border in your region or even global to grow sales for your standout product or service.

However, before taking this big step, you need to make improvements to your ecommerce platform and even your business processes to ensure that everything works for your new markets. Aside from figuring out logistics, pricing, and localization, you should also be reviewing the payment methods that you offer. Payment methods are critical to getting cross-border ecommerce to work.

Preferred Payment Methods Vary

Much like there is no universal currency, there is no definitive payment method that is accepted by all consumers and merchants worldwide. Each region or country would have their own preferred payment method.

In the US, cards dominate payments. Despite the rise of payment service providers (PSPs) such as PayPal, digital wallets such as Apple Pay and Android Pay, and even brand loyalty cards, most of these services still require the use of credit or debit cards as funding sources. What these technologies essentially provide is a layer of convenience for users so they don’t have to manually input their credentials to pay for online purchases.

European countries are leading the world in going cashless. Take Sweden’s case for example. Due to cooperation between their banks and an evolving financial technology scene, the Swedish can use their bank accounts for most financial concerns such as receiving salaries, savings, loan applications, and payments. This focus on bank accounts is shared by other European countries.

“The use of online banking is on the rise throughout Europe, due to the increasing convenience and simplicity for consumers to access their bank accounts via the online channel. As a consumer the online bank account is your primary funding source, your true digital wallet,” said Johan Nord, Chief Commercial Offic of Trustly, a Swedish Payment Services Provider (PSP), commenting on Europeans’ money habits.

Lack of Payment Methods Leads to Lost Sales

Nearly half of online shopping carts are abandoned at the payment stage. According to a Baymard study, among the reasons for abandonment isnot having enough payment methods available.

In less developed countries such as some in Asia, cash remains king. Despite having high rates of technology adoption, countries such as the India continue to be cash dependent. Banking and card penetration remains low so even with online purchases, merchants are compelled to provide cash-on-delivery (COD) payment options.

Amazon, which generally accepts credit cards and even has its own gift card in the US, had to accommodate cash-on-delivery as means of fulfillment when it expanded to India. A staggering 83 percent of Amazon India transactions are COD which represents a significant part of market Amazon could have lost if COD wasn’t available. Other ecommerce players Flipkart and Snapdeal also had to do the same.

PSPs Enable Cross-border Payments

Clearly, failing to cater to the generally accepted payment method in a particular market can be detrimental to your cross-border efforts. However, even payment processing can be complicated depending on the market’s preferred payment method. This is where PSPs can help out.

Trustly rides on Europe’s debit-centric mindset to enable merchants to accept payments through customers’ bank accounts for online transactions. This financial mindset is prevalent in Europe, allowing Trustly to expand their presence to 29 other European countries.

Elsewhere, a huge part of the appeal of partnering with processors like PayPal is that they provide means of accepting credit card payments. Enabling your ecommerce site to directly accept card payments requires considerable effort to comply with Payment Card Industry (PCI) standards. Using payment processors, you can readily accept payment from major card companies. In some jurisdictions, PayPal can also draw from bank accounts as funding source.

PSPs also simplify administration. Without a provider, you may be looking at directly creating agreements with banks and other entities in order to handle payments from abroad. Since they handle all the integrations with banks and card companies, you only have to deal with them in order to be able to accept these payment options.

Security and Chargebacks Are Concerns

But aside from allowing a variety of funding sources, the providers can also offer security for you and your customers.

Security continues to be a growing concern in today’s ecommerce. Because of the amount of customer information contained in ecommerce sites, they become prime targets for data breach attacks. By using a PSP, you can add the layer of security for your service.

Chargebacks and fraud become even more pressing concerns in cross-border ecommerce, both of which are risks when you accept cards for payments. Fraudsters particularly prey on retail since they effectively get their score through the value of the item they buy using stolen credit card numbers. In cases of chargebacks, merchants risk losing not only the cost of the item but also shipping and other logistics costs as well.

Among the appeals of the use bank accounts and fund transfers is that, unlike credit cards, chargebacks aren’t a concern with fund transfer. It also uses two-factor authentication and uses the bank’s system to verify purchases to ensure that the payment attempts are legitimate.

PSP Choice is Crucial

Going cross-border requires much preparation. As money habits vary per region, making sure that your ecommerce platform accommodates a wide range of payment options is key to serving these different markets. It is important to partner with PSPs that not only allow you to accept payment methods preferred by your new prospects but also has features that simplify the process. By avoiding the worry of payments, you can focus on working on other things that matter such as localization and pricing strategies. If all things go well, expanding your market would only help your company grow.

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Smartphones have made it possible for businesses to monitor you at all times. Take a company like Google for example. You may look up the name of a restaurant over Google Search, turn on your navigation to the destination using Google Maps and perhaps also check for weather and traffic updates along the way. The amount of information you provide to Google here is pretty exhaustive and is a treasure trove in the hands of a data analyst.

Privacy advocates may have their reasons to be concerned about users providing commercial entities like Google access to so much information. This article is however all about the different ways these tech businesses are transforming these billions of data points into something extremely useful and possibly revolutionary in the area of predictive data analytics.

Google Play Music

Music apps have been making use of historical data to recommend new playlists for a long time now. But Google is doing something extraordinary with their new Play Music. Recently, the company launched a revamped Play Music that will make use of dozens of data sources to recommend music more accurately than any other product out there. The main source of information is of course the music you have listened to before. But that is not all. Google now uses a host of other factors influencing your music preferences. For instance, you could pick classical music at work, peppy songs during your gym session and perhaps romantic songs while you travel. Google’s machine learning algorithm now intrapolates your music preference with other factors like location(at work or at gym, for example), weather (raining or sunny) and even other details pulled from your email or calendar to find the perfect playlist recommendation for you.

Uber Restaurant Guide

As a service that, among other things, transports people to and from restaurants, Uber has pretty valuable data points that can tell a user what restaurants its customers prefer to visit in any given location. Uber is now coming up with a restaurant guide that uses this data along with other real-time information about the number of drop-offs, the type of vehicle used and trending locations to prepare its restaurant guide. The number of drop-offs could perhaps tell you about the popularity as well as waiting times, type of vehicle could be an indicator of how upscale the restaurant is, and trending locations could be used to recommend restaurants to users who do not have any specific destination in mind. As of now, the Uber restaurant guide is only operational in twelve cities across the US although this is likely to go up in future.

Apple’s Siri Experiment

If there is one product that has brought machine learning to the mainstream, it is perhaps Siri. The voice assistant on the iPhone makes use of deep learning (which is a tad different from traditional machine learning) for speech recognition, natural language understanding, execution and voice response. Ever since it was incorporated into the iPhone, the software has undergone a sea change and uses machine learning incorporated through deep neural networks, convolutional neural networks, long short-term memory units, gated recurrent units and n-grams to cut down its error rate by a factor of two. Besides Siri itself, Apple also has ingrained machine learning into all of its products right from showing reminders for appointments you never got around to entering on your calendar, showing map locations of hotels even before you type it in and also detecting fraud on the Apple Store.

Facebook FBLearner Flow

The amount of data stored and processed on Facebook is humongous. The earliest users of Facebook today have over ten years of photos and videos stored on their timeline which needs to be pulled up anytime it is requested. Now take into account the over billion monthly active users and the sheer scale of the challenge becomes apparent. Last year, the company made its AI backbone called the FBLearner Flow available company wide. This platform is what controls every minute aspect of machine learning and AI within Facebook’s many products. Aside from plainly obvious features like deciding the right kind of content and friends to show on the timeline, FBLearner Flow also includes models for many intricate machine learning programs. For instance, one model helps Facebook provide auto-captioning of videos to its advertisers. Studies have shown that captioned videos bring about higher engagement levels than regular videos and can boost viewing time by as much as 40%. Quite evidently, such machine learning scripts are critical in bringing more advertising revenue. Such internal machine learning models have also helped Facebook reduce its reliance on third party tools to translate the nearly two billion news feed items each day (for which Facebook used Microsoft Bing’s Translation tools earlier).

Most of these machine learning innovations are not immediately evident to a layman user and pass off as a small addition towards better user experience. But in each of these instances, the companies have to deal with millions, if not billions, of data points to analyze, execute, test and relearn concepts. It will be interesting to see where these various experiments lead us to over the next decade.

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Since January 2015 Christian Bubenheim has been Senior Vice President Marketing & Product of AutoScout24. Before that he was part of the management team of Amazon Deutschland GmbH and responsible for the business unit „Consumables“ including health & beauty as well as foods. From 2003 to 2008 he was General Manager for the worldwide end consumer business and the product marketing of Thales Magellan, a worldwide leading manufacturer of GPS devices.

Tell us about AutoScout24 and their mission.

AutoScout24 is all about empowerment. We put people center stage. Our mission is to empower and support users in successfully buying and selling cars.

Today, people are deciding and acting more independently and on their own terms: They research online for the information they need in order to make the best decision. This is true, of course, for buying and selling a car as well. Being online on mobile devices lifts any time- and location-related constraints. No matter how and where people get online: We provide all relevant information and the online tools needed for taking the right decision and going through with it. We draw on sixteen years of experience and the data we collected throughout that time. This data signifies a great and profound knowledge that we make available to our users.

You mentioned you use a data driven approach in everything you do, can you expand on this? Does AutoScout24 have a particular model?

We use data to empower our users. Our tool for car valuation, for example, is a data-driven product. Many (private) sellers are insecure when it comes to setting the price for their used car. The car valuation tool analyzes historical and current data, compares the seller’s car with existing car ads on our platform and returns a price recommendation. The entire tool is based on a statistical model which allows us to process and evaluate ten years of historical data and 50 million different car prices – we combine our expertise with the market data supplied by our platform to support sellers in determining a realistic price for their car.

Our data collection empowers our partners in advertising as well. Scout24Media offers cutting-edge, data-driven advertising products such as real-time advertising (RTA) or targeting according to browsing behavior/interests, We also implement cross-marketplace analyses in order to benefit from the numerous positive synergies between the markets for real estate and cars: 30% of AutoScout24 users are interested in real estate objects and 43% of ImmobilienScout24 users intend to buy or/and sell a car. This significant overlap between the users’ interests allows us to provide customized solutions with regard to both market places. Simultaneously, we enable companies to implement target-group-related advertising and we support them in the acquisition of new customers – and we offer a consumer basis of about 17 million users per month.

And we empower our employees. We are data-driven to the bone and our analytics are deeply woven in our everyday working process. We developed our own tools for our teams in the product, sales and marketing departments. This allows us to evaluate data, draw lessons from that evaluation and develop new products and marketing schemes based on those lessons. Our success in reaching our defined performance indicators can be assessed by data evaluation as well. This agile and data-driven approach guarantees continuous improvement of our products and campaigns.

What kinds of questions do you need to ask your customers to find the right data?

Scout24 online portals offer a broad-range database with more than 400 defined parameters for automobile, real estate and finance. The challenge with putting this data to use is to ensure it always effectively supports our users and customers. Combining data from different marketplaces against the backdrop of this challenge delivers even better results. We use qualitative and quantitative results from market research as well as behavior-based user data and market data (e.g. supply and demand).

What is the most interesting data you have come across? Why?

The market data we collect are really exciting, because of the fact that we can leverage synergies with the real estate market. We are Europe’s largest online automotive marketplace with more than 2.4 million cars on offer. This provides us with manufacturer-independent, cross-border insights about the car market.

What new technologies or Data Science applications are you integrating into the business?

Currently, we are moving to the Cloud. We develop new data applications and establish one centralized hub for the data collected from our marketplaces AutoScout24, ImmobilienScout24 and FinanceScout24. This allows us to develop improved data functions and to generate optimized information – all tailored to the needs of our users. For example: Things change radically when people are having a baby. The apartment and the car might be too small for this new situation in life. In those life-changing moments, we can provide easy and stress-free support for our users to make the right decisions.

Do you think the smart car/driverless car trends will impact your business?

We are, of course, closely monitoring but also actively shaping the developments on the car market. Simultaneously, we constantly focus on the needs of our clients, ready to react as soon as they are open to something new. We believe the market still needs a little more time to get ready for autonomous driving. Still, we are testing various approaches of how to integrate data from Connected Cars into our business model. In contrast to manufacturers that are rather hardware-driven, however, we pursue an approach that can be applied to all car makes and models.

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When it comes to collecting customer data, the games industry is in a unique and enviable position. Unlike other sectors where customer data is often incomplete, games makers and marketers can access a wealth of live data about every single one of their players.
But it wasn’t always so.

Games used to be sold almost exclusively in boxes, and companies, up until very recently, knew little about their players beyond what market research revealed. While it’s not surprising that mobile has heralded a big shift in games, what sets the industry apart from others in the mobile space is how it’s leading data innovation. And this all boils down to the way we now play mobile games.

A change in how we play games

The games industry knew it had to re-think things when the free-to-play model exploded onto our handsets and started picking up billions of new players. While on paper free-to-play may have seemed like a winning formula, the truth was that the majority of games suffered from poor engagement. Users weren’t getting a good enough experience in their first session, so they simply weren’t returning to the game again. If players didn’t stick around, the game made no money.

And this problem wasn’t just the misfortune of bad games. Even now, the most popular free-to-play games see Day One Retention rates sitting between 20 and 40 percent. The fact is, with no upfront payment necessary, players have no commitment to persevere with a game that doesn’t engage them. So, the onus is put firmly on the developer to understand player behaviour and use that data to create gaming experiences that maximise satisfaction.

Developers and marketers had to balance game experience with the requisite monetization mechanics, such as in-game advertising and in-app purchases (IAP), but doing this with big data analytics came with it’s own set of problems.

Data for data’s sake

Big data has made it possible to record a wealth of data. But with so much of it available, developers run the risk of recording ‘data for data’s sake’. So, driven by a market in which it’s increasingly hard to retain players long enough to monetize, what we are starting to see instead is the focus shifting toward the right data to generate actionable insights.

Instead of analyzing hordes of data, a range of tools have been developed to do this work, focusing only on metrics needed to improve games. This is being hailed as the ‘deep data’ approach. It makes the kind of analysis that used to be available solely to companies with the resource to hire professional teams, accessible even to small indie developers. And, in a hold-up lesson of innovation, they are using it to augment their games.

Analytics 3.0: Deep data is taking over

With the emergence of deep data, highly accurate game personalization is an effective option, and we’re seeing more and more developers using it. By segmenting players based on their behaviour, games are being adapted and augmented to suit their style. Not only can this be used to change the gameplay, it can completely change the method of monetizing each (and every) player.

The evolution of analytics in gaming

Over the last few years, there has been a significant shift in the games industry’s use of analytics. This evolution can be tracked across three distinct phases:

Analytics 1.0 – This was focused solely on game performance; dashboard reporting of what had happened in the game, but without providing the clarity that would enable developers to know where any issues may lie, or how to solve them.

Analytics 2.0 – This phase was about changing the game at the design level. Developers could see where the problems were, but could only implement broad-brush and one-size-fits-all changes to the game.

Analytics 3.0 – The most current approach. Deep Data – the combination of a large number of data points, incredibly fast database technology and multiple data sources – enables the gaming experience to be personalized for individual players within segments, based on their engagement and playing style.

Deep data is the future

Using deep data analytics tools to personalize player experiences in games not only improves monetization, it improves the gameplay for the whole audience. It’s a win-win scenario. In a recent deltaDNA survey of in-game advertising, 50% of games with 100k+ DAU (daily active users) said they provided different experiences to different non-paying players.

Deep data is not only a move towards efficiency and effectiveness, it signifies the democratization of analytics. Expect to see more and more developers adopting this approach in 2016, as other industries start to follow suite.

featured image credit: Playbuzz

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The Henn-na Hotel in Japan just got more futuristic. Walking up to the front desk, customers are greeted with the familiar bow and the typical “Welcome” spiel from a typical Japanese woman. The catch: she’s a robot. Henn-na means either “flower” or “it’s weird,” depending on your interpretation. This new development, however, isn’t so surprising. Service bots and AI are used to help customers in retail stores like Lowe’s. Why shouldn’t they help guests check into a room? Though the integration of AI will continue to grow, it is not quite what people might expect. Furthermore, hospitality experts and engineers do recognize that customer satisfaction is key. To move forward with service bots and robots, hotels need to convince their guests it’s a good idea. So how does the future look?

The future is, no doubt, full of robots. When guests of the Henn-na Hotel enter the lobby, they are greeted by several receptionist robots, including my favorite—a velociraptor in a hat. These bots check guests in and send them off to their rooms. Human staff is always on call, but the company intends to make 90% of its operations automated. From porters to cleaners to whatever else Japan may think of, the hospitality industry is, in fact, already overrun with bots. The Henn-na Hotel, however, also features many other kinds of AI. Guests can open their door simply by using facial recognition software. They can use verbal cues to turn on lights.

Similarly, Carnegie Mellon has a Social Robots Project, which includes Roboceptioncist, “Tank.” He stands ready to greet guests and provide helpful information. It sounds pretty incredible—except the bot’s current skills include looking up weather forecasts and giving directions. Research has also found that the average interaction time with Tank’s predecessor was less than 30 seconds—meaning, the robot is basically as exciting as your phone sans Facebook. In reality, the end goal is to learn more about human-robot interaction and design robots that humans will enjoy interacting with. The industry will, of course, continue making great strides, but the interactivity and mobility of servicebots will be a long journey. While a human can recognize a series of different social and physical cues and determine how to act, robots act in highly constrained ways. They can tell you the weather, and answer simple questions, but they can’t deliver full and detailed interactions.

Boltr is a famous bot in the Aloft Cupertino hotel. He can deliver toothpaste to your room, and will request a tweet instead of a tip. Rather than replacing humans, Boltr does the busywork of running around the hotel. More importantly, the bot is known for being regularly asked to help take selfies. Botlr may be cute, but if customer’s favorite function is his ability to take selfies, we are certainly not living in the future. In order to properly implement robots who speak will be yet another hurdle. Language is complex. If you’ve ever been on hold and asked to “speak your account number,” or verbally “choose your option,” you’ve run into another wall holding back genuine interaction between bots and humans.

Where does AI really shine in the hospitality industry? That same hotel, Aloft Cupertino, is also looking to implement smart mirrors, smart carpets and AI-powered thermostat systems. In fact, what AI does best is not human interaction. The Hyatt Regency Riverfront in Jacksonville, Florida uses artificial intelligence system to better generate staffing schedules and forecast food and beverage needs. The Pan Pacific in San Fransisco was able to achieve highly accurate restaurant, room service and banquet forecasting, as well maintain appropriate staffing levels. Accuracy in these areas means hotels can spend less money, which is much more practical than a selfie-taking robot. Though customers are largely unaware of these behind-the-scenes uses of AI, this sector will, no doubt, continue to blossom.

There is also the final factor in AI’s usage: human perception of robots and the implementation of artificial intelligence. Have you ever been greeted by a humanoid-styled robot? It’s mildly terrifying. Frankly, even when something is well-designed, that does not mean customers will respond well. An interesting study published in the International Journal of Contemporary Hospitality Management studied customer responses to hotel attempts to “go green.” The results show us that people have very specific, very human needs. Much like Carnegie Mellon’s students trying to understand what makes a human happy to interact their Roboceptionist, hotels need to understand what makes their customers happy to be greeted by robots. This fabulous video from BBC of a journalist checking into the kooky Henn-na Hotel makes one thing very clear: people are not quite ready to take all servicebots seriously. Whether it’s a uniformed Japanese woman or a dinosaur in a hat, currently implemented robots are still very alien.

How do companies make the leap from gimmick to full-scale implementation? It’s all about the perception. The study in the International Journal of Contemporary Hospitality Management found that hotel customers were somehow mentally tipped off when a hotel was attempting to implement a new system because of ulterior motives. Guests felt skepticism and discomfort. They were not pleased and did not want to further invest in such hotels. However, when the hotel staff implemented the same system with no motive but the actual comfort of their guests, the visitor had an entirely different response. There was no skepticism or discomfort. The role of AI in hospitality will have to come in a similar fashion if it is to succeed. There will be a slow trickle from gimmicky and high-class hotels to the ordinary family vacation spot, and exactly what that means remains to be seen.

AI is already booming in the hospitality industry, though it isn’t always obvious. Friendly velociraptor receptionists are bound to cause a stir, but the overall use of AI and robots in hospitality might not be as striking as once thought—at least not yet.

image source: Henn-na Hotel

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In politics decision making takes time, especially when there is a lot at stake. In Brussels, home of the European Union, this has been the case for the new EU data protection package.

Last June, the EU Civil Liberties and Justice Committee (aka LIBE) entered “trilogue” negotiations between the EU Parliament (representing us, the citizens), the EU Commission (the government of the EU) and the EU Council (all 28 heads of EU member states’ governments) on the proposed changes in Data Protection regulations. On the 17th of December 2015 LIBE announced that all parties have finally reached agreement consensus.

The major points of the package are:

• Explicit consent: Companies that want to use personal data for purposes other than delivering the service for which their clients provide the data, must seek formal, written permission from the client for such use. No more “general data processing” tick boxes. Instead, companies will need “explicit consent.”
• Right to be forgotten: In some instances, like when the data has been collected during a time when the data subject was a minor and in need of parental consent, data subjects have a “right to be forgotten.” Their personal data must be removed from IT systems, including those in test environments.
• Privacy by design: All IT systems must be “privacy ready.” Data protection must be by design, not as an afterthought.
• Onerous fines: Failure to comply will be met with massive fines, up to 4 percent of the offender’s global turnover. For large global companies, this could amount to billions.
• Timeframe: Upon enactment, companies will have two years to adopt.

As the LIBE rapporteur, Jan Albrecht put it, “The regulation returns control over citizens’ personal data to citizens. Companies will not be allowed to divulge information that they have received for a particular purpose without the permission of the person concerned. Consumers will have to give their explicit consent to the use of their data.”

How easy is it to ‘forget’?

The new rules coming into force with the arrival of the EU Data Regulations pose a major challenge for all companies that collect and store personal data. Take for example the “Right to be forgotten.” To be able to execute on this law it requires companies to be in control of where any personally identifiable information (PII) resides within their systems. This might sound pretty simple, but it’s far from it; organisations not only need to consider their own back-end databases and backups, but they also need to consider any data being used by outsourcers, partners or cloud service providers they’re working with. In many cases, data could even be in use outside of the EU—in the systems of an outsourcer developing mainframe applications for the business, for example. This would instantly create a breach of the new EU regulations unless the proper controls were in place.

we consent to having our data used for system testing?

Explicit consent seems simple. We all know the tick boxes that we already see when doing business online. But do we ever read and understand what our data is collected and used for? What data do these online services need to deliver the service request and what kind of data is collected that has ‘purposes other than delivering the service for which the clients provide the data”? Do we consent to the latter?

Translating this issue from legal into IT lingo, we can take testing as an example: testing applications with real personal data will require an explicit consent of the end customer. If customers were to reject to the usage of their data in testing it could severely impact application testing. Complex applications, such as those developed for the mainframe, are often tested using live customer data in order to create an impression of how they’ll perform in the real world. However, this practice is already unlawful when businesses have not treated the data as personal and put stringent controls in place, not to mention informing people what their data will be used for beyond “normal business.” This is even more significant when the data is being used by third-parties, such as outsourcers. Unless the business has explicit consent from the customer for their data to be handed to an outsourcer and used in controlled testing environments, they’ll be in direct breach of the new EU legislations and face a painful fine.

Impact on testing/development

Alarmingly, research by Compuware indicates that many businesses lack a clear understanding of how their testing practices will be impacted by the new data protection legislation. A fifth of firms do not mask or protect customer data before sharing it with outsourcers, with the vast majority of them relying on non-disclosure agreements that in essence do not satisfy even current data privacy regulation. It is therefore extremely important for all businesses to start looking at their testing practices to ensure that they can comply with the “privacy by design” demand of the EU laws.

If any real personal data is used for testing, it’s high time to start protecting it with a test data privacy project to ensure compliance with the existing as well as new EU regulations. There is absolutely no excuse for continuing to use unmasked customer data in testing projects, and those that continue to do so will have nowhere left to hide when the EU legislators come calling.

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Smart watches are trendy. Smart thermostats are popular. But what else can you do with IoT technology? Whether you’re a tech­lover, lazy, or like trying new things, smart gear is able to do a lot more than track your calorie goals.

The Practical

Goji: Home security is important, and we are tired of the whole lock­and­key routine. There has to be something better than just heavy metal bolts. Smart locks like Goji let you use your phone as a key, whether you’re at the door or across town. Here’s why Goji is special: it can send you picture alerts. Goji will automatically take a picture of visitors and send them to your phone. Second, you can grant access to anyone with a supported smartphone just by selecting a date and time with the Goji app.

BodyGuardian: You know FitBit, Nike FuelBand and the dozens of other fitness trackers… but there is a whole other equally important use for smart watches: actual health monitoring. BodyGuardian was approved earlier this year by the FDA and monitors vital signs like heart rate and EKG rhythms. Small, wireless and Bluetooth­compatible, the BodyGuardian syncs up with Samsung tablets to share up­to­date information. This means not only better monitoring, but the ability to respond to signs of cardiac arrest, and to better analyze a person’s physical health and state. We also love the Tempo watch, which acts as a “rhythm journal” so you can keep a close watch on loved ones.

Drop ­Kitchen Scale: Baking is fun, right? What about those of us that don’t really like measuring, being precise, and are generally bad at following directions? Drop makes sure you don’t screw up your recipes with improper measuring. The scale automatically weighs itself as your pour in each ingredient. Connected with the Drop app, it let’s you know when you are ready to move on to the next ingredient.

The Fun

WeMo Smart CrockPot: WeMo has been making oodles of fun smart appliances, and this is just the best. As if using a crock pot wasn’t easy enough, it just got so much better. If you’re away, you can start your slow cooker, turn the temperature up, down, or whatever you might need. Who doesn’t want to come home to the aroma of freshly cooked dinner?

Parrot Pot: This flower pot is equipped with a water­ sensor to automatically water your plants when they’re thirsty. With a 2.2L reservoir, it can take care of plants for up to a month, giving you peace of mind and happy plants. The machine can even switch to conservation mode when there is little water left. With a database of several thousand plants, and sensors to observe light and heat, it definitely knows more about how to take care of your plants than you do.

Belkin WeMo: Currently billed as the crowd ­favorite smart­-plug, it can replace a host of other costly “smart” items simply by plugging into normal machines. Rather than investing some hundred dollars on a coffee machine specifically because it’s “smart,” why not buy a smart plug and attach it to your favorite coffee maker? You can then use the WeMo app from your android or iPhone to track your energy usage, or brew a cup of coffee. It’s even Amazon Echo compatible.

SmartMat: Yoga has become increasingly widespread and popular these days, leaving a trail of both enthusiasm and injuries in its wake. Improper Practice doesn’t just mean you’re ineffective, it can mean real damage for your body. The SmartMat is a yoga mat equipped with pressure sensors to sense not just which pose you are doing, but how your weight is distributed. These sensors hook up to an app that can give you the full details on your movements. It can also lead you through a full class, complete with real­time feedback, or go into silent mode, tracking your moves for you to analyze later.

The Brand Spanking New

Heddoko Smartwear: Want to work out properly? Heddoko clothing gives you 3D visuals of your movements. Real­time instruction and feedback as well as movement analysis mean this could be incredibly useful for athletes, coaches, and physical therapists everywhere. Hook it up to the Heddoko app and see how your body moves. Heddoko is still in testing but, golly, we cannot wait to try it out.

Mycroft: There are oodles of smart home devices. There’s the Apple HomeKit and Ivee (both of which suffer from less than stellar reviews), as well as the popular Amazon Echo. Automating your entire home is, perhaps, not something we are totally prepared for. It’s a work in progress.

Unfortunately, hubs like Apple’s HomeKit are going to geared towards Apple products, means lots of complications down the road. So, what’s so exciting about Mycroft? It’s open source. When it comes to new technology, you do not want to be trapped into using apps and tech from only one company. Combining Arduino and Raspberry Pi, who could ask for more?

Vinli: This little box transforms any car into a 4G LTE­connected smart(er) machine. While plenty of apps exist to connect your phone to your car, this cuts the phone out of the equation and hooks directly to your car. By plugging into the OBD­II (Onboard vehicle diagnostic), the Vinli can track just about anything and is limited only by the apps developers can dream up. With more and more phone companies investing in Vinli and jumping on board, we are keeping a close on eye on this baby.

It’s easy to get wrapped up in the wearables ­craze, or think that IoT is only good for giving us more environmentally helpful light bulbs, and smart washing machines. IoT is changing the way we think about, well, everything. From health, to hobbies, to money and the environment, interconnected tech provides real, practical opportunities. Besides, how can we not get excited about the adorable open source Mycroft, or the possibility of the Drop scale saving us from baking disasters?

The travel industry is no stranger to Data Science disruption – especially air travel. Online flight finders have been snapping up data scientists from other industries in order to make better use of the available data, and airlines have been building and opening APIs.

Many services are using this opportunity to offer the cheapest or most convenient flights, but FLYR Labs have a slightly different perspective: Rather than have customers watching flight prices fluctuate, trying to find the perfect time to buy, FLYR offers a predictive service that allows customers to see how cheap a flight is likely to get and lock in that price.

We spoke to Alex Mans, Co-Founder and CTO of FLYR Labs, to find out more about his story and the vision of FLYR Labs.

You’ve been involved in successful technology startups for over a decade now. Can you share any observations or remarkable milestones from that period, from a personal perspective?

The major changes that have driven my decision making over the past decade relate to the fact that with every year that passes, formerly impossible tasks become within reach of anyone who dares to take them on.

When it comes to utilizing data for bettering people’s lives or simplifying the complex, I learned not to be afraid of thinking big as there’ll pretty much always a solution within reach.

How about in terms of technology shifts and the development of data driven technology?

Over time, we moved from descriptive analytics (“What happened?”) to diagnostic analytics (“Why did it happen?)”.

Early on for example, I was involved with a company that analyzed (internet) network traffic to determine whether security threats were present on a company’s local network. This was diagnostic analytics while most people were still focussed on general error reporting or building dashboards to manually sift through data.

Right now, every industry or vertical is trying to move towards the next major category in data analysis. Predictive analytics (“What will happen?”).

The most important and life-changing shift that we’re starting to see today is a move into the realm of prescriptive analytics (“How can we make it happen?”). Intelligent, prescriptive technologies are the ultimate force in forcing changing and simplicity in people’s lives. Done right, it gives people peace of mind.

FLYR’s technology falls into that category as we predict what airfares will look like in the near future and structure that information in ways that the use can easily consume and act upon (e.g. “Don’t buy this flight yet, we are confident that we can find you a better deal”).

Fluctuating airfares is one of the most interesting predictive analytics applications we’ve come across. How did you land on this as your problem to solve with FLYR?

As many of us, I noticed the huge volatility in airline ticket pricing and figured there had to be some kind of recognizable logic to it.

I did a lot of research into how airlines price their seats. I figured that with the right data, understanding airlines based on how they change their ticket prices would be feasible and value could be extracted from that knowledge.

One of your offerings is the Foresight API. What kind of use-cases can we expect to see for this? What does your target market look like?

Foresight is the API that sits right on top of our prediction technology. Foresight can return a day-by-day prediction of how airfares will move for specific flights, complex combinations of flights or simply at a aggregate (route) level.

This technology and API is made available to any company that wants to integrate it into their products, including online travel agencies, meta-search companies, etc.

Looking at the consumer facing side with GetFLYR.com – can you break down how the services works?

Getflyr.com is dedicated to the 98% of people that search for flights, but have no intention of booking today. For those people, we provide the tools to deal with highly volatile airfares while they finalize their plans.

A graph (powered by Foresight) will tell you how much money is at stake if you wait while fares are expected to increase. If we believe fares are going down, you can quickly see in what savings holding off on your booking would result.

To track a flight and it’s predictions for longer periods of time, you can set a FareBeacon alert that tells you when the time to buy has arrived.

If complete peace of mind is what you want, then FareKeep is the thing for you. Instead of purchasing a flight at say 300 USD today, you can lock-in that fare for a small fee (usually anywhere in between 3 USD and 30 USD) for a week. If fares go up over those 7 days, we will pay for the increase. If fares go down or we find you a better deal, you have the option to pocked the savings.

How much data goes into powering your product, and what does your technology stack look like?

Every hour, FLYR receives millions of airfares from all over the world that feed into our data pipeline. This data pipeline normalizes the data before storing it in a massive database. We use a combination of NoSQL and SQL databases, depending on the storage purpose (e.g. pricing data vs. transactional data).

Whenever we get new airfare data, our prediction algorithms are automatically retrained and distributed to the server cluster that sits right underneath our Foresight API.

As an entrepreneurially minded person, what technological advances excite you most for the future?

As mentioned previously, I see us moving in a world full of prescriptive technology. Technologies that understand what we are looking for and help us to achieve our underlying goals based on predicting will happen next.

The major considerations and in some cases also challenges will evolve around balancing relevance and trust between the user and technology.

Alex Mans, CTO of FLYR Labs, is an entrepreneur with 8+ years of experience in launching and growing online and offline tech products. Focussed on assembling great teams with diverse complementary backgrounds and expert skill levels. Ability to connect and work with entrepreneurs, engineers, designers, marketeers, investors and academics by continuously trying to achieve a deeper understanding of technology, design, research and finance.

(image credit: curimedia)

Andrew joined Etsy in 2014, and lives in London, making him their first data scientist who lives outside the USA. Prior to Etsy he spent almost 15 years designing machine learning workflows, and building search and analytics services, in academia, startups and enterprises, and in an ever-growing list of research areas including biomedical informatics, computational linguistics, social analytics, and educational gaming. He has a masters degree in bioinformatics and a PhD in natural language processing, can count to over 1000 on his fingers, but doesn’t know how to drive a car.

Follow Peadar’s series of interviews with data scientists here.

As part of my regular ‘Interview with a Data Scientist’ feature, I recently interviewed Andrew Clegg. Andrew is a really interesting and pragmatic Data Science professional and currently he’s doing some cool stuff at Etsy. You can visit his blog here and his most recent talk on his work at Etsy from Berlin Buzzwords.

What project have you worked on do you wish you could go back to, and do better?

The one that most springs to mind was an analytics and visualization platform called Palomino that my team at Pearson built: a custom JS/HTML5 app on top of Elasticsearch, Hadoop and HBase, plus a bunch of other pipeline components, some open source and some in-house. It kind of worked, and we learnt a lot, but it was buggy, flaky at the scale we tried to push it to, and reliant on constant supervision. And it’s no longer in use, mostly for those reasons.

It was pretty ambitious to begin with, but I got dazzled by shiny new toys and the lure of realtime intelligence, and brought in too many new bits of tech that there was no organisational support for. We discovered that distributed data stores and message queues are never as robust as they claim (c.f. Jepsen); that most people don’t really need realtime interactive analytics; and that supporting complex clustered applications (even internal ones) is really hard, especially in an organisation that doesn’t really have a devops culture.

These days, I’d try very hard to find a solution using existing tools — Kibana for example looks much more mature and powerful than it did when we started out, and has a whole community and coherent ecosystem around it. And I’d definitely shoot for a much simpler architecture with fewer moving parts and unfamiliar components. Dan McKinley’s article Choose Boring Technology is very relevant here.

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

I was asked this the other day by a recent PhD grad who was interested in a data science career, so I’ll pass on what I told him.

I think there are broadly three kinds of work that take place under the general heading of “data scientist”, although, there are also plenty of exceptions to this.

The first is about turning data into business insight, via statistical modelling, forecasting, predictive analytics, customer segmentation and clustering, survival analysis, churn prediction, visualization, online experiment design, and selection or design of meaningful metrics and KPIs. (Editor note: In the UK this used to be called an ‘Insight Analyst’ role, typical at retail firms or banks)

The second is about developing data-driven products and features for the web, e.g. recommendation engines, trend detectors, anomaly detectors, search and ranking engines, ad placement algorithms, spam and abuse classifiers, content fingerprinting and similarity scoring, etc.

The third is really a more modern take on what used to be called operational research, i.e. optimizing business processes algorithmically to reduce time or cost, or increase coverage or reported satisfaction.

In many companies these will be separate roles, and not all companies do all three. But you’ll also see roles that involve two or occasionally all three of these, in varying proportions. I guess a good start is to think about which appeals to you the most, and that will help guide you.

Don’t get confused by the nomenclature: “data scientist” could mean any of those things, or something else entirely that’s been rebranded to look cool. And you could be doing any of those things and not be called a data scientist. Read the job specs closely and ask lots of questions.

What do you wish you knew earlier about being a data scientist?

Well, I wish I’d taken double maths for A level, all those years ago! As it was, I took the single option, and chose the mechanics module over statistics, something that held me back ever since despite various post-graduate courses. There are certain things that are just harder to crowbar into an adult brain, if you don’t internalize the concepts early enough. I think languages and music are in that category too.

(For our global readers: A-levels are the qualifications from the last two years of high school. You usually do three or four subjects. You could do standard maths with mechanics or stats, or standard + further with both, which counted as two qualifications.)

I had a similar experience with biology – I dropped it when I was 16 but ended up working in bioinformatics for several years. Statistics and biology are both subjects that are much more interesting than school makes them seem, and I wish I’d known that at the time.

How do you respond when you hear the phrase ‘big data’?

Well, I used to react with anger and contempt, and have given some pretty opinionated talks on that subject before. It’s one of those things you can’t get away from in the enterprise IT world, but ironically, since I joined Etsy I’ve been numbed to the phrase by over-exposure… Just because the Github repo for our Scalding and Cascading code is called “BigData”.

It’s a marketing term with very little information content – rather like “cloud”. But unlike “cloud” I actually think it’s actively misleading – it focuses attention on the size aspect, when most organisations have interesting and potentially valuable datasets that can fit on a laptop, or at least a medium-sized server. For that matter, a server with a terabyte of RAM isn’t much over $20K these days. “Big data” makes IT departments go all weak-kneed with delight or terror at the prospect of getting a Hadoop (or Spark) cluster, even though that’s often not the right fit at all.

And as a noun phrase, it sucks, as it really doesn’t refer to anything. You can’t say “we solved this problem with big data” as big data isn’t really a thing with any consistent definition.

What is the most exciting thing about your field?

That’s an interesting one. Deep learning is huge right now, but part of me still suspects it’s a passing fad, partly because I’m old enough to remember when plain-old neural networks were at the same stage of the hype cycle. Then they fell by the wayside for years. That said, the concrete improvements shown by convolutional nets on image recognition tasks are pretty impressive.

Time will tell whether that feat can be replicated in other domains. Recent work on recurrent nets for modelling sequences (text, music, etc.) is interesting, and there’s been some fascinating work from Google (and their acqui-hires DeepMind) on learning to play video games or parse and execute code. These last two examples both combine deep learning with non-standard training methods (reinforcement learning and curriculum learning respectively), and my money’s on this being the direction that will really shake things up. But I’m a layman as far as this stuff goes.

One problem with neural architectures is that they’re often black boxes, or at least pretty dark grey – hard to interpret or gain much insight from. There are still a lot of huge domains where this is a hard sell, education and healthcare being good examples. Maybe someone will invent a learning method with the transparency of decision trees but the power of deep nets, and win over those people in jobs where “just trust the machine” doesn’t work.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough? 

It took me a long time to realise this, but short release cycles with small iterative improvements are the way to go. Any result that shows an improvement over your current baseline is a result — so even if you think there are much bigger wins to be had, get it into production, and test it on real data, while you work on its replacement. (Or if you’re in academia, get a quick workshop paper out while you work on its replacement!)

This is also a great way to avoid overfitting, especially if you are in industry, or a service-driven academic field like bioinformatics. Instead of constantly bashing away at the error rate on a well-worn standard test set, get some new data from actual users (or cultures or sensors or whatever) and see if your model holds up in real life. And make sure you’re optimizing for the right thing — i.e. that your evaluation metrics really reflect the true cost of a misprediction.

I worked in natural language processing for quite a while, and I’m sure that field was held back for a while by collective, cultural overfitting to the same-old datasets, like Penn Treebank section 23. There’s an old John Langford article about this and other non-obvious ways to overfit, which is always worth a re-read.

Peadar Coyle is a Data Analytics Professional based in Luxembourg. He has helped companies solve problems using data relating to Business Process Optimization, Supply Chain Management, Air Traffic Data Analysis, Data Product Architecture and in Commercial Sales teams. He is always excited to evangelize about ‘Big Data’ and the ‘Data Mentality’, which comes from his experience as a Mathematics teacher and his Masters studies in Mathematics and Statistics. His recent speaking engagements include PyCon Sei in Florence and he will soon be speaking at PyData in Berlin and London. His expertise includes Bayesian Statistics, Optimization, Statistical Modelling and Data Products

(Image Credit: brewbooks / Natural Language Processing with Python / CC BY SA 2.0 )

Content monetization startup, Skimlinks, which helps digital publishers get value through their affiliate marketing, has announced raising $16 million in a Series C funding round.

The latest capital will help in platform expansion to assist publishers get more value out of their commerce-related content. Termed “comtent”, this type of shopping-oriented editorial content includes product galleries, reviews, features, gift guides, wish lists, and deal news, explains their news release.

The round was spearheaded by Frog Capital, and saw contribution from existing investors Bertelsmann Digital Media Investments (BDMI), Greycroft, Sussex Place Ventures and Silicon Valley Bank (SVB).

Vouching for the innovator, Iyad Omari, a Partner at Frog Capital said : “Publishers are increasingly turning to content-led monetization strategies for growth, as traditional digital display advertising rates continue to decline. With incredibly efficient technology and deep commerce insight, Skimlinks has become the go-to content monetization partner for the world’s most prestigious digital publishers.”.

“We are very impressed by what Alicia and her team have achieved and look forward to working with them to make this an even bigger success story,” he added.

The current investment round took the total capital raised so far, to $24 million in equity.

Founded in 2007 Skimlinks has increased its customer base emphatically last year with Vox Media, Time Inc and Cafemom added to an attractive list that includes clients such as Gawker Media, Hearst and Condé Nast all the while earning a revenue of over $625 million in sales for its 20,000 retail partners. The workforce saw a 40% growth while they also opened an office in the U.S., in New York.

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(Image credit: Skimlinks)

Alibaba Group, the Chinese e-commerce company, released a report earlier this month on the 18th of December, charting the results of a task force with the police that has been trying to stop the sale of fake goods on the company’s Taobao platform, reports the Shanghai-based online media outlet the Paper.

A series of reports have been published within last week tracking the progress of false goods and their origins, reports the Want China Times.

It has been revealed through the report that ‘400 suspects among 18 groups, involving more than 1,000 cases of trademark infringement, were caught making knock-off goods in more than 200 locations,’ the biggest of the crack downs being counterfeit sports shoes valued at 21.5 million yuan (US$3.46 million) and blenders and jerseys together valued at 10 million yuan (US$16.1 million).

Alibaba will tap Big Data Analytics to root out such fake instances.

In the past, founder Jack Ma had made clear his stance against counterfeits by utilizing analytics and tracking transactions, delivery details, IP addresses and other information, at the World Internet Conference, in November.

The police has been aided by the company’s massive transaction data in resolving the latest issues, ushering the structuring of a big data model to glean, analyze and ultimately take steps to limit likely cases of infringement in the future, the report said, according to the Want China Times.

Read more here.

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Mark Cuban is an American businessman, investor, tech mogul, and owner of the NBA’s Dallas Mavericks. He is also a “shark” investor on the hit television series Shark Tank, and creator of privacy focused messaging app ‘Cyber Dust’.

After being falsely accused of insider trading by the SEC in 2008 and having to hand over all of his emails and messages, Mark decided to build a truly secure and private method of communication.  Enter Cyber Dust, a messaging alternative that promises to never let your data touch a hard drive, only staying in-memory for a period of 24 hours.

We had a quick chat with Mark to find out some more about the app, his reasoning, and the technology behind it.

We know you had legal protection in mind when you created Cyber Dust, had you also considered a situation like Sony’s recent breach?

Absolutely. Everything and anything is hackable. There is always someone better at it than your security. For this reason we made sure that we never kept anything longer than 24 hours. More importantly for those 24 hours, nothing ever touches a hard drive.

if we detect a problem, we just pull the plug and the data within the 24 hour period is gone. Being exclusively in memory also makes it harder for anyone to root around and search.

How much of a shock do you think the breach was to the US media and tech industry?

It was a shock only because of the fact it impacted the release of a movie and surfaced emails from and about big celebrities. Beyond that i dont think it was a shock at all. If companies have a hard time protecting credit cards, it should be no surprise when emails or pictures are hacked.

Have you had corporate clients pick up Cyber Dust since then? Do you see much traction at an enterprise level for the app?

We had them before and after.

We don’t currently try to be an enterprise solution. Much like dropbox and other apps were introduced to organisations outside of their tech groups, the same is happening with Cyber Dust.

Are there emerging technologies or trends you think increase the risk to individual privacy? (Or erode privacy in a more insidious manner by changing our perception of it?)

I think social media is reducing our awareness of privacy issues. You look on twitter and there are people with 20k public tweets. How is there any upside to that ? Same with facebook, tumbler, instagram, etc. We just introduced an app called Xpire (in ios store, android coming). you can get info at getxpire.com. It allows you to search and delete old social media posts. It also allows you to set a timer to new posts.

Is there any reason at all why social posts should live forever?

What sounds reasonable and safe today most likely wont in a few years.

Do you see other opportunities for this straight forward approach to data privacy?

Yes. We will extend it into notifications for the Internet of Things. Its already being used by companies to send company updates and alerts. From simple reminders about meetings to critical information.

The fact that it’s non intrusive, is gone quickly and just as importantly prevents the recipient from procrastinating, you have to respond right then while you remember it, makes us a great corporate tool.

Rather than trying to replace email, you will see us extend into being a place where we can send updates to people, places and things and not leave a trace.

Just so people know, we have no server logs. None. We don’t know who used the service and don’t want to know. We don’t have or keep IP addresses or any information. Not GPS data. Nothing.

Any information we do gather is limited to the device and when the message is gone, so is the information.

Only exception is if you go to a website from inside the browser. Then the website operates normally.

Had you also considered the social component? There’s a lot to be said for a way to connect that is as private and immediate as a face to face conversation. If so, how might this digital intimacy factor into the development of the app?

We are definitely a content source. From celebrities, from businesses, from websites. You can get business headlines from BusinessInsider, tips from Daymond John of Shark Tank, GaryVee and Jason Calicanais, 2 big time tech investors. From entertainers, Sports teams and stars. We have LifeHacks, Factoftheday, Horoscopes. Every day there are a ton of new data and information sources being added. You can get a sense of them at http://www.cyberdust.com/popular

Because of the nature of the app, there’s no way to verify who any account actually belongs to (and therefore use it against them), short of an official blast. Was this a consideration in the design, or are you planning on adding verification at some point?

If you are on our popular page, you are verified. That will be our verification. There are a ton of A list celebrities and athletes using the service. But they use it for their own privacy. We want people to be able to use it with absolute privacy. If you happen to find a celeb’s user name, its incredibly easy for them to block you.

Cyber Dust is available for Android and iOS.

“Every spoken word isn’t recorded. Why should your texts be?”

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Hampton Creek, the San Francisco based, food technology company with pioneering data analysis research in utilizing plant protein as an egg substitute, has just picked up $90 million in a series C round of funding.

Leading the round were Horizons Ventures and Khosla Ventures with participation from Marc Benioff, the Salesforce.com founder and CEO and Facebook’s Eduardo Saverin among others.

“The way that consumers are buying and consuming food is undergoing massive change. And for a long time technology didn’t play a role in food as it relates to the end consumer, but that’s changing drastically and Hampton Creek is on the forefront of that,” notes Craig Shapiro at Collaborative Fund, a Hampton Creek investor.

The new funding will help Hampton Creek in developing their R&D and protein substitution technology and also enhance outreach and distribution strategy.

“Part of what we do is figure out out a way to get rid of s— food that’s bad for our bodies and bad for the planet. That all requires a different approach when it comes to technology,” Hampton Creek founder and CEO Joshua Tetrick told Business Insider.

“We have distribution with some of the largest food companies in the world,” Tetrick explained, “and we’ve got to produce. We want to make sure we’re not holding back. We want to grow much deeper in Asia than we already are.”

Founded in 2011, Hampton Creek has so far garnered $120 million with products such as Just Mayo and Just Cookies being available in most retail chains. Their latest are two new products slated for release next year – Just Pasta and called Just Scrambled – both doubtlessly, eggless.

Read more here.

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Global private equity firm Warburg Pincus have committed $75 Million to the growth of predictive analytics industry leader Blue Yonder. The investment adds Warburg Pincus to the existing group of investors which includes OTTO Group and the founders.

Blue Yonder has strong foundation in the European retail market, with customers including the Next, Otto Group, EAT, dm, Bauhaus  and supermarket chain Kaiser’s Tengelmann. A recent expansion into other verticals including industrial and transport, and logistics adds customers such as Eurotunnel, Lufthansa Systems and Bosch to that list.

“We are very excited about the large and rapidly growing market for Predictive Applications and see many opportunities developing alongside the rise of the Industrial Internet of Things.”

“Going forward Big Data analytics will be used to improve operational efficiencies and to transform business models, changing the way our clients can compete in the marketplace.”

Uwe Weiss, CEO at Blue Yonder

The Managing Director at Warburg Pincus, Joseph Schull, stated their delighted to partner with Blue Yonder, accelerating the company’s international growth as more and more corporates understand the value of harnessing their data assets for predictive analytics and decision automation.

Read more in the press release here.

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U.S. department store Macy’s is fusing Apple’s iBeacon technology with Shopkick to identify shoppers’ locations and provide them with ads & vouchers on their smartphones or tablets, in a bid to boost sales.

According to the Washington Post, Macy’s is deploying 4,000 sensors inside its 768 stores to make this happen. Macy’s has been trying to bring the two technologies together since the holiday season of last year.

Shopkick is a shopping app that offers customers rewards for walking into participating stores in the form of “kicks”, and the iBeacon is a low-cost transmitter that can interact with other devices in the proximity. Combining these technologies means when you pass near a certain item in the store, the iBeacon registers it, and a corresponding offer or voucher is presented to you via Shopkick.

Public opinion of using the iBeacon in retail is split. Enterprises like Lord & Taylor, American Eagle and Duane Reade have tested the technology in a limited number of stores, whereas others find it downright “creepy”.

“The customer who gets more engaged in more of the channels that Macy’s has to offer gives us more wallet share,” Kent Anderson, President of Macys.com, told WP in an interview.

Apart from this, the 156 year old chain, also has plans for an Image Search app, which allows customers to take pictures of merchandise and in turn be directed to similar items on sale at Macy’s. Developed at Macy’s San Francisco-based Ideas Lab, they collaborated with image-recognition technology start-up, Cortexica.

Read more here.

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Ecommerce analytics startup Qubit has secured $26 million in Series B funding on Monday, with Accel Partners leading the round. Original investors Salesforce Ventures and Balderton Capital, also participated. The funding will be used to expand Qubit’s US and European operations and development of the ongoing product innovation pipeline.

Qubit specialises in tools which assist online retailers to monitor and optimize sales through A/B testing and user centric personalization of content. Mark Choueke, the global communications director at Qubit, wrote in a blog post, “To receive backing from such experienced heavyweights in the market; to have their confidence that what we do and how we do it is worth investing in is amazing. ”

“It shows us that others are seeing what we have known for a long time: that personalization and web optimization are increasingly going to be among the marketing department’s most important growth levers for their businesses,” he added.

TechCrunch reports that, with approximately 150 customers in the U.K. and the U.S., including Hilton Hotel, Jimmy Choo, Staples, Farfetch, Topshop and Uniqlo, the company revealed a 260% year on year growth in sales in the six months to June 2014.

As a result of the funding, Bruce Golden of Accel Partners, will join the Qubit board. Founded in 2010 by four ex-Googlers, Qubit has raised $36.5 million in funding so far.

Read more here

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U.S. sandwich restaurant chain Jimmy John’s reported last week of a possible theft of its customers’ credit and debit card information at 216 of its stores and franchised locations.

“The credit and debit card information at issue may include the card number and in some cases the cardholder’s name, verification code, and/or the card’s expiration date. Information entered online, such as customer address, e-mail, and password, remains secure,” informed a statement published online. “The locations and dates of exposure for each affected Jimmy John’s location are listed on affected stores and dates.”

However, cards that had been swiped at the stores seem to be affected, and not the ones that were entered manually or online.

Third party forensic experts have been hired to help with the investigation. The breach took place between June 16, 2014 and September 5, 2014, when a miscreant manages to steal log-in credentials from Jimmy John’s point-of-sale vendor and it to remotely access the point-of-sale systems at the outlets.

According to online security blogger, Brian Krebs, who first broke the news in July, “Point-of-sale vendors remain an attractive target for cyber thieves, perhaps because so many of these vendors enable remote administration on their hardware and yet secure those systems with little more than a username and password — and often easy-to-guess credentials to boot.”

The restaurant chain came to know of this on the 30th of July, and they now claim that the security compromise has been contained. Customers can use their credit and debit cards securely at Jimmy John’s stores, reports the statement.

Read more here.

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Ometria, an outfit providing software solutions to drive profitable e-commerce, has bagged $500k in seed funding, after the $1.5 million seed round in March this year.

This round saw participation from 17 new investors including James Bromley, COO at Swiftkey, Andreas Andreou, commercial director at Quidco, Alicia Navarro and Joe Stepniewski of Skimlinks, and early stage technology VC fund SaatchInvest. It has also been reported that early investor and Huddle co-founder Ali Mitchell will be joining Ometria board.

Ali Mitchell explains, “Building any successful retailer is all about finding and keeping hold of the right customers.

“Now, Ometria is democratising customer insight for retailers – providing the same level insights for businesses of all sizes. It’s a fantastic product, with a great team behind it. That’s why I’m really excited to announce that today I’m joining the likes of Dr. Mike Baxter and Elisabeth Ling as an advisor to Ometria’s board.”

Ometria’s product is an all-in-one ecommerce marketing software package which claims to help online retailers make better use of data to acquire and retain “better customers”. Its “intelligence” platform helps retailers understand customer behaviour and product performance and thus assisting them in making educated decisions on follow up offers and further targeting.

The newly acquired funding will be directed towards development of the product, through addition of “predictive modelling and more automated functionality for customer communication”, reports TechCrunch.

Read more here.

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When Netflix forayed into original programming by committing $100 million for two seasons of House of Cards, it did so without even watching the pilot. What made Netflix so confident that it thought this decision was a no-brainer?

Big Data!

Netflix is, at its core, a data-driven company that diligently collects information from its 50 million-plus subscribers at an extraordinary speed. Netflix made the House of Cards decision by identifying that subscribers who watched the original British version of House of Cards were very likely to watch movies starring Kevin Spacey or directed by David Fincher.

The series was a huge hit and has been renewed for a third season. Here’s the question though: is this the future? Did Netflix just get lucky? Or were they right in banking on big data?

The New Approach to Picking TV Shows

Let’s take a look at how TV shows are traditionally approved. Networks receive hundreds of pitches from writers and producers. The networks then request scripts for a few of these and then order 20 to 30 pilots. Once the pilots are produced, they are presented to executives and sometimes focus groups to predict how successful the show might be. From that, networks approve a handful.

What is the success rate for the shows that see the light of day? Despite such an exhaustive process, only about one show out of three is renewed for a second season, according to publicly available data from 2009-2012.

Netflix, which is a new entrant into original programming, has licensed five original series to date. Four of them, including House of Cards, have been renewed for subsequent seasons whereas the jury is still out on the most recent one, Turbo Fast, an animated show for kids which has only recently finished its first season.

That still gives Netflix an 80 percent success rate (at the very minimum) with original programming, compared to the 30 to 40 percent success rate for networks. These shows have primarily been picked by running data mining and other algorithms against the vast user behavior data available to determine the size of the possible audience and thereby the likelihood of success.

More Big Data at Netflix

For Netflix, big data doesn’t stop here. Not only does it use the data to identify what shows to commit to, but it can now take additional steps to ensure that the show reaches the right audience.

For example, Netflix made ten different versions of the trailer for House of Cards geared towards different audiences. Fans of Kevin Spacey watched trailers that were focused on him while people who liked female-oriented movies saw trailers that highlighted the women in the show.

In addition, Netflix uses its recommendation engine to promote new content to its subscribers. The recommendation engine uses a complex algorithm that has been built and fine-tuned around the rich stash of behavioral data that Netflix has collected. This cross-promotion of products enables Netflix to cut down on its marketing costs, especially with original content.

The Takeaway

Netflix collects a lot of data to understand how its users behave and what their preferences are. It collects metrics including what people watch, when they watch, where they watch, what devices they use, ratings, searches, when users pause or stop watching, etc.

Netflix derives meaningful insights from all of this data to personalize and enhance the user’s experience on the platform. This allows the company to provide a unique experience for every individual subscriber, specifically tuned to their tastes and preferences.

There is concern that such data-driven programming could impact quality and diversity, as directors could sacrifice creativity and instead tailor their shows solely based on what the data shows is the right thing to do.

Also, most of us might have seen that Netflix’s data is not perfect. For example, Netflix thinks that I would like Portlandia based on my interest in The Office, but that is not the case. However, there is no denying the impact that big data can have and Netflix is radically transforming the broadcasting industry with big data.

This article was first posted here

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Harsha Hegde currently works at MResult where he focuses on the company’s goal of maximizing business results for clients. He got his MBA from Carnegie Mellon University, USA and prior to that worked as a software developer for 6 years at companies including Oracle. He is passionate about the different ways in which technology can have a positive impact on our lives.

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Amazon is a big data giant, which is why I want to look at the company in my second post of my series on how specific organisations use big data.

We all know that Amazon pioneered e-commerce in many ways, but possibly one of its greatest innovations was the personalized recommendation system – which, of course, is built on the big data it gathers from its millions of customer transactions.

Psychologists speak about the power of suggestion – put something that someone might like in front of them and they may well be overcome by a burning desire to buy it – regardless of whether or not it will fulfil any real need.

This is of course how impulse advertising has always worked – but instead of a scattergun approach, Amazon leveraged their customer data and honed its system into a high powered, lazer-sighted sniper rifle. Or at least that is the plan – they don’t seem to get it completely right yet. Like most of us, I have had some very strange recommendations from Amazon.

Anyway, their systems are getting better and it looks like what we have seen so far is only the beginning – as I’ve previously mentioned, Amazon has recently obtained a patent on a system designed to ship goods to us before we have even decided to buy it – predictive despatch – you can read more about that here. This is a strong indicator that their confidence is reliable predictive analytics is increasing.

An important factor to consider when looking at Amazon is how commercial its big data is, compared to those of other companies that deal with data on a comparable scale. Unlike, say, Facebook – which might know an awful lot about which movies you like or who your friends are – the vast majority of Amazon’s data on us relates to how we spend hard cash.

And having worked out how to use it to get more money out of our pockets, it is now setting out on a mission to help other global corporations do the same – by making that data, as well as its own tools for analyzing it, available to buy.

This means that, as with Google, we have started to see adverts driven by Amazon’s platform and based on its data appearing on other sites over the past few years. As noted by MIT Technology Review last year, this makes the company now a head-on competitor to Google – with both online giants fighting for a chunk of marketers’ budgets.

However, ad sales is not the only arena in which Amazon is taking on Google – its Amazon Web Services offers cloud-based computing and big data analysis on an enterprise scale. This allows companies which need to run highly processor-intensive procedures to rent the computing time far more cheaply than setting up their own data processing centres – just like Google’s BigQuery.

These services include datawarehousing (Redshift), hosted Hadoop solution (Elastic Map Reduce), S3 – the database service it uses to run its own physical warehousing operations and Glacier, an archival service. Recently added to this list is Kinesis, which is a real-time “stream processing” service designed to aid analysis of high volume, real-time data streams.

Amazon has also incorporated big data analysis into its customer service operations. Its purchase of shoe retailer Zappos is often cited as a key element in this. Since its founding, Zappos had earned a fantastic reputation for its customer service and was often held up as a world leader in this respect. Much of this was due to their sophisticated relationship management systems which made extensive use of their own customer data. These procedures were melded together with Amazon’s own, following the 2009 acquisition.

Finally, it is worth mentioning the public data sets that Amazon hosts, and allows analysis of, through Amazon Web Services. Fancy digging around in the data unearthed through the Human Genome Project, NASA’s Earth science datasets or US census data? Amazon hosts all of this and much more, and makes it available for anyone to browse for free.

Amazon has grown far beyond its original inception as an online bookshop, and much of this is due to its enthusiastic adoption of big data principles. It looks set to continue breaking new ground in this field, for the foreseeable future.

This article was first posted here

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Bernard Marr is a global enterprise performance expert and a best-selling business author. He has worked with and advised many of the world’s best-known organisations including Accenture, Astra Zeneca, Bank of England, Barclays, BP, DHL, Fujitsu, Gartner, HSBC, Mars, Ministry of Defence, Microsoft, Oracle, SAP and Shell, among many others. His leading-edge work with major companies, organisations and governments across the globe makes him an acclaimed and award-winning keynote speaker, researcher, consultant and teacher. Bernard is acknowledged by the CEO Journal as one of today’s leading business brains.

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In what the US Federal Communications Commission has termed as an “unacceptable”  usage of personal consumer information for marketing purposes, telecom giant, Verizon Communications has agreed to pay a fine of $7.4 million to the US Treasury.

Travis LeBlanc, Acting Chief of the FCC’s Enforcement Bureau said in a statement, “In today’s increasingly connected world, it is critical that every phone company honor its duty to inform customers of their privacy choices and then to respect those choices.”

“It is plainly unacceptable for any phone company to use its customers’ personal information for thousands of marketing campaigns without even giving them the choice to opt out,” he said.

According to the US Communications Act, telecom companies must get the approval of its customers through either an “opt in” or “opt out” process, with regards to sharing some of their personal data. If the process fails at some point, the company must report the problem to the FCC within five business days.

In its defence, Verizon issued a statement pointing out that any incident of data breach had not occurred. “The issue here was that a notice required by FCC rules inadvertently was not provided to certain of Verizon’s wireline customers before they received marketing materials from Verizon for other Verizon services that might be of interest to them. It did not involve a data breach or an unauthorized disclosure of customer information to third parties.”

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In a bid to reach out to more retail companies, IBM’s cognitive computing platform Watson might be utilised to fabricate ‘fashion’ apps that overlay structured and unstructured data to help people decide what to wear.

While at the Melbourne Spring Fashion Week, IBM Watson’s global retail transformation leader Keith Mercier spoke of data from social media, current weather, the clothes in a persons wardrobe, events in their calendar, and access to online shopping that could be analysed by custom apps to provide clothing suggestions to the user.

“If you know the places where consumers are getting data today, when it comes to shopping, it is weather, it is inspiration from social, it might be what is in my closet, it might be my purchase history. You could use Watson in other technologies to bring all those together, and then have that dialogue around the data,” Mercier pointed out to ZDNet.

However, the final decision lies with the user. What the AI-like Watson does is provide the user with the collated data that was previously unstructured to help choose better.

Building on its technology and data capabilities, IBM has signed into a three year partnership with Melbourne Spring Fashion Week to track trends in the show. IBM is assisting the organisers monitor the social posts on the events digital site in order to use the “unstructured input” to plan next year’s event.

Mercier explained, “We believe cognitive computing really is where the world is moving to and we’re one of the first movers in this space. We’re getting to a point that it can start to be commercialised.”

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American retail giant Home Depot maybe the latest company to fall victim to a massive online data breach.

Initial reports surfaced on cybersecurity journalist Brian Krebs’ website, about a new batch of stolen credit and debit cards that went on sale in the cybercrime underground on Tuesday morning. Various banks have confirmed that Home Depot stores may be the source of the breach.

In a statement issued to Reuters and other news agencies, Home Depot spokesperson Paula Drake said, “I can confirm we are looking into some unusual activity and we are working with our banking partners and law enforcement to investigate. Protecting our customers’ information is something we take extremely seriously, and we are aggressively gathering facts at this point while working to protect customers. If we confirm that a breach has occurred, we will make sure customers are notified immediately. Right now, for security reasons, it would be inappropriate for us to speculate further – but we will provide further information as soon as possible.”

US enterprises have been facing data breaches more frequently in the last few years, many rumoured to be the work of Russian and Ukrainian groups. It is believed that all Home Depot stores were affected in this hack, which is to say, around 2,200 stores in the US and nearly 300 in other countries, making the breach larger than the one that hit Target in 2013.

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We recently caught up with Kevin Wong, a business intelligence professional and machine learning enthusiast, to talk about his latest project: Building a better recommender system for Steam.

For anyone who isn’t familiar, Steam is a digital distribution platform for games, developed by Valve Corporation. It boasts over 75 million members, with between 5 and 8 million online at any time, and estimated revenues of around $1.75 billion.

To put those figures in perspective, entertainment giant Netflix only had 50 million members at the end of July. Gabe Newell, MD at Valve, has claimed the company is more profitable per head than either Apple or Google. Clearly, Steam is a force to be reckoned with, so how can one man hope to improve on their product?

Hi Kevin, tell us a bit about yourself, and how you get into Machine Learning.

I am currently a Tableau consultant, and I’m well trained in visual analytics principles and data analysis. I realised there’s much more that can be done to unlock data’s full potential and I am passionate about it, so I enrolled Data Science Retreat, a 3-month data science bootcamp in Berlin. That’s how I got into Machine Learning, amongst other areas such as Hadoop and software engineering.

What led you to focusing on Steam for this project?

I like video games and I have been a Steam user for some time already. I really like using the service to purchase and download video games, without ordering them and having to wait for delivery. Having said that, I am always pretty frustrated by the recommendations that Steam has suggested for me, either through their list of “More Like This” games on their website, or the personalised suggestions after logging in.

I surveyed a few other Steam users and found that its a common sentiment. One member even told me he felt he really wanted to expand his library, but Steam actually holds him back from purchasing because of the poor recommendations.

How exactly does the recommender system work, and what technologies are you using?

Steam’s approach is to look at the similarity between the games you already own compared to other games in their library, based on some predefined attributes.

My recommender looks at patterns in game ownership and patterns in actual time played (i.e. whether users who spent a lot of time in game A also spend a lot of time in game B). For instance, if a lot of Counter Strike players also own Half Life, and they spend a lot of time on both games, Half Life will be one of the top recommendations for users who like Counter Strike.

For the technically minded, Log-Likelihood Ratios and Pearson R correlation are the two recommendation algorithms I have implemented with Python. The website mainly uses PHP and Javascript to interact with users and load recommendations.

Building recommender systems for third party services is an interesting idea. What advantages do you think it offers the consumer?

Well, it is not as interesting if Steam had come up with an excellent recommendation system in the first place! If Steam really puts effort into this, they have the potential to improve their recommendation engine by adding information such as user’s browsing histories, wishlists, and other personal details.

In contrast, a recommendation site built by a video game fan is impartial, as it wouldn’t be promoting games that may provide extra incentives to retailers. I also have the freedom to include features that fit the needs of my users, even though some features may be commercially undesirable. There is no incentive for non-commercial fan sites to store personal data, making it safe for users who have concerns regarding to privacy.

What were the technical challenges involved in developing an external recommender system?

The most challenging aspect is to obtain the data. Unlike Steam, I only have limited access to non-sensitive information for some users only. Since there is an API limit of 100,000 calls per day per Steam user, it really took time to download the data I need. Fortunately, a few of my friends loaned the API keys associated with their Steam accounts to the project.

It is also very difficult to test the recommendation system objectively, as there are technical limitations to compare my results against Steam’s based on external metrics such as Click-Through-Rates. I have therefore resorted to internal tests using a metric to measure recommendation precision based on a research paper in 2011 by G.Shani and A. Gunawardana. The “Precision at N” measure suggests my recommendations has a score of 45% compared to Steam’s 27%. This provides some evidence that my recommendation engine is better from a particular perspective, but more needs to be done to validate this via user experience.

What has been your feedback so far?

User feedback is positive, with some people who really likes the site and the recommendations. There are already some feature requests too, such as recommending games on a members backlog, i.e. the games that they have bought but didn’t get around to trying yet. I’m aiming to improve the site in my spare time based on the feedback I get.

How was your time at Data Science Retreat?

It has been a gruelling and challenging 3 months! I have definitely gained a lot out of it, not only the technical bits but also the business and communications aspects of data science as well. DSR isn’t going to transform me into a magician overnight, but I am confident that I can learn new techniques very quickly, building on the firm foundation and the broad set of skills I have developed during the bootcamp.

Thanks Kevin!

If you are a video game fan with a Steam account, feel free to check out Kevins’s project at NextVideoGame.com.

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The futuristic idea of shops knowing your age, gender and race when you walk into a store and targeting products accordingly is becoming a reality. Real Life Analytics, a MassChallenge startup based out of Boston, have developed computer vision software which can determine your demographic profile within 20 milliseconds of you walking into a store.

This idea has left a sour taste in the mouth of some consumers. A similar venture, SceneTap, which aimed to use computer vision to tell you the age and gender breakdown of patrons at a bar you were considering visiting, found themselves having to write an open letter to defend their technology, in which they stressed nobody’s privacy was being infringed.

This is something Robert DeFilippi, Real Life Analytics’ co-founder, was also keen to highlight. “We don’t take photos, so we don’t know who they are. … We detect all these features without anything to go on,” he told BostonInno. All processing is done in-memory, and stores receive an anonymised breakdown of demographics at the end of the day, without photographs. “It’s not quite facial recognition but facial detection,” DeFilippi remarked.

Giving stores a breakdown of who’s walking through their doors each day is phase one of Real Life Analytics’ master plan. The plan is to sell the service to retailers on a monthly subscription basis, with stores in the Boston area having already confirmed subscriptions. Phase two is real-time targeted advertising, where stores can pick which adverts to display based on the particular characteristics of the shopper walking past.

However, those plans are a little way off- the startup is currently bootstrapping with $10,000 to their name. They hope to raise a seed round in the autumn, to make their dreams of the ultimately personalised shopping experience a reality.

Read more here.
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In an effort to improve its response to buying trends and costumer preferences, sportswear giant Adidas
is now starting to use the MicrosStrategy’s analytics platform which is based on SAP’s cloud-based in-
memory computing platform HANA.

Michael Voegele, Vice President of Global IT and Head of Enterprise Architecture at Adidas Group, phrases
the company’s aim like this: “We want to get more social competitor insights from the web, combined
with our information gathered through our backend systems and other environments.” According to
Voegele this will be achieved through big data analysis: ”In-memory computing, Internet of Things, all
of this we use for strategic big data and actionable insights. That’s what we see as part of a solution to get us closer to our consumers.”

Voegele raises the question “How do you convert from the old style financial reporting towards something that helps you predict what is going to predict what is going to happen in the marketplace,
going to predict what consumers will like, going to influence the consumer with regards to their
purchasing decisions?”

The MicroStrategy analytics platform will serve as the front-end of a combination of four different
data warehouses. Its’s data is collected from the company’s traditional databases, costumer information
that is gathered from their CRM platform and core data sourced from Hadoop clusters.

Voegele explained that Adidas wanted to embrace a faster approach to actually delivering insights to its
business partners, so it sought to provide its global outfit with more BI self-service capabilities,
enabling them to create their own dashboards. The company also prioritised making its analytics
available on mobile platforms. This way the company hopes to communicate and work more
effectively with it’s business partners and customers, as well as to become more flexible in response to
its competitors.

The geographical distances between Adidas and its wholesale, retail and online customers have grown,
some spanning as far as 80.000 km. But the brand is not planning to lose the direct connection to its
individual customers. Describing the nature of the project, Voegele says “Clearly we do see BI as the
foundation to understand each and every consumer – but we’re not talking aggregates. We don’t want
to look at aggregates, we want to look at the single and consumer and make sure he gets a great
experience with Adidas group.” Clearly stating the aim of this project, he concludes: “And hopefully,
at the end of the day, that helps bring us closer to our consumers.”

Read more here.

(image credit: Oscar Chavez)

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Bol.com is an online retail portal based out of the Netherlands. They sell over 8 million products, stocking everything from entertainment to electronics, from books to jewellery. Recommending the perfect product from this vast range can be like finding a needle in a haystack; we caught up with Barrie Kersbergen, Bol.com’s Expert Software Engineer at Berlin Buzzwords to find out the science behind the perfect recommendation.

Could you start by telling us a little bit more about yourself, your company and your work.
I’m Barrie Kersbergen, and I’m an Expert Software Engineer at bol.com. I have a background in Computer Science; I worked at the University of Utrecht for 12 years, where I was developing mathematical educational software at the Freudenthal Institute. In 2010, I thought it was time for something completely different, so joined bol.com, as part of the team which specialises in personalising content for customers. One of the systems I helped to design and built was the recommender system that aims towards personalizing content and recommending the ‘right’ products, in other words products that inspire customers.

And how do you recommend the right products?
I’m not claiming that I have the ultimate answer, but I have developed some strategies for recommending and inspiring customers. Most data scientists carry out offline evaluations- so they test how well they’re able to accurately predict behaviour through their algorithms compared to the true behaviour of the customers on their site. This has its advantages but if you’re able to predict something, the customer on the site probably already had the intent of doing that anyway, because they came to the site for a specific reason. So if I recommend a book to you which you were already going to buy, the recommendation adds no value.

At bol.com we carry out such offline evaluations because we want to know how accurate our predictions are, but we also do long-term analysis in order to find out what the impact is of the items that we show you in the long term, that is in multiple visitor sessions. What we certainly want to know is: Do visitors return in the long term?

Another thing we do is live-user experiments. Behind the scenes, without visitors knowing, we are experimenting to see if new algorithms and parameters are performing better, and adding more value to what our visitors are doing right now on our website.

What technologies are you using for this?
We use Hadoop for batches, and we have our own custom-built technology for the real-time part. We’re also evaluating new technologies which could add value for us in terms of doing real-time calculations. The difficulty with real-time is that we only have a limited time window in which we can recommend items to the users. What we can’t do is say to the visitors “Please wait five minutes, then we’ll calculate the ultimate product for you”. We only have a few milliseconds- approximately 80 milliseconds to do custom made recommendations. In this time span we have to calculate the most relevant recommendations and decide how to present them to the customer. This is something we are currently doing with custom technology, and in the future we want to do deeper analysis using more data, and we’re looking into which technology we should use for this. At the moment, Storm and Cassandra seem like viable options.

How important is visualisation for your customers? What work have you been doing to optimise this?
I tend to think it is important- if we don’t show a product image, and just show a box with “No Image”, that’s not a good idea. But the optimal visualisation is also in itself a kind of recommendation. So for some customers, we show really technical details, because we have concluded with statistical analysis that showing technical details really adds value to that customer’s experience. But some other customers get confused by all of the details- they might think “I just wanted to buy a pair of pants, why do I need to see all of these details? I don’t know what they mean, it makes me nervous!” So if you get the balance between showing the right combination of product attributes right, it can really add alot of value. This is something we’ve proven this using live-user experiments.

So aside from getting the recommendations in real-time, what are some of the other challenges faced by bol.com in terms of getting the recommendations out there?
The main challenge is the whole puzzle of personalising the full website. We can recommend almost anything; we can recommend products, but we can also recommend search queries. We can also recommend categories that might be interest to you and we can recommend you trending items within these specific categories.

There’s also the issue of how often you should repeat a recommendation. It might be a missed opportunity; you may have missed the recommendation because you were looking at other things, and didn’t notice it. How should we interpret this? Does it mean the recommendation is flawed? Or does it just mean that the visitor haven’t looked at it? In my opinion it’s ok to repeat the recommendation once in a while, but you shouldn’t overdo it because then it could get really annoying to the customer. So managing this is the tricky part.

So what we’re basically doing is recommending algorithms to you, and the outcome of the algorithms are shown to you. Then, we measure the effect on you as a customer. So if you’re clicking on and interacting with these recommendations we’re probably doing a good job, because we’re inspiring you and adding value to your website journey. But if you’re not interacting then we’re not doing such a good job, and it might be time to switch strategy, and try something new.

Trial and error?
Though experimentation we improve our software, this could be seen as a form trial and error, however it’s not entirely random. But this means we sometimes do things we think are irrational, but may add value. For instance, just showing a random recommendation algorithm on your profile and seeing if you like the outcome.

Has this yielded any surprising successes?
I’d love to say that it has! But sometimes we’re not entirely sure what the real intent of the customer is, therefore it is difficult to measure whether a change of strategy is more or less successful. Lots of visitors are anonymous to us so we have little information about who they are, and we don’t know what their intent is on our website. So we need to figure all of these things about in a few milliseconds’ time, which is quite challenging. So what we don’t want to do is do lots of calculations and miss our window of opportunity, because then the customer will see fallback content. So we want to make sure that we get the deadline.

Moving forward, what is bol.com working on right now?
What I want to do in the future is have more data available to do realtime analytics on. Why did specific systems show content to you on our website, for instance, what did the search engine need to do to show the search result? Because all of the metadata involved tells me something about why you were shown these specific products and attributes. Combining all of this data opens up new opportunities for us- I don’t know what these opportunities will be, but we will definitely incorporate this.

This means that we will need to have more computational power, which is why we need a real-time framework that simplifies working on larger datasets in milliseconds and help us out with the low-level infrastructure. Because what we don’t want to do is spend alot of time of the low-level infrastructure, because it’s not our core business- it’s fun from a technical perspective but it doesn’t add value for what we’re trying to do. We’re trying to inspire and personalise content for our customers.

Moving forward in terms of the larger picture, where do think big data is headed?
Big data is just there, it’s just available for everyone. So we started doing using big data in 2009, it was hi-tech somewhat unstable and it was all new and nobody knew what to expect from this. Now it’s all commoditised. Everyone is using this in our company, without even knowing that it’s special- we just use the technology without even caring that it’s supposed to be special; for us it’s just mainstream. Within 3 years, every company will be using this technology; it will be accepted as the norm next to the relational databases.

That’s interesting to hear; alot of people are talking about “Big data taking over”, but at bol.com it sounds like it was just part of the process.
Yeah, it was just part of the process- it gives us new abilities, it’s affordable technology, but distributed computing is very old. We call big data “commercially-applied distributed computing”; it’s nothing special. It’s been around for 30-40 years. However, now it’s affordable- even with a small budget you can install a new cluster and start doing distributed computing. Also, the technologies themselves are much easier to use- in the past, you really needed expert computer scientists to develop this software; now, your average Joe could program this and do these things for you.

Bol.com is an online retail portal based out of the Netherlands. They sell over 8 million products, stocking everything from entertainment to electronics, from books to jewellery. Established in 1999 with 26 employees, it’s since grown to 650 employees and become one of the largest European online retailers.

Avansera, a Finnish consumer analytics startup, just won the 3rd place in the EU Idea “Smart Spaces” Challenge, a series of pitching competitions organised by EIT ICT labs with over 800 entrants from across Europe. They won €15,000, as well as coaching and mentoring from experts of the EIT ICT Labs Business Development Accelerator, integration into future EIT ICT Labs Action Line Activities, and office space for up to 6 months in one of EIT ICT’s Co-Location Centers. This puts Avansera well on course on their mission to disrupt the retail industry by offering detailed reporting at a price point that opens up the market.

Their business model is two-fold: First, they offer a free app called Genius Shopper, which allows consumers to shave up to 30% off their food bills by showing them the best offers and cheapest deals for items on their shopping list. Data gathered through Genius Shopper allows them to find patterns in customer behaviour metrics, such as brand loyalty and price flexibility, which they sell back to the food industry. Their 6-month beta trial garnered over 50 million data points, giving food production companies insights into their customers that weren’t previously available.

Managing Editor Rohan Patil talked to CEO and Founder Cormac Walsh about Avansera’s business, market challenges, and plans for the future:

With the recent furore among cab drivers about the growing prominence of Uber in London, Paris, Milan, Madrid and Berlin, we thought it would be interesting to see how the mobile-app, car-for-hire company uses big data to better its services. Below are 2 ways Uber has used data to understand the behaviour, location, and preferences of their customers, as well as interesting correlations between crime rates and Uber usage. We were surprised by the results, and thought our readers might be too.

1)   Prostitution and Alcohol Promote Uber Usage

A few years ago, Uber’s team of data scientists (only three at that point) were trying to understand how to effectively manage the positioning of cab drivers in order to allocate them to the busiest areas at the right times. Of course, for Uber, this activity is crucial – by knowing that there is a Nicks game on a Saturday, for example, the company can have a line of cab drivers in the area to match the inevitable demand. At its core, this is quite unsurprising; Uber’s business model is predominately based on customer satisfaction and if customers are left waiting for a cab, Uber gains a bad reputation.

But therein lay the problem: what factors does one consider when deciphering why an area is busy? Looking at population density, the number of bars and business, and where people live in a given area could be one solution. However, what Uber did was look for something quite unusual. After they crunched some data and understood the areas and neighbourhoods that were most busy in New York and California, they decided to look for something more specific – crime.

What they found was that those neighbourhoods where crime – prostitution, alcohol, theft,
and burglary – was most prevalent, were also the neighbourhoods most positively correlated with frequent Uber trips. More specifically, the team at Uber found the most
heavily correlated crime was prostitution. What’s interesting is that prostitution had a considerable spike on Wednesdays, which happened to be at the same time Social Security and welfare checks arrived.

Of course, the team were quick to point out that this effect most probably reflects population density in terms of where people socialise, and that it in no way suggest causation. However, Uber’s Data Evangelist, Bradley Voytek did comment,

“This one of the coolest things about working for a data-driven company like Uber: on the surface we’re a transportation company, but below the hood there are so many ways to look at our data…This finding is a perfect example of the fascinating insights you can get when you combine big, seemingly disparate datasets. By trying to figure out how to predict where to position our cars, we got a peek at the ebb and flow of the life and crimes of San Francisco”

2) Calculating the The Walk of Shame

Almost a year after the aforementioned case study was conducted, Uber released another interesting post about the infamous “Walk of Shame,” which was renamed by the Uber office as “The Ride of Glory” (RoG). Now, to understand the definition of someone who takes “The Ride of Glory”, Voytek says,

“A RoGer was defined as anyone who took a ride between 10pm and 4am on a Friday or Saturday night, and then took a second ride from within 1/10th of a mile of the previous nights’ drop-off point 4-6 hours later (enough for a quick night’s sleep).”

After analysing the patterns of RoGer’s in San Francisco, New York, DC, Seattle, Chicago, NYC, the following results emerged:

Boston, as witnessed, was a clear winner — they had the most RoGer’s of the group.

But what about the correlation between certain days of the year? Uber looked at this too and surprisingly found that “brief overnight stays” dropped considerably during Valentines Day, whereas there was an influx in cab hiring on tax day (April 15th). Both are equally interesting, but the latter more so. According to Voytek, “an influx of cash might be making people more…frisky”).

“The areas that were most populated with RoGer’s in California”

Finally, Uber wanted to understand if there was a correlation between Rides of Glory and gender. They found that the greater the male/female ratio, the more likely for that neighborhood to “spawn” a Ride of Glory. As Voytek aptly concluded,

“You people are fascinating”

For more blog posts related to Uber, visit their blog here

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(Image Credit: Samantha Jade Royds)

IBM recently announced that Matrimony.com, one of India’s leading marriage matchmaking conglomerates, has begun using IBM Big Data and Analytics technology in order to create better matches for their subscribers. With over 3 million users across hundreds of micro-sites, Matrimony.com has no shortage data points with which to pair up their users.

The information comes from a range of sources including customer emails, telesales information, banner ads, and SMS activity. They have turned to IBM ExperienceOne and SPSS predictive analytics software in order to help achieve their goal of delivering near real-time results to those looking for a potential partner.

“Today, we see an increased ability to process and interpret all the information effectively as well as to cater to our customers’ customized needs efficiently. No doubt, that the online business industry will continue to flourish in the years to come with technology that not just helps cater to current needs of the market but helps augment growth…IBM’s solution will help us push the boundaries” – Jayaram K Iyer, Chief Strategy and Analytics Officer,   Matrimony.com [source]

Key to the success of Matrimony.com’s data strategy is understanding the user’s sentiment and social norms in India, which play an obvious critical role in matchmaking success.

“Creating insights from the vast amount of data being generated today is key to every industry or sector,” said Jason Mosakowski, Director Software Sales and Marketing, IBM India/South Asia. “This is especially true when it comes to clients in the online domain. [IBM’s] solution will help them better use their data for insights and deliver integrated marketing messages to target subscribers better, ultimately helping them match more potential partners.”

Read more here.

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Tailored retail experiences are one of the most well-known applications of big data. We have premium streaming sites that know what you want to watch next better than you do; book vendors which can accurately map your literary tastes; and now, an e-commerce lingerie startup which knows if you suffer from strap slippages or uncomfortable underwire- and also has the perfect bra to combat these problems.

Even if you’re not in possession of breasts yourself, it’s unlikely you haven’t encountered someone wrestling with an errant strap, or discretely trying to push up or readjust. The problem isn’t badly made bras; the problem is there are so many different types of body shape that rib cage and cup size alone don’t tell the whole story. This is something that True&Co, who have currently identified over 6,000 different body shapes, is very familiar with.

In the beginning, True&Co started out as a bra recommender. First-time users would take a two-minute quiz, telling True&Co if their bras were too tight, or if they had problems with “busting out” (apparently 62% of women do), and True&Co would use these metrics to recommend bras for a customer’s particular body shape. Now, they’re using the 7 million data points they’ve accrued to design bras tailored to their users’ body shapes. As founder Michelle Lam states: “With all this virtual stuff, it’s so easy to create a uniquely personal experience for every person, but creating physical goods that also feel like they’re made for you is what’s incredibly fascinating to me.”

The bras, using a patented fitting system called True Spectrum, are variable far beyond the usual remit of chest width and cup size. They take into accont if a customer’s breasts are full or shallow, high or low, wide-set or close together. These bras have quickly become True&Co’s bestselling products, accounting for a quarter of all sales and boosting revenue 600% in the past few months.

Victoria’s Secret have also established a quiz for their clients, and startup ThirdLove have developed a body scanning app for getting measurements. But in the future, we may see tailored fashion moving beyond the chest region. Many have fallen fowl to buying an item of clothing over the internet and immediately returning it, realising it doesn’t look half as good on their body shape as it does on the model. Tailored clothing recommendations could change this, something that Lam is aware of: “I look at the old retailers out there, and I see an imperfect model,” she says. “I think this is the way women are going to shop for intimate apparel in the future, and not only that, but I really believe this is the way women will shop for all apparel in the future.”

Read more here.
(Photo credit: Melissa Maples)

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When it comes to marketing a product, Amazon ratings can make or break a business. I’ve lost count of the times I’ve been talked in to (or out of) buying a product based on a passionate review or a high or low rating. Nithyanand Kota spent alot of time contemplating the significance of Amazon ratings, asking questions such as: Can a user actually differentiate between 1 and 2 star ratings? And why do certain items have bipolar ratings- is someone gaming the system or is it a politically charged item?

So began Kota’s “fishing expedition” in to Amazon rating analysis. For his research, Kota used the Amazon Annual Bestseller list, which contains the 100 best selling books on amazon for the past 19 years (beginning in 1995).

Here are some of his more interesting findings:

1. 2-Star Ratings Are Pretty Uncommon:

Kota’s dataset contained close to 2.18 million ratings. The average rating was 4.15- unsurprisingly high, considering he was analysing bestsellers.

The most interesting finding in this graph of normalized ratings is the relative lack of 2-star ratings. Kota attributes to this to ‘the nature of internet feedback’- we’re more likely to take to the internet to give an extremely disdainful 1-star review than a mildly disappointed 2-star review.

2. Amazon Ratings (and Maybe Internet Book Sales on the Whole) Are Still on the Rise

The number of ratings given per year is on the up, perhaps indicating internet book sales are continuing to climb too. As Kota notes: ‘One can also analyze the total number of ratings given for books in the list (for each year) as a function of the year. This metric serves as a proxy for number of people buying books over internet.’

3. 9/11 Had a Noticeable Affect On Literature Consumption

Looking at the same graph, you’ll notice two obvious dips. One is for 2013, which Kota assumes is due to the fact the titles are newer and therefore less widely-read and reviewed, and this should correct over time. The other dip, completely disrupting the otherwise monotonic increase, Kota attributes to a ‘post 9/11 reading lull’.

4. Everyone Loves Dr Seuss- But Opinion is Split on Tom Clancy, JK Rowling and John Grisham

Kota’s table of the worst-rated best sellers of each year features some pretty big names, including Rowling, Grisham and Clancy. Kota poses the possibility that so many novels end up on this list because fans of novelists lack the ‘religious zeal’ of fans of political/spiritual books, and are less likely to take to the ratings board to defend their favourite works against the detractors.

The best-rated bestsellers list is alot less diverse- which makes sense, as you would assume highly-rated bestsellers would continue to sell well after the year of publication, and continue to garner positive reviews from new readers. Dr Seuss’s ‘Oh, The Places You’ll Go!’ appears as the highest-rated bestseller on five seperate ocassions. Whereas the lowest-rated table was dominated by novels for adults, the highest-rated is mainly populated by children’s and young adult fiction, as well religious publications.

5. Controversy and Poor Ratings Go Hand-in-Hand

Kota created a Controversy Index, given by C.I=(number of 5 star votes /number of 1 star votes)+(number of 1 star votes /number of 5 star votes). C.I is of the form x+1/x and can obtain a minimum value of 2 for a positive x.

Above is a table of the books that most divided opinion for each year. Kota assumed on campaign/election years, the most controversial books would be political- but this is only the case for 2002 and 2004. Indeed, some of the most divisive titles are considered modern classics, such as Joyce Carol Oates’ We Were the Mulvaneys– and, of course, Pride and Prejudice and Zombies.

Also, in four of the years (2006, 2008, 2012 and 2013) the most controversial bestseller was also the worst-rated bestseller- suggesting being controversial and divisive isn’t necassarily healthy for your ratings.

You can find out more about Kota’s study here.

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Posiq, a company that aims to help restaurants enhance customer relationship through big data, announced yesterday that it received $3.2 dollars in Series A investment. Thayer Ventures led the round, with additional participation from SVG Partners and select angel investors.

“The restaurant industry is inundated with loyalty gimmicks, email or text blasting programs, and mobile apps that do not deliver meaningful results,” said the founder and CEO of Posiq, Rick Onyonm. “Posiq leverages big data and advanced technology to help restaurants communicate and engage in an incredibly personalized way. Just the right message, to the right guest, at the right time. This simple approach vastly improves the guest experience and grows restaurant revenues at the same time.”

Founded in 2012, Posiq’s CRM service uses its cloud platform to connect with existing PC, tablet or cloud based Point of Sale systems that are already in use by the restaurant industry. Mobile technology is also a technique used by Posiq to engage restaurant customers each time they visit by learning their purchasing habits, brand and dietry preferences, as well as their demographic and social profiles.

The managing director of Thayer Ventures, Chris Hemmeter, and founding partner of SVG Partners, John Stanton, will be joining Posiq’s Board of Directors. The investment in the company will go towards product development and expanding Posiq’s sales channels.

Read more here

In 2012, Americans watched more legally-delivered video content via the Internet than on physical formats such as DVDs. This shift in format also allows online content providers to gather large amounts of data on viewer habits.

Recommendation engine

Netflix is the de facto iTunes for online video content. It collects data on every search and every rating entered into the system. Netflix user logins allows further data enrichment with verified personal information (sex, age, location), as well as preferences (viewing history, bookmarks, Facebook likes). Third-party data providers, such as Nielsen, adds an extra layer of data on top.

In the same way Amazon crunches all that data to make book recommendations, Netflix makes movie recommendations. The better recommendation engine, the more content gets watched. Having details on when and where content is viewed also means Netflix knows when and where reminders best be sent.

Content creation

Having detailed knowledge of Netflix subscribers allowed the company take the next step of creating content. The data collected by Netflix indicated there was a strong interest for a remake of the BBC miniseries ‘House of Cards’. These viewers also enjoyed movies by Kevin Spacey or those directed by David Fincher. Now add a $100 million commitment, and you get two seasons.

Next steps

Online delivery of the video allows Netflix to log every event. Every time the viewer presses play, pause or repeat. With hundreds of millions of events, Netflix could create a playbook on how to create TV shows. When to show an explosion, when to show a sex scene, or even when to have a product placement.

Netflix is even capturing screenshots to capture characteristics such as color and scenery, and how well the audience responds to such imagery. These details could effectively govern the creative direction, on what’s being shown when. However, with so many details determined by algorithms, can it really still be described as a ‘creative direction’?

Read more here.

This week, we feature the best articles on limitations of Big Data. In particular, Tim Harford makes an excellent case on why correlation does not equal causation.

Image credit: David Erickson

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We met up with founders of GPredictive. GPredictive ´s mission is to provide customers with solutions that make decisions based upon data as easy as possible.

http://www.youtube.com/watch?v=7aqZX8kFBXU

Who are you and what are you doing?

We are G-Predictive, a big data start-up. We want to give access to everyone to the big data driven decision making processes. To widely distribute this and make it available to everyone is our mission.

How did you come up with the idea? 

Our company started in 2009 as a consultancy company to aide large companies and corporations in analytical questions.  In 2010, two of the founders were finishing one last model, one last prediction, one last prognosis, one last forecast and thought to themselves ‘there has to be a better way to do this.’  That was the catalyst for us where we realized we need to change something about our business’ philosophy.

Then the idea came to develop a product.  The situation, when we came to businesses tasked as consultants, was that we were often confronted with statistical software – which involves paying for large licensing fees, up front, so that means there was a big investment made already.  There are other technical issues around the statistical software, and the infrastructure necessary for it to run, as well as the people with the skills necessary to work with this and maintain it.

What do you with data that you collect? 

 We look at the data, establish a model, determine patterns in the data, and in the end provide a prognosis for the business customer.  This is a long and expensive process that in the end comes up with only one result, after which the consultants disappear again.

Our approach is that we essentially offer all of that as a service, where the companies pay us on a monthly subscription model instead of buying all the software that they can’t really use on their own.  They don’t need to buy statistical software or collect data or have statistical proficiency.  We provide the data-driven decisions at the date and time that our customers are making their marketing and distribution decisions.

In this day in age there is so much that can be forecast, from the areas of health and finance and politics, where we focus on marketing, distribution, and on customers.  There, our main goals is how to keep customers, gain new customers – the retention and return of customers – and trying to minimize the amount of money spent on gaining new customers and the like.

How do you compare yourself to the other companies in this field? 

We focus on some very specific fields in the distribution and marketing areas.  We want to allow business men and women to make data driven decisions on a daily basis.  We are very specific with our mathematical algorithms, but this isn’t what we believe makes our product unique.  The main point, for us, is to integrate this into the daily use in businesses.  The data will become better and better and should create a definite lift in comparison to the procedures being used currently.

What is your revenue model?

We usually start with one or two test campaigns for our customers.  Just to briefly illustrate one to you:  for a mailing for a specific product, we pick a certain number of potential clients for a business and they pick the same number of potential clients as well.  In the end, what ends up happening is that the potential clients that we chose are four times more likely to make a purchase than the potential clients the businesses themselves chose.

Our conversion rate greatly beats theirs, where 400% more purchases is not a difference that can be ignored.  From there on, the business can purchase our services on a monthly basis.  From there, we hope to set up automated exchange of data with which we can offer live recommendations for their business.

What are you looking for at the moment:  investors, customers, or talent?

All of the above. We are looking for data scientists at the moment.  We sell our product as an aide to the decision making process for people, and people, in the end, may have some more information that the machine doesn’t have.

But before we get there, a person needs to gather data, develop the models and more, so thats where the data scientists come in.  Talent is what we need most, but we are also looking for investors and customers.  We are in contact with some investors who are really supportive and also challenging us to take the project further because they are fascinated with and very hopeful for what we are creating.

Where do you see yourself in one year?

So much can happen in one year!  We are currently six full time members working here, in our extended circle we are 15.  In one year we would like to really scale our business and product, something that we are constantly working towards already.  We are trying to get established in Germany first, but logically we can expand past the border because our product is not language specific.

GPredictive´s mission is to provide customers with solutions that make decisions based upon data as easy as possible. GPredictive´s customers don’t have to worry about  raw data and the statistics which leads to good decisions.

We met Cormac Walsh, the CEO and Founder of Avansera at CODE_n contest in CeBIT, Hannover. Cormac gave us a fascinating insight into how Avansera is disrupting consumer analytics for FMCG companies and retailers.

 Who are you?

My name is Cormac Walsh. I am the CEO and founder of Avansera. Avansera is a Finnish big data start-up which was founded in February of last year.

What makes Avansera unique?

What makes this unique is that our users are exposing us to what they’re going to do.  This is the new data we are collecting.  They’re are indicating future intent through the use of a highly detailed and   very smart shopping list application. And while typically big data and statistical companies will apply modelling and mathematical techniques to predict the future, for us, on a seven day forward-looking model, we’re not making any predictions. We actually have demand figures by location. And that’s kind of cool and more importantly, extremely useful.

Could you tell us a little more about your product?

Sure – so the product is a service, an end-to-end service.  We offer applications which mobile app users use. These are personal productivity applications which help them save money on their grocery shopping or save calories on their product selection in terms of their grocery baskets. It helps them optimize what recipes are cheapest for them based on what they have at home because we have a view of their home food inventory. And importantly, all of these applications are free to use for our users.

 Now this is key.  As they use our applications, consumers expose a behavioral chain of events about various decisions they make, whether conscious or unconscious.  Just to name a few, these include how people make decisions about products, how people arrive at product choices, how brand loyal people are, how price flexible and how price-sensitive they are.  We can even see how people react to in-store geography.  We look at this information and then we take big data and turn it into small data.  In the process, we actively get rid of as much of it as we can and eventually, we sell tablet and smartphone tools, on a revenue paid basis, to the food production industry.

These are tools which crucially are easy for marketers, brand managers, and category managers to consult in near real-time with regards to how the market is reacting in their category, with their products, or with their competitors’ products. So, for example, if one of our customers, or if one of our industry customers, is in a grocery store just doing their own shopping and they see a competitor has a marketing campaign, they can take out their smartphone and query to see how this has campaign having an effect on their own product? Is the market changing size or is just their share changing size?

Sounds fascinating. How did you come up with this idea?

Personally, I have a background in telecoms.  Working for Nokia in the past, I frequently received requests from colleagues in Southeast Asia asking for information on their customers:  who should receive special offers?  Who should I keep as a valuable customer?  Which customers are nomadic?  In effect, all these answers can solidly be found in the data.  But, if you’re collecting massive amounts of information on a daily basis, there is no way to trawl through all of that while you are also trying to plan marketing campaigns and the like.  Relevant Answers to Simple Questions!

What then becomes necessary is a really, really easy and concise way to ask simple and direct questions and receive to-the-point answers.  What I realized from there is that all marketers, in any form of fast-moving consumer interface need access to this kind of data.  The big data industry needs to change the way it presents information and go beyond mathematical curiosity and move towards pure business benefit.

And that’s exactly what we’re doing in Avansera. We’re effectively dumbing down our big data.  We are making the data consumable and accessible. We’re democratizing it.  And in addition, what makes us unique is that you could compare us with legacy analysis companies such as Nielsen, who assemble a panel of people and talk to them to get opinions. The problem with this is that it is fraught with bad answers and extraordinarily expensive, so all-around inefficient. With our user applications, we’re providing something that is going to make their life better.  Then, we just sit back and passively observe what the users do.  This means that with regards to the behavioral information and the questions we’re essentially asking them, we’re getting absolutely honest answers. And, our cost of connection is orders of magnitude lower than our competition. So when the established industries sell a report for €100,000 about dairy in Scandinavia, we can sell the same report for €5000 and get a higher profit margin.

Are you looking for any funding or any special talents to hire?

We’re in a funding round at the moment.  And in terms of special talents to hire, I know exactly what I want:  translators.  And I don’t mean language translators, what I need are people who have an understanding of data, but also have an understanding of business and how to talk to stressed-out marketing executives. So I need people who have empathy, we need artists!

Where do you see yourself one year from now?

In a year from now, we’ll be present in all Scandinavian markets and we’ll also exist in Germany, in Poland, and in the Netherlands.  The reason we can do that is because our service is easy to internationalize. We call our hosting provider and we say, we need more capacity.  Then we localize the user applications and our commercial applications into local language variants. We get a local country list of all product barcodes, which are available to industry companies like us. And then we commence the collection of price information. And we’re in a funding round at the moment. We are VC (venture capitalist) backed in Finland.

How was your time at CODE_n, CeBIT?

I loved it.  It’s absolutely great because it’s very rare that you can be in a roomful of people who speak the same technical language.  And while there are companies here who I can honestly say are competition for me, the market is so enormous that there can be 100 times more competition and it doesn’t matter, we still have enough market. But the market itself is growing at a rate faster than new companies are coming into the space.  I think the biggest risk that this entire industry faces is lack of skills.

We met Cormac Walsh, the CEO and Founder of Avansera at CODE_n contest in CeBIT, Hannover. Avansera delivers the most advanced real-time action, location and performance analytics, and targeted digital marketing services to FMCG companies and retailers. 

Loyalty Cards status quo

In the world of business transactions, consumers and merchants alike are continuously and relentlessly inundated, the costumer with cards, rewards, points, friends programs, and the like (see Tesco) to incentivize, the vendor with programs, numbers, tools, spreadsheets, and more with the intention of informing. Every card, every pin number, every loyalty membership for a particular store is just one more item to keep track of and one more thing that gets lost in cluttered drawers while the prestige of being a member is a quickly waning one with the prevalence of every corner store now setting up its own customer programs, be it even something as simple as a punch card that allows you a tenth drink free when you have already purchased nine at the full price. How is this new age problem going to be solved? By turning our cards into just another app, another side effect of the smartphone? It sure looks like its headed that way, though we aren’t quite there yet. At the moment apps just add a layer of complication and extend transaction time needlessly. For the time being, the answer seems to be to simplify, simplify, simplify. Now Buzzoek is doing that all with one card, one card that the user already possesses.

The idea was born out of frustration with current solutions

The idea to strip down all the complications and logistical troubles associated with having a plentitude of cards of all the same shape and size, tangible objects so easily forgotten, then lost, to a back corner of a disorganised desk space comes from John Staunton. After gaining experience working across widely different fields, he co-founded Buzzoek a year ago. Buzzoek aims to address various angles of customer loyalty and payment options. Primarily, the focus for consumers is on simplicity: simplicity in engaging with merchants, as well as simplicity in being rewarded for loyalty.  For merchants, the main point is to turn the transaction which is the antiquated crux of the shopping experience into so much more than that: an enjoyable and above all memorable experience. Every business will be able to see this realised differently to their own specifications, but general direction and guidance is no doubt possible. Payment providers need to find new models of how to distinguish themselves to deliver exceptional service across the board. Buzzoek also provides the stores using their technology useful insights to help them understand and differentiate their customer base along new lines allowing them to better serve their customers and grow their business.  Meanwhile, the customer is able to use one card for all rewards and loyalty points, making every visit a fun experience, instead of a laborious chore.

Online and offline converging

There is a growing paradox between the anonymity of sitting at home, shopping online, yet receiving a hyper tailor-made experience, while going to a brick-and-mortar retailer where customers are treated generically and interaction is nothing close to the custom-fit ease consumers have grown accustomed to. With declining sales numbers in the physical stores becoming a continuous trend, and consumers shifting their focus to the world of online purchases, retailers need to offer their customers something more than just a transaction, some interactive experience that it is – as yet – not possible to replicate in the cyber world. This means daunting changes ahead for merchants, particularly in the realm of how retailers act and interact with their customer base, not least among them online, tying their shopping experience in with social media.

On the customer side of things, the consumer is overwhelmed with the aforementioned myriad of point systems, rewards clubs, membership cards, and loyalty promotions. In reality, these loyalty cards and the like seem to not further any brand loyalty, but rather are just part of the towering wave of entirely irrelevant offers. Offers that mean nothing to the consumer and need to be painstakingly sifted through to find something actually pertinent and personally valuable.

The services working together with business owners – providing everything from payment processors, internet and phone service, marketing, merchandising, brand agencies, etc. – must also adapt and expand their services to maintain their status with businesses, by providing new and more convenient value-adding services. With more and more shopping moving online, providers including Square, PayPal, Adyen, etc. have changed the playing field, and if older service providers want to stay in the game, that means to evolve to suit the changing needs of today’s consumer.

One way that payment-form can change, is that it merges to become one with loyalty rewards systems. People use and love rewards and the shopkeepers providing them profit from building relationships with their customers.  However, in recent years the urge to modernise undermined the need for simplicity and streamlining, resulting in messy rewards apps that involve several steps to complete, instead of stripping down the process to its bare essentials and minimising time and effort spent on rewarding return customers. Even large corporations, which could afford to invest and develop sophisticated loyalty card solutions, have not done so, reverting to using either archaic stamp cards or convoluted mobile apps.

How to make loyalty programs convenient

The question then becomes: How can vendors receive complex and multi-faceted information and insights about the businesses they are running in a simple way? How can consumers receive offers pertinent and relevant to them rather than a generic one-size fits-all discount? How can the payment system evolve to meet the changing needs of today’s customers?

According to Buzzoek four requirements need to be met:

1. A solution for today’s merchant needs to be very low cost and have minimal installation effort – including any hardware installation and loyalty program creation and set-up. We’ve managed to get it down to under 2 minutes.
2. Shopkeepers need help & hand-holding to create a customer interaction approach that suits their business (whether giving rewards, helping charity, or creating memberships) or something completely different. Here, having a consultative salesforce rather than pure salesmen makes all the difference.
3. Businesses should have the option to work together to create offers – for example, buy a coffee and get a discount in the hairdresser next door… but they shouldn’t be forced to. Flexibility is paramount in any solution.
4. The online and offline world need to be seamlessly linked, providing a consistent multi-channel experience. Embrace online, don’t fight it.

Merchants care about actionable recommendations

For merchants what matters most in the end is not a complex graphical representation of the data on hand, but rather what actions need to be taken. This does in no way imply that the analytics or algorithms applied would be simple, only that their advanced data is reduced of its apparent complexity and presented in a clear and understandable manner. Founder John Staunton learned through personal experience that more is not necessarily better when it comes to providing merchants with figures and graphs.  Instead, what is happening is once again an inundation in information;  merchants, too, want a minimalist approach, broken down into 3 actions points on which to focus each week, for example, and to be delivered the relevant information rather than logging into a platform and seeking it out among huge troves of other, undoubtedly interesting but less immediately personally relevant, information.

End users only need one loyalty card

Back on the consumer side of things, it remains of the utmost importance that loyalty cards don’t gather dust in corners, but are actually used. With Buzzoek the answer is simple: Use a card the customer already carries, such as a public transport card (e.g. the OV Chipkaart in the Netherlands) or any other kind of NFC device. They can be used instantly without registration, and can be scanned through the material of your wallet so you don’t even need to remove them from their holder. They can be used instantly without registration, and can be scanned through the material of your wallet so you don’t even need to remove them from their holder.

In keeping with the theme of convenience and ease, it is possible to download an app or register via a consumer dashboard to access several other features, like tracking points, receiver personal offers, donating rewards via social media platforms or even gamifying loyalty. It is also possible to reduce paper waste and wallet clutter by providing consumers with e-receipts. Of course this is all a customer choice, and all optional, none of the information is required from the consumer.

Toeing the line between convenience and privacy is increasingly tricky in our connected world, especially since so much useful behavioural information is out there based on geo-positioning which could be used to look at customers in a much more meaningful way. Because of this delicate balance, it is important that customers always remain in control of how they are tracked and are able to opt out easily. Customers can remain anonymous forever, and still receive loyalty rewards, although the rewards are better when information is shared. This however is up to the customer to decide, not for anyone else to dictate.

Addressing untapped potential of behaviour-based offers

The information collected should be used to provide customers with behavior-based offers and benefits, all of which will ultimately be combined in one step with payment, rewards, coupons, discounts, gift cards, vouchers, etc. Here payment providers have as yet untapped potential, since they know who spends what and where. However, passing on this information is something the public is still staunchly opposed to, so new possibilities may still open up here in the future. When it comes to Buzzoek, data is stored in a secure cloud and accessed by APIs, and though rapidly accessibly, strongly protected. However, there is the further option for consumers to set up a profile and attach an image to their card for visual identification confirmation.

All in all, with intent to simplify and enhance the transactions that still happen in brick and mortar stores, Buzzoek features experience improvements for the consumer and merchant alike. Exciting new developments to watch!

Follow Buzzoek on Twitter @buzzoek

John Staunton
Co-founder & CEO Buzzoek

John is a rocket-scientist that decided to take his engineering skills back down to earth, and he’s life-hacking the hell out of it. Don’t ever let him show off his guitar-skills. Start a conversation about rugby instead.

(Image Credit: Nick Webb)

Supermarket Giant Tesco pioneers Big Data: Turning Customer Loyalty into Royalties

                                                                                                          
Tesco Plc, the British supermarket chain, is currently the second most profitable retailer in the world with outlets in twelve countries. Although it began as a more down market grocery market, it began expanding its reach in the early to mid-90’s to include financial and telecom/internet services, as well as launching a loyalty card program. Thus Tesco began collecting ever more data on its consumers and was one of the first companies to embrace, and learn from, Big Data analytics. Thanks in part to this new approach Tesco managed to expand its market share by over ten percent in the coming ten years and re-accelerated its sales growth that had previously been floundering.

Beginning of the 1990s, Tesco faced numerous challenges to its existing business model that it needed to find a way to overcome. New Greenfield sites, which it used to develop into hypermarkets were difficult to come by and the competition through existing chains and new arrivals in the sector was also generally very strong. So in 1995, Tesco introduced the Clubcard, its own loyalty scheme. Most competitors used it only as a means to target discounts and coupons and so quickly abandoned the scheme as unprofitable. Tesco, however, realized the value of the insight it would be getting into its customers’ behaviours and now receives detailed data on two-thirds of all shopping baskets. Tesco was unable to process the flood of data that descended upon them and so very early outsourced the analysis to Dunnhumby, a company they would later buy a majority stake in.

The first step, however, was to segment the customers into appropriate groups. That resulted in two things. On the one hand Tesco could actually be more targeted in its mailings of vouchers and coupons (which worked: rate of redemption for coupons shot up from 3% to 70%), but it could also launch new product lines according to customer demands. Upmarket customers were targeted through product lines such as ‘Tesco Finest’, the health-conscious customers could now buy ‘Tesco Healthy Living’ and ‘Tesco Value’ aimed to entice the price-sensitive among Tesco’s customers. The mailings became more complex also. As data from Clubcard subscribers became more insightful the variations on the standard Clubcard mailing rose from a mere 100 after the fifth mailing to over 145.000 in 1999.

 Having broken down customers into segments, Tesco increased its reach by launching the Clubcard Plus, which had an integrated debit card. This was later replaced by a credit card but nonetheless lured customers into spending more at Tesco. Using all this data Tesco started trying to convert the non-buyers. For example, finding that recent parents were spending their money elsewhere, they launched a Baby club and ended up capturing 24% of the baby market.

Seeing that its analytics approach worked, Tesco started applying it to other fields also. One example is its optimized stock keeping system which forecasts sales by product for each store based on historical sales and weather data. Through predictive analytics Tesco managed to save 100m pounds in stock that would have otherwise expired and thus wasted. In another instance Tesco found that its management of the fridge and store temperatures was sub-optimal and thus enabled significant savings in energy costs.

Using the insights it gained from the collected data Tesco evolved from a retailer that thought it knew what the customers wanted into one that actually did know and could monitor the preferences as they changed over time. Tesco managed to break its customers down into segments it understood better and thus target its sales efforts accordingly.

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