Decision intelligence plays a crucial role in modern organizations, enabling them to navigate the intricate and dynamic business landscape of today. By harnessing the power of data and analytics, companies can gain a competitive edge, enhance customer satisfaction, and mitigate risks effectively.

Leveraging a combination of data, analytics, and machine learning, it emerges as a multidisciplinary field that empowers organizations to optimize their decision-making processes. Its applications span across various facets of business, encompassing customer service enhancement, product development streamlining, and robust risk management strategies.

What is decision intelligence?

Decision intelligence is a relatively new field, but it is rapidly gaining popularity. Gartner, a leading research and advisory firm, predicts that by 2023, more than a third of large organizations will have analysts practicing decision intelligence, including decision modeling.

This business model is a combination of several different disciplines, including:

Data science: The process of collecting, cleaning, and analyzing data

Analytics: The process of using data to identify patterns and trends

Machine learning: The process of teaching computers to learn from data and make predictions

These platforms use these disciplines to help organizations make better decisions. These platforms typically provide users with a centralized repository for data, as well as tools for analyzing and visualizing data. They also typically include features for creating and managing decision models.

There are many benefits of having decision intelligence

Decision intelligence can offer a number of benefits to organizations.

Decision intelligence platforms can help organizations make decisions more quickly and accurately by providing them with access to real-time data and insights. This is especially important in today’s fast-paced business world, where organizations need to be able to react to changes in the market or customer behavior quickly.

For example, a retailer might use decision intelligence to track customer behavior in real-time and make adjustments to its inventory levels accordingly. This can help the retailer avoid running out of stock or overstocking products, which can both lead to lost sales.

Artificial intelligence is both Yin and Yang

It also can help organizations make better decisions by providing them with a more holistic view of the data. This is because decision intelligence platforms can analyze large amounts of data from multiple sources, including internal data, external data, and social media data. This allows organizations to see the big picture and make decisions that are more informed and less likely to lead to problems.

A financial services company might use decision intelligence to analyze data on customer demographics, spending habits, and credit history. This information can then be used to make more informed decisions about who to approve for loans and what interest rates to charge.

Utilizing it can help organizations reduce risk by identifying potential problems before they occur. This is because decision intelligence platforms can use machine learning algorithms to identify patterns and trends in data.

Let’s imagine that, a manufacturing company uses decision intelligence to track data on machine performance. If the platform detects a patern of increasing machine failures, the company can take steps to prevent a major breakdown. This can save the company time and money in the long run.

It may help organizations become more efficient by automating decision-making processes. This can free up human resources to focus on more strategic tasks.

For example, a customer service company might use decision intelligence to automate the process of routing customer calls to the appropriate department. This can save the company time and money, and it can also improve the customer experience by ensuring that customers are routed to the right person the first time.

And last but not least, Decision intelligence can help organizations improve customer satisfaction by providing them with a more personalized and relevant customer experience. This is because decision intelligence platforms can use data to track customer preferences and behaviors.

For example, an online retailer might use decision intelligence to recommend products to customers based on their past purchases and browsing history. This can help customers find the products they’re looking for more quickly and easily, which can lead to increased satisfaction.

How to develop decision intelligence?

There are a number of steps that organizations can take to develop decision intelligence capabilities. These steps include:

• Investing in data and analytics: Organizations need to invest in the data and analytics infrastructure that will support decision intelligence. This includes collecting and storing data, cleaning and preparing data, and analyzing data.
• Developing decision models: Organizations need to develop decision models that can be used to make predictions and recommendations. These models can be developed using machine learning algorithms or by using expert knowledge.
• Deploying decision intelligence platforms: Organizations need to deploy these platforms that can be used to manage and execute decision models. These platforms should provide users with a user-friendly interface for interacting with decision models and for making decisions.
• Training employees: Organizations need to train employees on how to use decision intelligence platforms and how to make decisions based on the output of those platforms. This training should cover the basics of data science, analytics, and machine learning.

Automation’s role is vital in decision intelligence

Automation is playing an increasingly important role in decision intelligence. Automation can be used to automate a number of tasks involved in decision-making, such as data collection, data preparation, and model deployment. This can free up human resources to focus on more strategic tasks, such as developing new decision models and managing decision intelligence platforms.

In addition, automation can help to improve the accuracy and consistency of decision-making. By automating tasks that are prone to human error, such as data entry and model validation, automation can help to ensure that decisions are made based on the most accurate and up-to-date data.

Big tech is already familiar with this concept

Decision intelligence is a powerful tool that can be used by organizations of all sizes and in all industries. By providing organizations with access to real-time data, insights, and automation, it can help organizations make faster, more accurate, and more efficient decisions.

Amazon

Amazon uses it to make decisions about product recommendations, pricing, and logistics. For example, Amazon’s recommendation engine uses it to recommend products to customers based on their past purchases and browsing history.

Google

Google uses decision intelligence to make decisions about search results, advertising, and product development. For example, Google’s search algorithm uses decision intelligence to rank search results based on a variety of factors, including the relevance of the results to the query and the quality of the results.

Facebook

Facebook uses it to make decisions about newsfeed ranking, ad targeting, and user safety. For example, Facebook’s newsfeed ranking algorithm uses decision intelligence to show users the most relevant and interesting content in their newsfeed.

Microsoft

Microsoft utilizes this technology to make decisions about product recommendations, customer support, and fraud detection. For example, Microsoft’s product recommendations engine uses it to recommend products to customers based on their past purchases and browsing history.

Apple

Apple uses this business model to make decisions about product recommendations, app store curation, and fraud detection. For example, Apple’s app store curation team uses it to identify and remove apps that violate the app store guidelines.

Data science and decision intelligence are not related concepts

Data science and decision intelligence are both fields that use data to make better decisions. However, there are some key differences between the two fields.

Data science is a broader field that encompasses the collection, cleaning, analysis, and visualization of data. Data scientists use a variety of tools and techniques to extract insights from data, such as statistical analysis, machine learning, and natural language processing.

Decision intelligence is a more specialized field that focuses on using data to make decisions. Professionals use data science techniques to develop decision models, which are mathematical or statistical models that can be used to make predictions or recommendations. Professionals also work with business stakeholders to understand their decision-making needs and to ensure that decision models are used effectively.

In other words, data science is about understanding data, while decision intelligence is about using data to make decisions.

Here is a table that summarizes the key differences between data science and decision intelligence:

As you can see, data science and decision intelligence are complementary fields. Data science provides the foundation for decision intelligence, but decision intelligence requires an understanding of business needs and the ability to communicate with decision-makers.

In practice, many data scientists also work in decision intelligence roles. This is because data scientists have the skills and experience necessary to develop and use decision models. As the field of decision intelligence continues to grow, we can expect to see even more data scientists working in this area.

Featured image credit: Photo by Google DeepMind on Unsplash.

Making the right decisions in an aggressive market is crucial for your business growth and that’s where decision intelligence (DI) comes to play. As each choice can steer the trajectory of an organization, propelling it towards remarkable growth or leaving it struggling to keep pace. In this era of information overload, utilizing the power of data and technology has become paramount to drive effective decision-making.

Decision intelligence is an innovative approach that blends the realms of data analysis, artificial intelligence, and human judgment to empower businesses with actionable insights. Decision intelligence is not just about crunching numbers or relying on algorithms; it is about unlocking the true potential of data to make smarter choices and fuel business success.

Imagine a world where every decision is infused with the wisdom of data, where complex problems are unraveled and transformed into opportunities, and where the path to growth is paved with confidence and foresight. Decision intelligence opens the doors to such a world, providing organizations with a holistic framework to optimize their decision-making processes.

At its core, decision intelligence harnesses the power of advanced technologies to collect, integrate, and analyze vast amounts of data. This data becomes the lifeblood of the decision-making process, unveiling hidden patterns, trends, and correlations that shape business landscapes. But decision intelligence goes beyond the realm of data analysis; it embraces the insights gleaned from behavioral science, acknowledging the critical role human judgment plays in the decision-making journey.

Think of decision intelligence as a synergy between the human mind and cutting-edge algorithms. It combines the cognitive capabilities of humans with the precision and efficiency of artificial intelligence, resulting in a harmonious collaboration that brings forth actionable recommendations and strategic insights.

From optimizing resource allocation to mitigating risks, from uncovering untapped market opportunities to delivering personalized customer experiences, decision intelligence is a guiding compass that empowers businesses to navigate the complexities of today’s competitive world. It enables organizations to make informed choices, capitalize on emerging trends, and seize growth opportunities with confidence.

What is decision intelligence?

Decision intelligence is an advanced approach that combines data analysis, artificial intelligence algorithms, and human judgment to enhance decision-making processes. It leverages the power of technology to provide actionable insights and recommendations that support effective decision-making in complex business scenarios.

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

In addition to data analysis, decision intelligence integrates principles from behavioral science to understand how human behavior influences decision-making. By incorporating insights from psychology, cognitive science, and economics, decision models can better account for biases, preferences, and heuristics that impact decision outcomes.

AI algorithms play a crucial role in decision intelligence. These algorithms are carefully selected based on the specific decision problem and are trained using the prepared data. Machine learning algorithms, such as neural networks or decision trees, learn from the data to make predictions or generate recommendations.

The development of decision models is an essential step in decision intelligence. These models capture the relationships between input variables, decision options, and desired outcomes. Rule-based systems, optimization techniques, or probabilistic frameworks are employed to guide decision-making based on the insights gained from data analysis and AI algorithms.

Human judgment is integrated into the decision-making process to provide context, validate recommendations, and ensure ethical considerations. Decision intelligence systems provide interfaces or interactive tools that enable human decision-makers to interact with the models, incorporate their expertise, and assess the impact of different decision options.

Continuous learning and improvement are fundamental to decision intelligence. The system adapts and improves over time as new data becomes available or new insights are gained. Decision models can be updated and refined to reflect changing circumstances and improve decision accuracy.

At the end of the day, decision intelligence empowers businesses to make informed decisions by leveraging data, AI algorithms, and human judgment. It optimizes decision-making processes, drives growth, and enables organizations to navigate complex business environments with confidence.

How does decision intelligence work?

Decision intelligence operates by combining advanced data analysis techniques, artificial intelligence algorithms, and human judgment to drive effective decision-making processes.

Let’s delve into the technical aspects of how decision intelligence works.

Data collection and integration

The process begins with collecting and integrating relevant data from various sources. This includes structured data from databases, unstructured data from text documents or images, and external data from APIs or web scraping. The collected data is then organized and prepared for analysis.

Data analysis and modeling

Decision intelligence relies on data analysis techniques to uncover patterns, trends, and relationships within the data. Statistical methods, machine learning algorithms, and data mining techniques are employed to extract meaningful insights from the collected data.

This analysis may involve feature engineering, dimensionality reduction, clustering, classification, regression, or other statistical modeling approaches.

Behavioral science integration

Decision intelligence incorporates principles from behavioral science to understand and model human decision-making processes. Insights from psychology, cognitive science, and economics are utilized to capture the nuances of human behavior and incorporate them into decision models.

This integration helps to address biases, preferences, and heuristics that influence decision-making.

AI algorithm selection and training

Depending on the nature of the decision problem, appropriate artificial intelligence algorithms are selected. These may include machine learning algorithms like neural networks, decision trees, support vector machines, or reinforcement learning.

The chosen algorithms are then trained using the prepared data to learn patterns, make predictions, or generate recommendations.

Decision model development

Based on the insights gained from data analysis and AI algorithms, decision models are developed. These models capture the relationships between input variables, decision options, and desired outcomes.

The models may employ rule-based systems, optimization techniques, or probabilistic frameworks to guide decision-making.

Human judgment integration

Decision intelligence recognizes the importance of human judgment in the decision-making process. It provides interfaces or interactive tools that enable human decision-makers to interact with the models, incorporate their expertise, and assess the impact of different decision options. Human judgment is integrated to provide context, validate recommendations, and ensure ethical considerations are accounted for.

Continuous learning and improvement

Decision intelligence systems often incorporate mechanisms for continuous learning and improvement. As new data becomes available or new insights are gained, the models can be updated and refined.

This allows decision intelligence systems to adapt to changing circumstances and improve decision accuracy over time.

Decision execution and monitoring

Once decisions are made based on the recommendations provided by the decision intelligence system, they are executed in the operational environment. The outcomes of these decisions are monitored and feedback is collected to assess the effectiveness of the decisions and refine the decision models if necessary.

How is decision intelligence different from artificial intelligence?

AI, standing for artificial intelligence, encompasses the theory and development of algorithms that aim to replicate human cognitive capabilities. These algorithms are designed to perform tasks that were traditionally exclusive to humans, such as decision-making, language processing, and visual perception. AI has witnessed remarkable advancements in recent years, enabling machines to analyze vast amounts of data, recognize patterns, and make predictions with increasing accuracy.

On the other hand, Decision intelligence takes AI a step further by applying it in the practical realm of commercial decision-making. It leverages the capabilities of AI algorithms to provide recommended actions that specifically address business needs or solve complex business problems. The focus of Decision intelligence is always on achieving commercial objectives and driving effective decision-making processes within organizations across various industries.

To illustrate this distinction, let’s consider an example. Suppose there is an AI algorithm that has been trained to predict future demand for a specific set of products based on historical data and market trends. This AI algorithm alone is capable of generating accurate demand forecasts. However, Decision intelligence comes into play when this initial AI-powered prediction is translated into tangible business decisions.

In the context of our example, Decision intelligence would involve providing a user-friendly interface or platform that allows a merchandising team to access and interpret the AI-generated demand forecasts. The team can then utilize these insights to make informed buying and stock management decisions. This integration of AI algorithms and user-friendly interfaces transforms the raw power of AI into practical Decision intelligence, empowering businesses to make strategic decisions based on data-driven insights.

By utilizing Decision intelligence, organizations can unlock new possibilities for growth and efficiency. The ability to leverage AI algorithms in the decision-making process enables businesses to optimize their operations, minimize risks, and capitalize on emerging opportunities. Moreover, Decision intelligence facilitates decision-making at scale, allowing businesses to handle complex and dynamic business environments more effectively.

Below we have prepared a table summarizing the difference between decision intelligence and artificial intelligence:

How can decision intelligence help with your business growth?

Decision intelligence is a powerful tool that can drive business growth. By leveraging data-driven insights and incorporating artificial intelligence techniques, decision intelligence empowers businesses to make informed decisions and optimize their operations.

Strategic decision-making is enhanced through the use of decision intelligence. By analyzing market trends, customer behavior, and competitor activities, businesses can make well-informed choices that align with their growth goals and capitalize on market opportunities.

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Optimal resource allocation is another key aspect of decision intelligence. By analyzing data and using optimization techniques, businesses can identify the most efficient use of resources, improving operational efficiency and cost-effectiveness. This optimized resource allocation enables businesses to allocate their finances, personnel, and time effectively, contributing to business growth.

Risk management is critical for sustained growth, and decision intelligence plays a role in mitigating risks. Through data analysis and risk assessment, decision intelligence helps businesses identify potential risks and develop strategies to minimize their impact. This proactive approach to risk management safeguards business growth and ensures continuity.

Market insights are invaluable for driving business growth, and decision intelligence help businesses uncover those insights. By analyzing data, customer behavior, and competitor activities, businesses can gain a deep understanding of their target market, identify emerging trends, and seize growth opportunities. These market insights inform strategic decisions and provide a competitive edge.

Personalized customer experiences are increasingly important for driving growth, and decision intelligence enable businesses to deliver tailored experiences. By analyzing customer data and preferences, businesses can personalize their products, services, and marketing efforts, enhancing customer satisfaction and fostering loyalty, which in turn drives business growth.

Agility is crucial in a rapidly changing business landscape, and decision intelligence supports businesses in adapting quickly. By continuously monitoring data, performance indicators, and market trends, businesses can make timely adjustments to their strategies and operations. This agility enables businesses to seize growth opportunities, address challenges, and stay ahead in competitive markets.

There are great companies that offer decision intelligence solutions your business need

There are several companies that offer decision intelligence solutions. These companies specialize in developing platforms, software, and services that enable businesses to leverage data, analytics, and AI algorithms for improved decision-making.

Below, we present you with the best decision intelligence companies out there.

• Qlik
• ThoughtSpot
• DataRobot
• IBM Watson
• Microsoft Power BI
• Salesforce Einstein Analytics

Qlik

Qlik offers a range of decision intelligence solutions that enable businesses to explore, analyze, and visualize data to uncover insights and make informed decisions. Their platform combines data integration, AI-powered analytics, and collaborative features to drive data-driven decision-making.

ThoughtSpot

ThoughtSpot provides an AI-driven analytics platform that enables users to search and analyze data intuitively, without the need for complex queries or programming. Their solution empowers decision-makers to explore data, derive insights, and make informed decisions with speed and simplicity.

DataRobot

DataRobot offers an automated machine learning platform that helps organizations build, deploy, and manage AI models for decision-making. Their solution enables businesses to leverage the power of AI algorithms to automate and optimize decision processes across various domains.

IBM Watson

IBM Watson provides a suite of decision intelligence solutions that leverage AI, natural language processing, and machine learning to enhance decision-making capabilities. Their portfolio includes tools for data exploration, predictive analytics, and decision optimization to support a wide range of business applications.

Microsoft Power BI

Microsoft Power BI is a business intelligence and analytics platform that enables businesses to visualize data, create interactive dashboards, and derive insights for decision-making. It integrates with other Microsoft products and offers AI-powered features for advanced analytics.

While you can access Power BI for a fixed fee, with the giant company’s latest announcement, Microsoft Fabric, you can access all the support your business needs with this service in a pay-as-you-go pricing form.

Salesforce Einstein Analytics

Salesforce Einstein Analytics is an AI-powered analytics platform that helps businesses uncover insights from their customer data. It provides predictive analytics, AI-driven recommendations, and interactive visualizations to support data-driven decision-making in sales, marketing, and customer service.

These are just a few examples of companies offering decision intelligence solutions. The decision intelligence market is continuously evolving, with new players entering the field and existing companies expanding their offerings.

Organizations can explore these solutions to find the one that best aligns with their specific needs and objectives to achieve business growth waiting for them on the horizon.

How decision intelligence changes the way companies make decisions

Our hyperconnected world has become so complex that existing decision-making processes within organizations are no longer sufficient. In a study, about 65% of executives from Fortune 500 companies said that, as a result, decision-making in their organization has also fundamentally changed. The perception that high-quality company decisions are made is reported by just 57% of respondents.[i]

Moreover, human nature is not very efficient at making good decisions. Most of the time, whether we like it or not, our judgments are usually based on emotions and influenced by unconscious biases. People want to act rationally, but they can’t because they have natural limits to how much information they can absorb and process.

We also tend to settle for the minimum acceptable requirements we need to find a satisfying solution – a phenomenon that is known as “satisficing” (a combination of the words “suffice” and “satisfy”): It’s just a lot easier and faster to sacrifice some things to obtain satisfaction rather than considering all the necessary information to find the optimal solution to a problem.

What is Decision Intelligence?

Time for companies to rethink decision-making. The tool to make proven groundbreaking decisions for your business is Decision Intelligence (DI). It enables organizations to make future-proof decisions faster and more efficiently using advanced technologies such as AI, machine learning, or process automation.

The great breakthrough is: Consideration is given not only to raw data but also to a multidimensional set of data that includes text, images, video, and audio. This way, cognitive technologies cannot only deeply analyze vast amounts of data but also evaluate their correlation, making it possible to derive reliable forecasts and identify decision needs that you might otherwise miss.

The term “decision intelligence” was first introduced in Lorien Pratt’s book “Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World” before being adopted by market researcher Gartner, who has named DI one of the most important technology trends in 2022 and further developed as a strategic business tool.[ii]

How does Decision Intelligence improve decision-making?

By using decision intelligence, we can make better and more informed decisions, for example, by matching hazy feelings with validated data. Beyond that, AI-powered decision-making comes with five key benefits:

• Identify complex interrelationships 

By 2025, there will be 175 zettabytes (ZB) of data worldwide, predicts the International Data Group.[iii] For humans, ingesting and processing such massive amounts of data is almost impossible. 

• Make decisions faster 

Slow decision-making hinders progress and profitability. With the help of AI systems, companies can decisively shorten complex decision paths and respond more quickly to changing parameters, reducing the risk of unforeseen events.

• Improve personal judgment 

External factors such as cognitive or emotional biases influence factual judgment. The truly optimal decision for business success is thus often overlooked. By using Decision Intelligence, we can rationalize our decisions because we can make factual judgments based on bias-free data analysis.

• Decisions become measurable 

Decision Intelligence elevates decisions to an essential strategy tool for sustainable business success. Based on concrete business goals and key figures, AI systems can be used to derive suitable optimization measures and future scenarios.

• Decisions become scalable 

A company’s multi-layered data sets are usually scattered across different departments. AI-driven decision-making processes enable companies to correlate critical influencing factors from different sources and data perspectives.

What makes DI so impactful for businesses?

Every day, companies have to make countless decisions. Decision Intelligence enables you to leverage your data strategically and consistently to make the optimal decision for your business goals at any time and across the entire value chain. The reasons for this are self-evident: AI-supported decision-making can reduce unnecessary costs arising from slow processes and high failure rates, and decisions are made transparently and measurably. These reasons greatly increase a company’s knowledge management in the long run.

In other words: The ability to consistently and logically create value by reproducing optimal decisions, again and again, is the perfect ground to drive an effective strategy for reaching new levels of business growth. In particular,

1. Generating customer growth

With the help of DI-driven predictions and insights, companies can make reliable predictions about the effectiveness of their actions, identify cost-saving potential, and optimize internal processes.  

2. Increasing sales

Data-driven customer analytics allows you to identify high-value customers, deliver targeted marketing campaigns, and optimize the entire customer journey to attract new customers faster. 

3. Reducing costs

With Decision Intelligence, organizations can identify the factors that affect their revenue, predict how pricing, cross-selling, and upselling impact sales, and forecast when leads will convert to buyers. 

4. Maximize profit margins

AI-powered forecasts and trade-offs also will help you set prices and discounts or balance your staff capacity to maximize profit margins. 

How does AI-based decision-making work?

For routine business tasks, production and customer operations, an end-to-end automation is the fastest and most profitable solution. Using programmed processes, repetitive tasks and actions can be executed flawlessly and without interruption. But beyond these predefined processes there are innumerable choices to be made which require intuition, flexibility and coordination between all personnel involved.

Just like us, machine-learning systems learn from experience and independently find solutions to new and unexpected problems as they prepare the entire process from data analysis to decision recommendation. Decision makers can make the right decisions by choosing from all proposed alternatives. In other words, using Decision Intelligence never involves leaving critical decisions to machines but rather combines human experience and intuition with automation to take decision-making to a whole new level.

How can I implement DI in the company?

The number of DI users is still relatively small. Analyst Dr. Pieter J. den Hamer predicts that 33% of large companies will begin implementing Decision Intelligence by 2023.[iv] A good starting position if you want to put your company ahead of the competition.

However, the use of AI technology in itself is not enough to outpace the competition. It usually involves rethinking your company’s culture and removing from focusing purely on IT. According to Gartner’s analysts, a company that wants to fully exploit the benefits of Decision Intelligence should make its decisions as follows:[v] 

Connected

Decisions have a mutual impact on individual personnel of an organization so the process must be much more connected at all levels. Sharing data and insights is the bread and butter of this process.

Contextual

Any alternative decision being considered must be evaluated beyond the constraints of a single event or transaction.

Continuous

Companies must respond to both opportunities and disruptions as quickly as possible. Decision-making is increasingly becoming a continuous process.

Companies set their starting point for using DI by analyzing the current state of their decision-making processes. At what point are the decision-making processes so complicated that they become unmanageable? At what point is there a huge amount of data but little insight? Where is the opportunity to merge multiple decision silos? Meetings, where decisions are made, should be monitored along with organizing interviews with decision-makers and asking them to explain some examples of how decisions are being made. This allows decision-making principles to be defined and decision-making habits to be identified.

Scale your business with Decision Intelligence tools

Following this and selecting customized technologies and tools will make it possible to review important use cases step-by-step before scaling the DI approach for the entire company.

This is where paretos steps in, Germany’s leading Decision Intelligence platform. The Heidelberg-based tech start-up makes analysis processes for companies as easily accessible and integrable as an email programme. With the help of AI-based software as a service tool, innovative SMEs, dynamic start-ups, and large corporations can carry out extensive data analyses without prior knowledge or the expertise of data science specialists.

Based on existing company data, paretos analyzes optimization potentials and visualizes correlations in a user-friendly dashboard so everyone can obtain in-depth insights without data expertise. Thanks to a modern user interface, all information can be managed quickly and easily. The automated optimization approach identifies new solutions faster and more efficiently than familiar analysis tools or manually calculated scenarios. This allows logistics companies, for example, to evaluate how CO2 consumption, delivery speed, and costs should be balanced to increase profit margins. To be able to do this, paretos takes on the task of combining all of the dynamic factors that make many digital organizational processes so complex today.

Among other things, the underlying software is capable of fully automating the most complex challenges in the business and marketing context today:

• Targeting customers using personalized product recommendations, cross-selling options, and impact analysis (Customer Recommendations).
• Dynamic pricing of products and services in response to market changes (Dynamic Pricing).
• Efficient inventory management to optimize logistics processes in real-time (Warehouse Optimization).

Using paretos, the German e-commerce retailer SNOCKS, for example, established an intelligent price management system quickly. This allows the company to control its discount prices in a data-driven manner and adjust them according to demand. Also, one of Europe’s largest parcel delivery companies achieved a prediction accuracy of up to 95% on its expected parcel volumes after just five months of using paretos’ software. The increasing volume of available analyses and automated forecasts allows companies to create more precise deployment plans and, thus, sustainably save on operating costs while incorporating CO balance targets into their decisions. 

And this is just the beginning. Altogether, the opportunities to leverage Decision Intelligence to improve the profitability of your business are endless. Now is the time to reconsider your decision-making processes.

[i] https://www.mckinsey.com/business-functions/people-and-organizational-performance/our-insights/decision-making-in-the-age-of-urgency

[ii] https://www.gartner.com/en/documents/4006925

[iii] ⁱ https://www.iwd.de/artikel/datenmenge-explodiert-431851

[iv] https://www.forbes.com/sites/eriklarson/2022/03/21/how-decision-intelligence-will-finally-change-decision-making-from-mystical-to-mundane/?sh=26c957086a74

[v] https://www.gartner.com/en/publications/what-effective-decision-making-looks-like