Machine learning (ML) is a definite branch of artificial intelligence (AI) that brings together significant insights to solve complex and data-rich business problems by means of algorithms. ML understands the past data that is usually in a raw form to envisage the future outcome. It is gaining more and more popularity in the IT space, and every organization is seeking to grab the advantages of ML advancements.
According to Fortune Business Insights, the expected value of the global machine learning market will be $117.19 billion by 2027 at a CAGR of 39.2% during the forecast period. Easy data availability, growing data volumes, faster computational processing, and economical data storage are driving the growth of machine learning.
With machine learning tools, organizations can figure out gainful opportunities as well as possible risks more promptly. ML aids companies in improving business scalability and enhancing business operations. The rapidly evolving new techniques in the ML field are expanding the usage of machine learning to nearly infinite possibilities.
The article focuses on how you can expedite your business growth with the use of machine learning, and here are the key points:
1. Prediction of the market segment: When businesses are entering into the market with a new idea, it is very important to understand and forecast the reactions of the market. If you go with human intelligence for a logical prediction, it would be a huge task to consider all the applicable parameters from a large set of historical data. However, if you make use of the correct classification algorithm(s), you can predict the response from the prospective market segment if it is good, bad, or neutral. Besides, you can use continuous or regression algorithms to predict the size or range.
2. Prediction of customer lifetime value: For marketers, it is quite important to know about the customer lifetime value prediction and customer segmentation. For this, companies use huge amounts of data effectively with the help of ML and data mining to obtain meaningful business insights. So, customer behaviors and purchasing patterns can be predicted, and new offers can be sent to individual customers depending on their browsing and purchase history.
3. Knowing the target demographics: Envisaging the correct gender, age groups, likes and dislikes, preferences, and geographical locations about the customers are important to figure out the success or failure of your next idea that is to be implemented. When you apply such a scientific approach using ML rather than just common sense, you can know about many more potential outcomes with the help of historical data.
4. Predictive Maintenance: ML helps manufacturing firms in following preventive and corrective maintenance practices by providing significant insights and hidden patterns in their factory data. This predictive maintenance decreases the risks caused due to unexpected failures and also helps in saving needless expenses. To build ML architecture, historical data, flexible analysis environment, workflow visualization tool, and the feedback loop are used.
5. Reduces errors of manual data entry: ML and predictive modeling algorithms can significantly help avoid errors such as duplicate and inaccurate data that are caused by manual data entry. For doing so, ML programs make use of the discovered data itself. Hence, employees can save a lot of time and utilize it for doing other valuable tasks.
6. Optimize the result of marketing campaigns: Marketing your products through various online or offline marketing campaigns is very imperative for business growth. If you could foresee the outcome of the campaign upfront, you will save both time and money. Here, classification algorithm-based models work best that sense if a reach or response of the customers will be high or low. On the other hand, a regression-based model can predict the lead conversion or response range. With the knowledge of these factors, you can tweak the elements of the campaign to calculate the response or probability of lead conversion and accordingly take steps if it needs improvement.
7. Customer servicing and customer satisfaction: For better customer servicing, you have to be proactive in this modern age, and hence predicting customer response or reactions is key. Based on the historical data such as previous call records, ML helps to understand how typically a customer would respond. It is useful in enhancing customer loyalty and providing them with a superior customer experience. In this way, the money and the time invested in managing customer relationships get saved. Major organizations employ predictive algorithms to know about the products that their customers enjoy.
8. Core business force: Depending on the type of business, you can predict the functional outcome with the utilization of ML. Here, the ML model is decided based on the core problem that you are solving as the ideas are problem or business case-centric.
9. Product suggestions to boost sales: Machine learning is helpful in generating product-based recommendation systems. Especially, e-commerce websites are greatly using such unsupervised learning for making product recommendations. ML algorithms examine a customer’s purchase history to recognize hidden patterns and group similar products. When these products are recommended to customers, there are great chances that product purchases get enhanced.
10. Helps with cybersecurity: ML is helpful in improving the cybersecurity of an organization. With machine learning, organizations can develop newer technologies to detect unknown threats effectively and quickly. Machine learning is also used for detecting spam. With the neural networks, spam filters build new rules to detect spam and phishing messages.
With these many applications, we can surely say that machine learning is here to stay, and there is almost every industry employing ML effectively. Here is a brief description of industry-specific usage of ML;
· Healthcare: Wearable devices or sensors that check pulse rates, steps walked, sleeping patterns, oxygen levels, or sugar levels are the few examples to mention here with new algorithms evolving in the market. This way, ML allows doctors to analyze their patients’ health in real-time.
· Financial Services: ML is used in finance for portfolio management, loan underwriting, identifying new opportunities, algorithmic trading, and fraud detection. Extensive use of Chatbots and other conversational interfaces for security, customer service, and sentiment analysis is also started.
· Transportation: ML is used here for data analysis and modeling functions that are essential for public transportation, delivery, and freight transport sectors. Also, algorithms of machine learning help to uncover the factors that have both positive and negative effects on a supply chain’s success, and hence ML is quite significant within supply chain management.
· Government: Government officials can make use of data to predict prospective future scenarios so that adjusting to quickly changing situations gets easy. ML also helps to advance cybersecurity and cyber intelligence, enhances operational preparedness, shore up counterterrorism efforts, helps with predictive maintenance and logistics management.
· Oil and gas: ML and AI collectively help to find new energy sources, predict refinery sensor failure, inspect mineral deposits in the ground, and reorganize oil distribution for efficiency and cost reduction. Besides, ML benefits in reservoir modeling, case-based reasoning, and drill floor automation. So, machine learning is making this risky industry safer.
· Manufacturing: Machine learning in manufacturing is helpful in improving operations from initial phase to final delivery, reducing error rates notably, increasing inventory turn, and enhancing predictive maintenance.
· Social media: Social networking sites including Facebook, LinkedIn, and Twitter use machine learning for online advertising, custom-made content delivery, and bidding. The ads showing up in the Facebook feed are generated with the help of an ML algorithm that analyzes your preferences with respect to the marketer’s ad delivery settings.
Thus, ML has a great impact on the business from all sectors due to its fast information processing that ultimately helps achieve rapid business growth.
Machine learning enables businesses to anticipate and satisfy customer needs, make inventory systems more efficient, and decrease the number of errors and manual work when managing huge data.
ML helps achieve advanced business intelligence, improved revenue, increased KPIs, and better ROI. The value of incorporating machine learning with your business is surely a great idea. Businesses should embrace machine learning now so that they don’t risk falling behind the competition.
