ML techniques are frequently used to forecast customer events. For instance, ML can be used by a Voluntary Health Insurance (VHI) insurance company to anticipate which clients would seek expensive medical treatment soon.

FREMONT, CA: Machine learning is currently used in various settings, including banks, restaurants, gas stations, and manufacturing robots. New directions in Machine Learning (ML) emerge nearly daily in response to new obstacles.

Examples of ML solutions for business

Process Optimization

ML allows businesses to organize the supply of items to retail chains in the most efficient way possible. For example, one of the network’s merchants recently deployed a self-learning system that creates orders for the delivery of drinking water to the chain’s locations entirely on its own. In addition, the program considers sales dynamics, weather forecasts, seasons, and other factors, allowing to avoid overstocking or, on the other hand, a scarcity of goods at the point of sale.

Segmentation When Working with Clients

The duties of forecasting and segmentation are both efficiently solved with the help of training programs. Finding clients who are similar to a specific group is the most vivid example. A case like this has been implemented by a dental clinic network with tens of thousands of clients. They took clients who had previously booked a professional hygiene service and identified the most likely buyers of this service from their whole customer base using machine learning approaches based on this data. Such ingenious segmentation can cut the cost of lifeless calling in half.

Customer Behavior Predictions

ML techniques are frequently used to forecast customer events. For instance, ML can be used by a Voluntary Health Insurance (VHI) insurance company to anticipate which clients would seek expensive medical treatment soon. With this information, the corporation contacts’ high-risk’ clients ahead of time and takes preventative actions, such as offering the client a medical examination or arranging a visit with a more skilled doctor. As a result, clients began receiving professional assistance before the onset of the sickness, and the insurance company reduced VHI expenditures by hundreds of thousands of dollars every month.

Selling Systems for Online Stores

Recommendation algorithms for online stores are a good business example. The goal is simple: depending on user activity on the site and their transactions, recommend other products that the customer is likely to acquire. Then, a training program is developed, which is an algorithm that analyzes a large amount of data on online store purchases and, after training, can make fairly accurate predictions for new customers. A solid recommendation system can boost an online store’s revenue by up to 50 percent, according to experience.

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