March 18th, 2020
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Use Cases of AI & Machine Learning in Retail

There are various companies that are using Machine Learning to enhance customer experience and to boost sales. Mentioned below are some Machine Learning use cases in the retail sector:

  1. Price Optimization: Due to the comprehensive analysis of data on customers and their solvency, it becomes possible to determine the clearest price that they are willing to pay for a particular product. On this basis, businesses can change their assortment by tailoring the product to a suitable price.
  2. Demand Prediction: Businesses need to predict demand. Machine Learning facilitates better inventory planning and ensures that the products are stocked according to demand prediction. Predictive analytics and Machine Learning make it possible to predict fluctuations in demand and change the price in order not to lose potential profit.
  3. Logistics Support: Big data is the basis for the formation of routes for the delivery of goods to a particular consumer. Smart systems make logistics more thoughtful, achieving two goals at the same time ? the maximum possible improvement of customer service due to rapid delivery, and the maximum reduction of retailer?s costs.
  4. Personalized Offers: The Machine Learning system studies the user?s behavior, adds information about his last purchases, Google search history, comments and likes on social networks, places the client visits, and solvency, and makes the best suggestions about what kind of product will suit the user and at what point in time he will need it.
  5. Predictive Analytics: Predictive analytics is a powerful weapon that helps predict how events would develop, what trends would emerge and how customers would respond to them with. Today, owing to Machine Learning and artificial intelligence, strategies are built based on a huge array of historical, current and alleged data. This is one of the main benefits of predictive analytics.
  6. Churn Rate Prediction: When a business loses one of its customers, it also loses the money invested in attracting this buyer. Machine Learning systems can track situations that are very likely to result in the loss of a client so that the company can take the most urgent measures to retain them.
  7. Location Optimization: Targeting can be done based on the geographical location of the customers. The technology can also be utilized to determine better and faster routes of delivering effectively and efficiently to the customer.
  8. Fraud Detection: Since the system is capable of self-learning, Machine Learning and AI are very strong in recognizing and preventing fraudulent activity with credit cards when shopping online or offline. ML systems can assist in preventing fraudulent activities involving coupons and discounts as well by tracking user behavior from a specific IP address.
  9. Document Work Automation: Machine Learning is capable of analyzing internal data, such as information about how human resources documents are managed. Thus, routine tasks can be automated thereby enabling employees to competently plan their work schedule towards efficiency and aimed at customer service.
  10. Merchandising: Product images play a vital role when it comes to sales. Machine learning can be used for visual merchandising, where an online customer will have the same experience as an offline store customer.

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