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IMPLEMENTATION OF AN ML-ALGORITHM FOR PRODUCT RECOMMENDATIONS

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GOALS AND OBJECTIVES

  • BUSINESS OBJECTIVE

    To increase the average purchase amount using targeted offers to customers.

  • IT OBJECTIVE

    To develop and implement a ML-system for recommendations at the time of checkout.

SOLUTION

  • A Machine Learning algorithms-based system.
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IMPLEMENTATION

Jet Infosystems specialists have developed and implemented an artificial intelligence-based system for the VEK ZHIVI pharmacy chain that analyzes customer purchase data with a machine learning model. Based on this analysis, the service shows the cashier three products, which the client is highly likely to add to their purchases, so that the cashier can provide a qualified recommendation. Products (together with their SKU number) are suggested from 27 thousand pharmaceutical products.


In order to cope with the task at hand, a whole set of Machine Learning methods was used, and the mathematical model was trained on a long-term data array storing customer receipts. So as to create a personal offer, the system analyzes several parameters simultaneously, including: the structure of the receipt, the list of goods purchased, and their prices.


Thus, based on comprehensive analysis of previous purchases, the ML-algorithm helps reveal buyers’ hidden needs to a high degree of accuracy, and suggests additional medicines at the time of any new purchase.

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PROJECT RESULTS

Thanks to the solution which has been implemented, the pharmacy chain can make targeted offers to customers and increase the average purchase amount. At the same time, automated recommendations ensure additional sales without creating an additional burden on employees - the software solution takes on all the work of determining the most interesting products for each customer.

ML systems of this kind are now widely used in retail, both by retail chains and by online stores. To date, Jet Infosystems specialists have implemented more than 50 projects based on Machine Learning technologies for banks, retail, industry, insurance and other sectors.

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