Thanks to the formation of associative rules, we analyzed two years’ worth of data on transactions and discovered what product combinations are the most popular among 966.ua’s clients. Special graphs were designed to present visually which products are bought in sets with other products and patterns in users behavior. This report helped the company to improve its product set promotions.
This report was prepared for the food delivery company to identify client clusters for better personalization and to allow it to focus on the most profitable group of users. On the graphs, we presented the frequency of orders and the average purchase price of every cluster, the income from the customer, and time of the order. Every cluster was studied separately and its share of the whole customer base identified.