Applied big data analysis to build customer product recommendation model

Abstract

With the development of the Internet environment, the trend of the retail industry in the future. It cannot be separated from the community, data and experience. Consumers’ lifestyles and purchasing behaviors are constantly changing and retailers must adopt policies to understand consumers. This research analyzes supermarkets most commonly touched by consumers in daily life. In order to find hidden information behind customer transaction data, it helps supermarkets to learn about the habits of customers to help them Formulate marketing strategies and improve the profitability of supermarkets and maintain long-term relationships with customers. Thus, the RFM model is used to convert customer transaction data into R, F, and M values and then clustering using the Ward’s method to combine with K-means, fuzzy C-means, and self-organizing maps. Using discriminant analysis find out the grouping method with the highest accuracy rate to calculate the customer lifetime value score. In terms of product recommendation, customers can be recommended to buy products in the top five categories or to use rules found in association rule to make recommendations. In terms of customers, we maintain long-term relationships with customers by recommending other related products, products for bundling sale, giving gifts or discount coupons, and regularly organizing promotional activities.