A Web Application to Support Customer Churn Management for Retail Grocery Stores

Abstract

— In the business world ‘Customer Churn’ is a principal issue. The retail grocery business holds a peak point in churning customers due to various reasons. Churn means gradually breaking every connection with the business by the customers. According to the experts, retaining the existing customers cost less, than attracting new customers. Therefore, a web-based prediction model; “CRetention” with some additional features is proposed as a solution. The main features in the proposed system are to analyze data and predict customers who are about to churn, manage the storage of inventory items, provide marketing strategies by market basket analysis, and offer personalized marketing recommendations to retain customers. Machine Learning and Deep Learning technologies are used to implement the solution. The main advantage and novelty of the product are that a definition for churn adjusted to a retail business is created and churners and results are obtained are based on a real scenario. It is clear that the retail grocery store owners highly recommend and appreciate the proposed system from a survey conducted.