Customer segmentation and customer churn prediction for Babil.com

Abstract

In the past decade, a lot of players have joined into e-commerce market and competition in the market has been increasing lately. The e-commerce companies want to use their resources more efficiently to stay ahead in the competition. Personal communication with customers, increasing customer loyalty, acquiring new customers and preventing customer churn are some of the ways to achieve this goal. Babil.com is an e-retailer that sells books online and it is one of the companies which wants to stay ahead in the competition. It is founded in 2013 and now it is a 8 years old mature company. So, instead of spending much resources on acquiring new customers, trying to keep existing customers and increasing retention rate is a more ideal goal for the company. Also, personal communication with customers and reaching them with the right product in the right time is crucial. In this project, a customer segmentation with two levels is implemented to help Babil.com. For the first level of segmentation, customers’ value to company is identified by RFM segmentation and in the second level of segmentation customers’ behaviors are identified by K-Means clustering. To prevent customer churn, a machine learning algorithm which predicts customers who will churn in the next 6 months. With this algorithm, it will be easy to take an action for the right customers in the right time.