B2C E-Commerce Customer Churn Management: Churn Detection using Support Vector Machine and Personalized Retention using Hybrid Recommendations

E-Commerce industry, especially the players in Business-to-Consumer (B2C) sector is witnessing immense competition for survival - by means of trying to penetrate to the customer base of their peers and at the same time not letting their existing customers to churn. Avoiding customer attrition is critical for these firms as the cost of acquiring new customers are going high with more and more players entering into the market with huge capital investments and new penetration strategies. Identifying potential parting away customers and preventing the churn with quick retention actions is the best solution in this scenario. It is also important to understand that what the customer is trying to achieve by opting for a move out so that personalized win back strategies can be applied. E-Commerce industry always possess huge amount of customer data which include information on searches performed, transactions carried out, periodicity of purchases, reviews contributed, feedback shared, etc. for every customers they possess. Data mining and machine learning can help in analyzing this huge volume of data, understanding the customer behavior and detecting possible attrition candidates. This paper proposes a framework based on support vector machine to predict E-Commerce customer churn and a hybrid recommendation strategy to suggest personalized retention actions