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Considering the level of competition prevailing in Business-to-Consumer (B2C)E-Commerce domain and the huge investments required to attract new customers,firms are now giving more focus to reduce their customer churn rate. Churn rateis the ratio of customers who part away with the firm in a specific timeperiod. One of the best mechanism to retain current customers is to identifyany potential churn and respond fast to prevent it. Detecting early signs of apotential churn, recognizing what the customer is looking for by the movementand automating personalized win back campaigns are essential to sustainbusiness in this era of competition. E-Commerce firms normally possess largevolume of data pertaining to their existing customers like transaction history,search history, periodicity of purchases, etc. Data mining techniques can beapplied to analyse customer behaviour and to predict the potential customerattrition so that special marketing strategies can be adopted to retain them.This paper proposes an integrated model that can predict customer churn andalso recommend personalized win back actions.

An Integrated Framework to Recommend Personalized Retention Actions to Control B2C E-Commerce Customer Churn