An Integrated Framework to Recommend Personalized Retention Actions to Control B2C E-Commerce Customer Churn
This addresses customer retention for e-commerce firms, but appears incremental as it combines existing prediction and recommendation methods.
The paper tackles customer churn in B2C e-commerce by proposing an integrated model that predicts churn and recommends personalized retention actions, aiming to reduce churn rates through data mining techniques.
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 rate is the ratio of customers who part away with the firm in a specific time period. One of the best mechanism to retain current customers is to identify any potential churn and respond fast to prevent it. Detecting early signs of a potential churn, recognizing what the customer is looking for by the movement and automating personalized win back campaigns are essential to sustain business in this era of competition. E-Commerce firms normally possess large volume of data pertaining to their existing customers like transaction history, search history, periodicity of purchases, etc. Data mining techniques can be applied to analyse customer behaviour and to predict the potential customer attrition so that special marketing strategies can be adopted to retain them. This paper proposes an integrated model that can predict customer churn and also recommend personalized win back actions.