This work focuses on one of the central topics in customer relationship management (CRM): transfer of valuable customers to a competitor. Customer retention rate has a strong impact on customer lifetime value, and understanding the true value of a possible customer churn will help the company in its customer relationship management. Customer value analysis along with customer churn predictions will help marketing programs target more specific groups of customers. We predict customer churn with logistic regression techniques and analyze the churning and nonchurning customers by using data from a consumer retail banking company. The result of the case study show that using conventional statistical methods to identify possible churners can be successful.
|Title of host publication||Data Mining for Business Applications|
|Editors||Carlos Soares, Rayid Ghani|
|Publication status||Published - 2010|
|MoE publication type||D2 Article in professional manuals or guides or professional information systems or text book material|
|Series||Frontiers in Artificial Intelligence and Applications|
- customer churn
- retail banking