Customer churn prediction - A case study in retail banking

Teemu Mutanen, Sami Nousiainen, Jussi Ahola

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleProfessional

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationData Mining for Business Applications
EditorsCarlos Soares, Rayid Ghani
Pages77-83
Volume218
DOIs
Publication statusPublished - 2010
MoE publication typeD2 Article in professional manuals or guides or professional information systems or text book material

Publication series

SeriesFrontiers in Artificial Intelligence and Applications
Volume218

Keywords

  • Churn
  • customer churn
  • retail banking

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