Comparing Churn Prediction Techniques and Assessing Their Performance: A Contingent Perspective. (May 2016)

• Record Type: Journal Article
• Authors: Tamaddoni, Ali; Stakhovych, Stanislav; Ewing, Michael
• Is Part Of: Journal of service research. Volume 19:Number 2(2016:May)
• Page Start: 123
• Page End: 141
• Publication Date: 2016-05
• Subjects: customer churn -- prediction -- profitability -- retention -- probability models -- data mining -- simulations; Customer services -- Periodicals; Service industries -- Periodicals; 658.81205
• DOI: 10.1177/1094670515616376
• Languages: English
• ISSNs: 1094-6705

Abstract

Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is complex and dependent on firms' ability to accurately identify both at-risk customers and those worth retaining. Drawing on empirical and simulated data from two online retailers, we evaluate the performance of several parametric and nonparametric churn prediction techniques, in order to identify the optimal modeling approach, dependent on context. Results show that under most circumstances (i.e., varying sample sizes, purchase frequencies, and churn ratios), the boosting technique, a nonparametric method, delivers superior predictability. Furthermore, in cases/contexts where churn is more rare, logistic regression prevails. Finally, where the size of the customer base is very small, parametric probability models outperform other techniques.