Comparing churn prediction techniques and assessing their performance: a contingent perspective

Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is com-
plex 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 pre-
diction 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 tec