Comparing Churn Prediction Techniques and Assessing Their Performance
基于两家在线零售商的实证和模拟数据，评估多种参数和非参数客户流失预测技术，发现提升法在多数情况下预测效果最佳，逻辑回归在流失率低时更优，小客户群下参数概率模型表现更好。
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.