Measuring the Success of Retention Management Models Built on Churn Probability, Retention Probability, and Expected Yearly Revenues

In this paper, we claim that optimal retention management models should consider not only churn probability but also retention probability and expected revenues from target customers. To validate our claim, we develop and compare five retention management models based on churn probability, retention probability, expected revenues, and combination of these models along with different evaluation metrics. Our experimental results show that the retention management model with the highest accuracy in predicting possible churners is not necessarily optimal because it does not consider the probability of accepting retention promotions. In contrast, the retention management model based on both churn and retention probability is the best in terms of predicting customers who are most likely to positively respond to retention promotions. Ultimately, the model based on expected yearly revenue of customers accrues the highest revenues across most target points, making it the best model out of five retention management models.