Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms
Management Science
Determinants of customer loyalty in the wireless telecommunications industry
Telecommunications Policy
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Two-level model of customer retention in the US mobile telecommunications service market
Telecommunications Policy
Customer churn prediction using improved balanced random forests
Expert Systems with Applications: An International Journal
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
We claim that often marketers have not all the information to develop various marketing campaign models. For example, marketers may have sufficient information to build a model for predicting possible churners, while they may have no clues of which customers are most likely to accept a retention campaign. In this paper, we first show that the information useful for a successful churner prediction model alone is not sufficient to develop a successful retention marketing program. In such a case, we claim that only theory-based simulation approach is feasible. In particular, it is claimed 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 churn management models.