Feature-selection-based dynamic transfer ensemble model for customer churn prediction

2014 ◽  
Vol 43 (1) ◽  
pp. 29-51 ◽  
Author(s):  
Jin Xiao ◽  
Yi Xiao ◽  
Anqiang Huang ◽  
Dunhu Liu ◽  
Shouyang Wang
Keyword(s):  
Feature Selection ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model

2021 ◽  
Vol 14 (2) ◽  
pp. 174-186
Author(s):  
Sivasankar Karuppaiah ◽  
N. P. Gopalan
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Churn ◽  
Proposed Model ◽  
Customer Churn Prediction ◽  
Effective Models

In a rapidly growing industry like telecommunications, customer churn prediction is a crucial challenge affecting the sustainability of the business as a whole. The fact that retaining a customer is more profitable than acquiring new customers is important to predict potential churners and present them with offers to prevent them from churning. This work presents a stacked CLV-based heuristic incorporated ensemble (SCHIE) to enable identification of potential churners so as to provide them with offers that can eventually aid in retaining them. The proposed model is composed of two levels of prediction followed by a recommendation to reduce customer churn. The first level involves identifying effective models to predict potential churners. This is followed by result segregation, CLV-based prediction, and user shortlisting for offers. Experimental results indicate high efficiencies in predicting potential churners and non-churners. The proposed model is found to reduce the overall loss by up to 50% in comparison to state-of-the-art models.

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