Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308845 
Authors: 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Marketing Analytics [ISSN:] 2050-3326 [Volume:] 11 [Issue:] 4 [Publisher:] Palgrave Macmillan [Place:] London [Year:] 2022 [Pages:] 738-746
Publisher: 
Palgrave Macmillan, London
Abstract: 
While the COVID-19 pandemic negatively affects the world economy in general, the crisis accelerates concurrently the rapidly growing subscription business and online purchases. This provokes a steadily increasing demand of reliable measures to prevent customer churn which unchanged is not covered. The research analyses how preventive uplift modeling approaches based on decision trees can be modified. Thereby, it aims to reduce the risk of churn increases in scenarios with systematically occurring local estimation errors. Additionally, it compares several novel spatial distance and churn likelihood respecting selection methods applied on a real-world dataset. In conclusion, it is a procedure with incorporated additional and engineered decision tree splits that dominates the results of an appropriate Monte Carlo simulation. This newly introduced method lowers probability and negative impacts of counterproductive churn prevention campaigns without substantial loss of expected churn likelihood reduction effected by those same campaigns.
Subjects: 
Churn
Prevention
Uplift modeling
Local errors
Decision trees
Additional splits
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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