Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction

Zhenkun Liu, Ping Jiang, Koen W. de Bock (), Jianzhou Wang, Lifang Zhang and Xinsong Niu
Additional contact information
Zhenkun Liu: NJUPT - Nanjing University of Posts and Telecommunications [Nanjing]
Ping Jiang: DUFES - Dongbei University of Finance and Economics, Dalian
Koen W. de Bock: Audencia Business School, Audencia Recherche - Audencia Business School
Jianzhou Wang: MUST - Macau University of Science and Technology
Lifang Zhang: NUFE - Nanjing University of Finance and Economics
Xinsong Niu: CAS - Chinese Academy of Sciences [Beijing]

Post-Print from HAL

Abstract: Customer retention campaigns increasingly rely on predictive analytics to identify potential churners in a customer base. Traditionally, customer churn prediction was dependent on binary classifiers, which are often optimized for accuracy-based performance measures. However, there is a growing consensus that this approach may not always fulfill the critical business objective of profit maximization, as it overlooks the costs of misclassification and the benefits of accurate classification. This study adopts extreme gradient boosting trees to predict profit-driven customer churn. The class weights and other hyperparameters of these trees are optimized using Bayesian methods based on the profit maximization criterion. Empirical analyses are conducted using real datasets obtained from service providers in multiple markets. The empirical results demonstrate that the proposed model yields significantly higher profits than the benchmark models. Bayesian optimization and adjustment of class weights contributed to enhanced model profitability. Furthermore, when optimizing multiple hyperparameters, the computational cost of model optimization is significantly reduced compared with an exhaustive grid search. Additionally, we demonstrate the robustness of the proposed model through a sensitivity analysis employing Bayesian optimization. Using the proposed model, marketing managers can design targeted marketing plans to retain customer groups with a higher likelihood of churning.

Keywords: Bayesian optimization; Customer churn prediction; Extreme gradient boosting tree; Profit maximization; Profit-driven customer churn prediction; Sensitivity analysis (search for similar items in EconPapers)
Date: 2024-01
Note: View the original document on HAL open archive server: https://hal.science/hal-04273578v1
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published in Technological Forecasting and Social Change, 2024, 198 (January 2024), pp.122945. ⟨10.1016/j.techfore.2023.122945⟩

Downloads: (external link)
https://hal.science/hal-04273578v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04273578

DOI: 10.1016/j.techfore.2023.122945

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().