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

Technological Forecasting and Social Change, 2024, vol. 198, issue C

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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162523006303
Full text for ScienceDirect subscribers only

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:eee:tefoso:v:198:y:2024:i:c:s0040162523006303

DOI: 10.1016/j.techfore.2023.122945

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().