A PCA-AdaBoost model for E-commerce customer churn prediction

Zengyuan Wu, Lizheng Jing, Bei Wu () and Lingmin Jin
Additional contact information
Zengyuan Wu: China Jiliang University
Lizheng Jing: China Jiliang University
Bei Wu: Zhejiang Gongshang University
Lingmin Jin: China Jiliang University

Annals of Operations Research, 2025, vol. 350, issue 2, No 8, 537-554

Abstract: Abstract Customer churn is detrimental to corporate revenue. Hence, accurate customer churn prediction is vital for enterprises to improve customer retention and corporate revenue. However, in e-commerce, there are challenges when predicting customer churn using traditional models. First, the conventional recency, frequency, and monetary (RFM) analysis based on single-source data can hardly accurately predict the e-commerce customer churn since such customers are often non-contractual. Second, in general, the data about e-commerce customers is high-dimensional and unbalanced, making traditional models even less effective. In this paper, we propose an improved prediction model by integrating data pre-processing and ensemble learning to solve these two problems. Specifically, two new features are first integrated into the RFM analysis to better capture customer behaviors. Second, the principal component analysis is adopted to reduce data dimensions. Third, adaptive boosting (AdaBoost) is employed to cascade multiple decision trees to minimize the impacts from the unbalanced data. For clarity, this model is called the PCA-AdaBoost model. We use an e-commerce dataset published on the Kaggle platform to demonstrate its effectiveness by conducting numerical experiments. We compare the performance of the PCA-AdaBoost model developed in this paper with several models proposed in the literature. Our results confirm that the PCA-AdaBoost model can achieve more accurate customer churn prediction and better overall stability.

Keywords: E-commerce; Customer churn prediction; Ensemble learning; PCA-AdaBoost model (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-022-04526-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:annopr:v:350:y:2025:i:2:d:10.1007_s10479-022-04526-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-022-04526-5

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().