Bagging and Boosting Classification Trees to Predict Churn
Open accessAbstract
In this article, the authors explore the bagging and boosting classification techniques. They apply the two techniques to a customer database of an anonymous U.S. wireless telecommunications company, and both significantly improve accuracy in predicting churn. This higher predictive performance could ultimately lead to incremental profits for companies that use these methods. Furthermore, the results recommend the use of a balanced sampling scheme when predicting a rare event from large data sets, but this requires an appropriate bias correction.
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Classification trees are a suitable technique for predicting customer behavior.
“According to the study of Lemmens and Croux (2006), classification trees are a suitable technique for predicting customer behavior.”
From Forecasting the next likely purchase events of insurance customers: A case study on the value of data-rich multichannel environments (2018) · cites this paper as Lemmens and Croux (2006)
The Results and Conclusions (page unavailable) support bagged and boosted classification trees for predicting customer churn. The claim broadens that finding to customer behavior generally and does not distinguish the ensembles from a single classification tree, which the Results say performed worse than the logit benchmark in this application. (LLM, conf=0.84)
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- Authors
- Lemmens, A., & Croux, C.
- Affiliations
- KU Leuven (BE)
- Corresponding author
- Lemmens, Aurélie
- Croux, Christophe
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2006-05
- Volume
- 43
- Issue
- 2
- Pages
- 276-286
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2026219386
- Cited by
- 411
- References
- 52
- Citations by year
- 2026: 15 · 2025: 15 · 2024: 17 · 2023: 21 · 2022: 33 · 2021: 26 · 2020: 24 · 2019: 23 · 2018: 29 · 2017: 26 · 2016: 21 · 2015: 31 · 2014: 19 · 2013: 15 · 2012: 20
- FWCI
- 42.24
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Locations
- Journal of Marketing Research (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Lirias (submittedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- bagging, boosting, classification trees, customer churn prediction, wireless communication, rare event prediction, balanced sampling, bias correction, customer retention, prediction accuracy
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