Machine Learning and Knowledge Extraction· 2025Q1· Review
Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning
- 50citations
- Q1SCImago
- 2025year
Short summary
A systematic review of 240 studies (2020-2024) reveals ensemble ML methods like XGBoost and LightGBM remain dominant for customer churn prediction, with increasing adoption of DL techniques like LSTM and CNN for complex data.
AI-generated from the title and abstract; the full text is not read.
Key points
- Ensemble ML methods (XGBoost, LightGBM) are dominant in churn prediction.
- Deep learning methods (LSTM, CNN) are increasingly applied to complex data.
- Major challenges include class imbalance, interpretability, and concept drift.
- Limited use of profit-oriented metrics hinders practical business application.
- Explainable AI and adaptive learning show potential but lack real-world adoption.
AI-generated from the title and abstract; the full text is not read.
Abstract
Background: Customer churn significantly impacts business revenues. Machine Learning (ML) and Deep Learning (DL) methods are increasingly adopted to predict churn, yet a systematic synthesis of recent advancements is lacking. Objectives: This systematic review evaluates ML and DL approaches for churn prediction, identifying trends, challenges, and research gaps from 2020 to 2024. Data Sources: Six databases (Springer, IEEE, Elsevier, MDPI, ACM, Wiley) were searched via Lens.org for studies published between January 2020 and December 2024. Study Eligibility Criteria: Peer-reviewed original studies applying ML/DL techniques for churn prediction were included. Reviews, preprints, and non-peer-reviewed works were excluded. Methods: Screening followed PRISMA 2020 guidelines. A two-phase strategy identified 240 studies for bibliometric analysis and 61 for detailed qualitative synthesis. Results: Ensemble methods (e.g., XGBoost, LightGBM) remain dominant in ML, while DL approaches (e.g., LSTM, CNN) are increasingly applied to complex data. Challenges include class imbalance, interpretability, concept drift, and limited use of profit-oriented metrics. Explainable AI and adaptive learning show potential but limited real-world adoption. Limitations: No formal risk of bias or certainty assessments were conducted. Study heterogeneity prevented meta-analysis. Conclusions: ML and DL methods have matured as key tools for churn prediction, yet gaps remain in interpretability, real-world deployment, and business-aligned evaluation. Systematic Review Registration: Registered retrospectively in OSF.
The authors' abstract, as published at the source. Machine Learning and Knowledge Extraction, 2025 · DOI ↗
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