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Related Experiment Videos

A novel hybrid deep learning framework for customer churn prediction using RFM and embedding clustering.

Samia Ibrahim1, BenBella S Tawfik2, Mohamed Abdallah Makhlouf2

  • 1Information System Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt. samia.ibrahim@ci.suez.edu.eg.

Scientific Reports
|May 28, 2026
PubMed
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This study introduces a hybrid framework for e-commerce customer churn prediction, combining feature engineering, deep embedded clustering, and deep learning models. The approach significantly enhances prediction accuracy by integrating representation learning with customer segmentation.

Area of Science:

  • Data Science
  • Machine Learning
  • E-commerce Analytics

Background:

  • E-commerce customer churn prediction is challenging due to limited labeled data and traditional models' inability to capture complex customer behavior.
  • Existing methods often lack effective representation learning or meaningful customer segmentation, hindering accurate churn prediction.

Purpose of the Study:

  • To propose a unified hybrid framework for joint customer segmentation and churn prediction in e-commerce.
  • To address limitations of existing approaches by integrating RFM-based feature engineering, Deep Embedded Clustering (DEC), and deep learning models.

Main Methods:

  • A deep autoencoder learns compact latent representations from transactional data.
  • An improved Deep Embedded Clustering (DEC) mechanism segments customers into behaviorally meaningful groups.
Keywords:
Customer behavior analysisCustomer churnDeep embedded clusteringE-commerceGRULSTMMachine learningRFM features

Related Experiment Videos

  • Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models predict multi-class churn using learned representations.

    • Main Results:

    • LSTM achieved 99.65% accuracy on the Online Retail dataset and 99.83% on the Events dataset.
    • GRU achieved 99.77% accuracy on the Online Retail dataset and 99.75% on the Events dataset.
    • The hybrid framework demonstrated superior adaptability and performance compared to traditional models like Logistic Regression and Support Vector Machine.

    Conclusions:

    • Integrating representation learning, clustering, and deep sequential models significantly enhances e-commerce churn prediction performance.
    • The proposed framework provides structured, actionable insights for effective customer retention strategies in e-commerce.