Customer Retention: A Self Learning ‎Approach

  • Authors

    • Monali Chaudhari Department of Electronics and Telecommunication Engineering, VES Institute of Technology, Mumbai, India https://orcid.org/0000-0002-8045-8294
    • Saylee Gharge Department of Electronics and Telecommunication Engineering, VES Institute of Technology, Mumbai, India
    • Rasika Naik Department of Electronics and Telecommunication Engineering, VES Institute of Technology, Mumbai, India
    • Nandini Ammangi Department of Electronics and Telecommunication Engineering, VES Institute of Technology, Mumbai, India
    • Garima Chhabra Department of Electronics and Telecommunication Engineering, VES Institute of Technology, Mumbai, India
    • Abhiram Pashankar Department of Electronics and Telecommunication Engineering, VES Institute of Technology, Mumbai, India
    https://doi.org/10.14419/y2qpn172

    Received date: July 14, 2025

    Accepted date: September 10, 2025

    Published date: October 19, 2025

  • Customer Retention; Reinforcement learning; Market Basket Analysis; Churn Prediction

  • Abstract

    In the competitive business landscape, customer retention is crucial for business profitability, as acquiring new ‎customers is significantly more expensive than retaining existing ones. Traditional retention strategies rely on ‎static machine learning models that predict churn based on historical data, limiting their ability to adapt to ‎changing customer behaviors. This study proposes a Self-Learning System powered by Reinforcement ‎Learning (RL) to enhance customer retention dynamically. The proposed system analyzes customer data and ‎segments individuals based on behavioral and transactional characteristics. It autonomously implements ‎tailored retention strategies specific to each segment's needs, such as personalized discounts and loyalty ‎rewards. A key innovation lies in the system's real-time learning capability, utilizing feedback from customer ‎interactions to refine its decisions. Experimental results demonstrate that the proposed system adapts to evolving ‎market conditions and improves retention rates compared to traditional models. These findings highlight the ‎potential of AI-driven adaptive retention strategies in optimizing customer engagement and long-term business ‎growth‎.

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  • How to Cite

    Chaudhari , M. ., Gharge, S., Naik, R., Ammangi, N. ., Chhabra, G., & Pashankar, A. (2025). Customer Retention: A Self Learning ‎Approach. International Journal of Accounting and Economics Studies, 12(6), 696-702. https://doi.org/10.14419/y2qpn172