Improving Customer Retention Through Machine Learning : A Predictive Approach to Churn Prevention and Engagement Strategies

Authors

  • Bolaji Iyanu Adekunle  Department of Data Science, University of Salford, UK
  • Ezinne C. Chukwuma-Eke  TotalEnergies Nigeria Limited
  • Emmanuel Damilare Balogun  Independent Researcher, USA
  • Kolade Olusola Ogunsola  Independent Researcher, USA

Keywords:

Customer Retention, Improvement, Machine Learning, Predictive Approach, Churn Prevention, Engagement Strategies

Abstract

Customer retention is a critical factor in ensuring business sustainability and long-term profitability. The ability to predict and prevent customer churn enables organizations to enhance customer satisfaction, optimize engagement strategies, and improve financial performance. This study explores the role of machine learning (ML) in customer retention, focusing on predictive analytics for churn prevention and personalized engagement strategies. Machine learning models such as logistic regression, decision trees, random forests, gradient boosting methods (XGBoost, LightGBM), and deep learning techniques (LSTMs, neural networks) have demonstrated high accuracy in predicting churn. These models analyze large datasets, including customer transaction history, behavioral patterns, sentiment analysis, and external factors, to identify churn risk early. Feature engineering and data preprocessing play a crucial role in improving model performance, ensuring relevant insights for businesses. Beyond prediction, ML-driven engagement strategies allow businesses to implement targeted retention measures. Personalized marketing campaigns, customer segmentation, proactive customer support, and AI-driven loyalty programs enhance customer satisfaction and reduce churn rates. The integration of real-time analytics and automated intervention systems further strengthens retention efforts. However, challenges such as data quality issues, model interpretability, bias, and privacy concerns remain. Addressing these requires ethical AI practices, transparent modeling techniques, and continuous model refinement. Future advancements in deep learning, real-time intervention systems, and predictive customer lifetime value (CLV) modeling will further enhance churn prevention. This highlights the transformative impact of machine learning in customer retention and provides actionable insights for businesses seeking to leverage predictive analytics for sustainable growth. As ML continues to evolve, its integration into customer experience strategies will be essential for maintaining competitive advantage in an increasingly data-driven market.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-07-24

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Volume 9, Issue 4, July-August-2023

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Research Articles

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[1]
Bolaji Iyanu Adekunle, Ezinne C. Chukwuma-Eke, Emmanuel Damilare Balogun, Kolade Olusola Ogunsola, " Improving Customer Retention Through Machine Learning : A Predictive Approach to Churn Prevention and Engagement Strategies " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.507-523, July-August-2023.