Machine Learning Approaches for Improving Client Engagement and Retention

  • Authors

    • Dr. Charles Arockiasamy Assistant Professor, St. Joseph’s College (Autonomous), Tiruchirappalli, Tamil Nadu, India. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2018PII4V9Y

    Published 11-04-2018

  • Machine Learning, Client Engagement, Client Retention, Information Analytics, Predictive Analytics, Resource Management, Customer Behavior Prediction, Personalization, Churn Prediction, Data Privacy, Sentiment Analysis

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    C. Arockiasamy, “Machine Learning Approaches for Improving Client Engagement and Retention”, IJADSMC, vol. 1, no. 2, pp. 01–15, Nov. 2018, doi: 10.67228/30713498/IJADSMC-2018PII4V9Y.

  • Abstract

    In today's competitive business environment, client engagement and retention are critical factors for sustainable growth and profitability. The advent of machine learning (ML) techniques has revolutionized the way businesses interact with their customers, providing valuable insights from large volumes of data. This paper explores how machine learning approaches can be employed to enhance client engagement and retention by leveraging information analytics and optimizing resource management. We investigate the role of ML algorithms in predicting customer behaviors, personalizing interactions, and improving customer satisfaction. Furthermore, the paper discusses how businesses can manage their resources efficiently through ML-driven solutions such as predictive analytics and automation. The challenges associated with the adoption of ML, including data privacy concerns and model interpretability, are also examined. Through case studies and examples, we highlight successful implementations and propose future directions for integrating ML in client retention strategies. This paper aims to provide insights into the potential of ML to transform client relationship management and foster long-term customer loyalty.

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