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@article{173703,
        author = {Dr.M.R.Raja Ramesh and N.V.V.S.Sudheer and S.V.S.Prajnesh and S.Vasavi and M.Pavan Kumar},
        title = {Customer Retention Radar},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1392-1402},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173703},
        abstract = {Customer retention is a vital factor in determining success within the highly competitive banking sector, especially in the digital era where customer choices abound. This research focuses on predicting customer attrition using advanced machine learning techniques, enabling banks to proactively address churn and enhance customer loyalty.
The research utilizes algorithms such as Logistic Regression, Random Forest, and Gradient Boosting to evaluate crucial customer data, including demographic profiles, transaction trends, and sentiment analysis from feedback. The integration of real-time monitoring ensures that churn predictions remain dynamically updated, allowing banks to respond promptly to emerging risks.
Additionally, a Customer Retention Radar workflow is introduced, facilitating personalized retention strategies by leveraging predictive insights and feedback analysis. Important performance metrics like accuracy, precision, recall, and F1-score validate the effectiveness of the model.
This work underscores the transformative role of predictive analytics in banking, offering actionable insights to improve retention rates and foster stronger customer relationships.},
        keywords = {Behavioural Analysis, Churn Prediction, Customer Attrition, Machine Learning, Retention Strategies},
        month = {March},
        }