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@article{179862,
        author = {MR.S.PADMANABAN and MS.G.MAHALAKSHMI},
        title = {USING A PREDICTIVE APPROACH TO CUSTOMER MANAGEMENT : FROM SEGMENTATION TO RETENTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {9053-9055},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179862},
        abstract = {Customer segmentation groups people based on their buying habits, demographics, and preferences to improve marketing strategies and keep customers. Traditional static segmentation models have a hard time capturing dynamic behaviors, which makes marketing less effective and people less interested. This project uses advanced clustering methods based on Regency, Frequency, and Monetary (RFM) metrics to combine dynamic segmentation and churn prediction. It puts customers into three groups: Gold, Silver, and Normal. Then, Logistic Regression uses RFM scores, behavioral patterns, and demographic data to figure out how likely someone is to leave. Businesses can use segmentation and predictive analytics together to get useful information that helps them create targeted retention strategies, personalized offers, and loyalty programs. This two-step method fills the gap between traditional segmentation and predictive modelling of customer behaviour. It makes better use of resources and improves customer relationship management},
        keywords = {},
        month = {May},
        }