Approaches to Customer Base Analysis for Improving Retention Rates

By combining predictive analytics, machine learning, customer segmentation, and relationship management practices, this study looks at ways to increase customer retention.It divides approaches into four groups: relationship management, machine learning, customer segmentation, and predictive and prescriptive analytics.Superior prediction accuracy and profitability optimization were shown by sophisticated models such as Predict-and-Optimize (PnO) and machine learning algorithms like Random Forest and Light Gradient Boosting Machine (LGBM).Targeted campaigns for a variety of client profiles were made possible by segmentation frameworks like Time-Frequency-Monetary (TFM) and Recency-Frequency-Monetary (RFM), which improved personalization.Loyalty and satisfaction were also greatly enhanced by relationship marketing, trust-building programs, and better service quality.The study emphasizes how combining data-driven insights with customer-centric tactics may work in concert to lower attrition, boost engagement, and boost long-term profitability.These results provide useful information for companies, as well as policymakers, industry regulators, and educators designing curricula for marketing, data science, and customer relationship management programs.