Data Mining Approaches for Customer Relationship Management and Business Intelligence

Abstract

Customer relationship management (CRM) increasingly depends on the ability to transform large volumes of customer data into actionable business intelligence. This methodology paper develops a structured framework for applying data mining to customer identification, acquisition, retention, and development. The framework integrates transactional, demographic, interaction, and web-derived data; performs data cleaning and feature engineering; and evaluates clustering, classification, association-rule mining, regression, and ensemble techniques according to the CRM decision being supported. The proposed method links analytical outputs to business-intelligence dashboards and campaign actions so that model results are translated into customer segments, churn-risk scores, profitability estimates, and cross-selling recommendations. Prior studies show that data mining can strengthen marketing knowledge, improve churn targeting, support customer-value prediction, and expand the analytical role of CRM [1]-[8]. Business-intelligence research further emphasizes the importance of integrating data, analytics, and decision processes rather than treating model construction as an isolated technical activity [9]-[11]. The result is a reproducible, literature-grounded methodology suitable for comparative evaluation of CRM mining approaches without assuming that one algorithm is universally superior.