ADVANCED CUSTOMER SEGMENTATION IN E-COMMERCE: CLUSTERING TECHNIQUES AND THEIR IMPACT ON MARKETING STRATEGY OPTIMIZATION
Keywords:
Customer Segmentation, Demographic Segmentation, E-commerce, K-means Clustering, Marketing StrategyAbstract
This study compares traditional demographic segmentation and K-means clustering to optimize customer segmentation in e-commerce. Using the "Customer Personality Analysis" dataset from a UK-based retailer, it evaluates the effectiveness of these methods based on behavioral variables, including product expenditures, promotional engagement, and purchase channels. To test the hypotheses, K-means clusters were compared with demographic clusters. ANOVA assessed spending differences, while MANOVA examined whether K-means clustering provided distinct and actionable insights. Findings confirm that K-means clustering identifies behaviorally distinct customer groups, offering deeper insights and better marketing applications than traditional segmentation. However, practical challenges may limit its adoption. This research underscores the value of data-driven clustering techniques for precise and effective customer segmentation, improving business strategies. Future research should explore additional behavioral variables and validate these findings in real-world marketing applications.
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