Predicting Consumer Profiles to Enhance Targeted Marketing

Eleftheria Matta, George Stalidis () and Kyriaki Dimitriadou
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Eleftheria Matta: International Hellenic University, Department of Organization Management, Marketing and Tourism
George Stalidis: International Hellenic University, Department of Organization Management, Marketing and Tourism
Kyriaki Dimitriadou: International Hellenic University, Department of Organization Management, Marketing and Tourism

A chapter in Strategic Innovative Marketing and Tourism, 2026, pp 559-567 from Springer

Abstract: Abstract This study addresses the need for behavior-based customer segmentation in the retail sector by introducing a novel methodological framework that combines multidimensional factor analysis with machine learning. The framework also yields strategic insights with direct implications for retail marketing, campaign management, and customer relationship development. Adopting a data-driven approach, the study uncovers behavioral patterns among supermarket customers in Greece. Using factor and clustering methods, six distinct shopper profiles were identified based on purchasing habits, store preferences, promotional responsiveness, and affinity for private label products. A predictive model was then developed to classify unknown customers into these profiles. The results provide practical tools for targeted marketing and customer engagement. Key business implications include enhanced personalization, improved customer loyalty strategies, and more efficient campaign planning, demonstrating the strategic value of integrating behavioral analytics into retail decision-making.

Keywords: Customer profiling; Retail marketing; Consumer behavior (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-12968-0_61

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DOI: 10.1007/978-3-032-12968-0_61

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