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Predicting Consumer Profiles to Enhance Targeted Marketing

In: Strategic Innovative Marketing and Tourism

Author

Listed:
  • 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)

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.

Suggested Citation

  • Eleftheria Matta & George Stalidis & Kyriaki Dimitriadou, 2026. "Predicting Consumer Profiles to Enhance Targeted Marketing," Springer Proceedings in Business and Economics, in: Androniki Kavoura & Ulrike Gretzel & Vasiliki Vrana (ed.), Strategic Innovative Marketing and Tourism, pages 559-567, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-12968-0_61
    DOI: 10.1007/978-3-032-12968-0_61
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