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Multicriterion Market Segmentation: A New Model, Implementation, and Evaluation

Author

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  • Ying Liu

    (College of Business Administration, California State University, Long Beach, Long Beach, California 90840)

  • Sudha Ram

    (Eller College of Management, University of Arizona, Tucson, Arizona 85721)

  • Robert F. Lusch

    (Eller College of Management, University of Arizona, Tucson, Arizona 85721)

  • Michael Brusco

    (College of Business, Florida State University, Tallahassee, Florida 32306)

Abstract

Market segmentation is inherently a multicriterion problem even though it has often been modeled as a single-criterion problem in the traditional marketing literature and in practice. This paper discusses the multicriterion nature of market segmentation and develops a new mathematical model that addresses this issue. A new method for market segmentation based on multiobjective evolutionary algorithms, called MMSEA, is developed. It complements existing segmentation methods by optimizing multiple objectives simultaneously, searching for globally optimal solutions, and approximating a set of Pareto-optimal solutions. We have applied and evaluated this method in two empirical studies for two firms from distinct industries: descriptive segmentation of the cell phone service market from a dual-value creation perspective and predictive segmentation of retail customers based on profit and customer sociodemographic attributes. The results provide decision makers with compelling alternatives and enhanced flexibility currently missing in existing market segmentation methods.

Suggested Citation

  • Ying Liu & Sudha Ram & Robert F. Lusch & Michael Brusco, 2010. "Multicriterion Market Segmentation: A New Model, Implementation, and Evaluation," Marketing Science, INFORMS, vol. 29(5), pages 880-894, 09-10.
    • Handle: RePEc:inm:ormksc:v:29:y:2010:i:5:p:880-894
    DOI: 10.1287/mksc.1100.0565
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    Cited by:

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    3. Liu, Jiapeng & Liao, Xiuwu & Huang, Wei & Liao, Xianzhao, 2019. "Market segmentation: A multiple criteria approach combining preference analysis and segmentation decision," Omega, Elsevier, vol. 83(C), pages 1-13.
    4. Huerta-Muñoz, Diana L. & Ríos-Mercado, Roger Z. & Ruiz, Rubén, 2017. "An iterated greedy heuristic for a market segmentation problem with multiple attributes," European Journal of Operational Research, Elsevier, vol. 261(1), pages 75-87.
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    6. Vargo, Stephen L. & Lusch, Robert F., 2017. "Service-dominant logic 2025," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 46-67.
    7. Jong Seok Kim, 2017. "Empirical Analysis Of Consumer Willingness To Pay For Smart Phone Attributes In Multi-Countries," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-37, February.
    8. Benedek Botond & László Ede, 2019. "Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 64(2), pages 53-71, August.
    9. Liu, Jiapeng & Wang, Yan & Kadziński, Miłosz & Mao, Xiaoxin & Rao, Yuan, 2024. "A multiple criteria Bayesian hierarchical model for analyzing heterogeneous consumer preferences," Omega, Elsevier, vol. 128(C).
    10. Marco Vriens & Nathan Bosch & Chad Vidden & Jason Talwar, 2022. "Prediction and profitability in market segmentation typing tools," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 360-389, December.
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