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A Parametric Constrained Segmentation Methodology for Application in Sport Marketing

Author

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  • Wayne S. DeSarbo

    (Pennsylvania State University)

  • Qian Chen

    (Pennsylvania State University)

  • Ashley Stadler Blank

    (University of St. Thomas)

Abstract

While the sport industry is a multibillion dollar industry, there is a paucity of academic marketing research regarding the various aspects of the industry, especially concerning fan avidity—the level of interest, involvement, passion, enthusiasm, and loyalty a fan exhibits to a sport entity. This is somewhat surprising given that avid fans are the lifeblood of any sport organization, spending significantly more money, time, and effort on sport-related products than other consumers. Thus, given its importance to the sport industry, we examine the relationship between fan avidity and its various behavioral manifestations. Recognizing the existence of consumer heterogeneity among fans, we present a new parametric constrained segmentation methodology and corresponding estimation algorithm that incorporates managerial constraints pertinent to the sport industry (or any other industry) while simultaneously segmenting the market and profiling each segment. We conducted a Monte Carlo simulation, which demonstrates the successful performance of the estimation algorithm across various models, data, and error structures. Then, we applied our proposed methodology to college football data for a major US university and found evidence for two distinct market segments. Finally, we performed a series of model comparisons and showed that our parametric constrained segmentation methodology outperforms existing alternatives.

Suggested Citation

  • Wayne S. DeSarbo & Qian Chen & Ashley Stadler Blank, 2017. "A Parametric Constrained Segmentation Methodology for Application in Sport Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 4(4), pages 37-55, December.
    • Handle: RePEc:spr:custns:v:4:y:2017:i:4:d:10.1007_s40547-017-0086-7
    DOI: 10.1007/s40547-017-0086-7
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    References listed on IDEAS

    as
    1. DeSarbo Wayne S., 2010. "A Spatial Multidimensional Unfolding Choice Model for Examining the Heterogeneous Expressions of Sports Fan Avidity," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-24, April.
    2. Wayne DeSarbo & J. Douglas Carroll, 1985. "Three-way metric unfolding via alternating weighted least squares," Psychometrika, Springer;The Psychometric Society, vol. 50(3), pages 275-300, September.
    3. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
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    5. Michel Wedel, 2001. "GLIMMIX: Software for Estimating Mixtures and Mixtures of Generalized Linear Models," Journal of Classification, Springer;The Classification Society, vol. 18(1), pages 129-135, January.
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    7. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 21-55, March.
    8. Kamel Jedidi & Wayne DeSarbo, 1991. "A stochastic multidimensional scaling procedure for the spatial representation of three-mode, three-way pick any/J data," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 471-494, September.
    9. Wayne DeSarbo & Robert Madrigal, 2012. "Exploring the Demand Aspects of Sports Consumption and Fan Avidity," Interfaces, INFORMS, vol. 42(2), pages 199-212, April.
    10. Benaglia, Tatiana & Chauveau, Didier & Hunter, David R. & Young, Derek S., 2009. "mixtools: An R Package for Analyzing Mixture Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i06).
    11. Gordon, A. D., 1996. "A survey of constrained classification," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 17-29, January.
    12. Wayne Desarbo, 1982. "Gennclus: New models for general nonhierarchical clustering analysis," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 449-475, December.
    13. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    14. Roberts, John H. & Kayande, Ujwal & Stremersch, Stefan, 2014. "From academic research to marketing practice: Exploring the marketing science value chain," International Journal of Research in Marketing, Elsevier, vol. 31(2), pages 127-140.
    15. Wayne DeSarbo & Richard Oliver & Arvind Rangaswamy, 1989. "A simulated annealing methodology for clusterwise linear regression," Psychometrika, Springer;The Psychometric Society, vol. 54(4), pages 707-736, September.
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    Cited by:

    1. Yu Ding & Wayne S. DeSarbo & Dominique M. Hanssens & Kamel Jedidi & John G. Lynch & Donald R. Lehmann, 2020. "The past, present, and future of measurement and methods in marketing analysis," Marketing Letters, Springer, vol. 31(2), pages 175-186, September.
    2. Kevin H. Lee & Qian Chen & Wayne S. DeSarbo & Lingzhou Xue, 2022. "Estimating Finite Mixtures of Ordinal Graphical Models," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 83-106, March.

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