A Parametric Constrained Segmentation Methodology for Application in Sport Marketing

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.