Forecasting sponsorship costs: marketing intelligence in the athletic apparel industry

Abstract

Purpose

Due in large part to the proprietary nature of costs, there is a dearth of academic literature investigating the factors influencing the costs for sport marketing investments, such as sponsorship. Therefore, the purpose of this paper is to provide an analytical framework for market intelligence that enables managers to better predict and forecast costs in today’s ever-changing sport marketing environment.

Design/methodology/approach

Given the dynamic and ultra-competitive nature of the athletic apparel industry, this context was chosen to investigate the influence of four distinct factors on sponsorship costs, including property-specific factors, on-field performance, and market-specific factors. A systematic, hierarchical procedure was utilized in the development of a predictive empirical model, which was then utilized to generate predicted values on a per property basis.

Findings

Results demonstrated that both property-specific and performance-related factors were significant predictors of costs, while variables reflecting the attractiveness of the property’s home market were non-significant. Further analysis revealed the potential for agency conflicts in the allocation of resources toward properties near the corporate headquarters of sponsors, as well as evidence of overspending by challenger brands (Adidas, Under Armour) in their quest to topple industry leader Nike.

Originality/value

Though the context of apparel sponsorships of US-based intercollegiate athletic programs limits the generalizability of the results, this study represents one of the few in the literature to empirically investigate the determinants of sponsorship costs, providing much-needed guidance to aid decision making in a highly volatile, unpredictable industry.

Citation

Jensen, J.A., Wakefield, L., Cobbs, J.B. and Turner, B.A. (2016), "Forecasting sponsorship costs: marketing intelligence in the athletic apparel industry", Marketing Intelligence & Planning, Vol. 34 No. 2, pp. 281-298. https://doi.org/10.1108/MIP-09-2014-0179