Dynamics and Peer Effects of Brand Revenue in College Sports

Branding plays a key role in attracting corporate sponsorship and raising revenue for the multi-billion dollar business of college sports. In this paper, we study the peer effects of brand performance in the dynamic revenue evolution of major college sports teams that play in the National Collegiate Athletic Association conferences. To examine the peer effects for each conference, we propose a novel model that can identify conference-specific peer effects in the setting of the dynamic evolution of brand revenue. Our estimation results reveal significant positive peer effects in four major conferences — Big 12, Big Ten, Southeast Conference, and Pacific-12 — indicating that teams benefit from playing in a conference with teams of strong brands. Our approach also sheds light on the effect of conference switches on a team’s brand revenue. By constructing the counterfactual brand revenue trajectory of not switching conferences for a switched team, we show the difference between the counterfactual trajectory and the actual brand revenue evolution after switch, which quantifies the effect of a conference switch. We also study the case where a team had considered but did not make a conference switch.

[1]  Steven L. Scott,et al.  Inferring causal impact using Bayesian structural time-series models , 2015, 1506.00356.

[2]  Puneet Manchanda,et al.  When Harry Bet with Sally: An Empirical Analysis of Multiple Peer Effects in Casino Gambling Behavior , 2014, Mark. Sci..

[3]  Jia Li,et al.  Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth , 2013, Mark. Sci..

[4]  Doug J. Chung.  The Dynamic Advertising Effect of Collegiate Athletics , 2013, Mark. Sci..

[5]  Prasad A. Naik,et al.  Discovering how Advertising Grows Sales and Builds Brands , 2012 .

[6]  Kenneth Gillingham,et al.  Peer Effects in the Diffusion of Solar Photovoltaic Panels , 2012, Mark. Sci..

[7]  Michael L. Anderson.  The Benefits of College Athletic Success: An Application of the Propensity Score Design with Instrumental Variables , 2012 .

[8]  J. LeSage,et al.  Interpreting dynamic space-time panel data models , 2012 .

[9]  Prasad A. Naik,et al.  Spatiotemporal Allocation of Advertising Budgets , 2011 .

[10]  Oliver J. Rutz,et al.  From Generic to Branded: A Model of Spillover in Paid Search Advertising , 2011 .

[11]  Carl F. Mela,et al.  The Long-Term Effect of Marketing Strategy on Brand Sales , 2010 .

[12]  Michel Wedel,et al.  Moment-to-Moment Optimal Branding in TV Commercials: Preventing Avoidance by Pulsing , 2010, Mark. Sci..

[13]  J. Elhorst.  Spatial Panel Data Models , 2010 .

[14]  Romana Khan,et al.  Dynamic Customer Management and the Value of One-to-One Marketing , 2009, Mark. Sci..

[15]  Avi Goldfarb,et al.  Estimating the Value of Brand Alliances in Professional Team Sports , 2009, Mark. Sci..

[16]  Michael Lewis.  Individual Team Incentives and Managing Competitive Balance in Sports Leagues: An Empirical Analysis of Major League Baseball , 2008 .

[17]  Suresh P. Sethi,et al.  Building Brand Awareness in Dynamic Oligopoly Markets , 2008, Manag. Sci..

[18]  Carl F. Mela,et al.  Building Brands , 2008, Mark. Sci..

[19]  Lung-fei Lee,et al.  Identification and estimation of econometric models with group interactions, contextual factors and fixed effects , 2007 .

[20]  Sangyoung Song,et al.  Neighborhood effects and trial on the internet: Evidence from online grocery retailing , 2007 .

[21]  J. LeSage,et al.  Spatial Growth Regressions: Model Specification, Estimation and Interpretation , 2007 .

[22]  Bernard Fortin,et al.  Identification of Peer Effects through Social Networks , 2007, SSRN Electronic Journal.

[23]  Sumit K. Majumdar,et al.  Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship , 2007 .

[24]  Carl F. Mela,et al.  Consumer Packaged Goods in France: National Brands, Regional Chains, and Local Branding , 2007 .

[25]  Bart J. Bronnenberg,et al.  Consumer Packaged Goods in the United States: National Brands, Local Branding: , 2007 .

[26]  Carl F. Mela,et al.  The Role of Spatial Demand on Outlet Location and Pricing , 2006 .

[27]  Kevin Lane Keller,et al.  Brands and Branding: Research Findings and Future Priorities , 2006 .

[28]  Eric T. Bradlow,et al.  Spatial Models in Marketing , 2005 .

[29]  Wolfgang Jank,et al.  Understanding Geographical Markets of Online Firms Using Spatial Models of Customer Choice , 2005 .

[30]  Michael Lewis,et al.  Research Note: A Dynamic Programming Approach to Customer Relationship Pricing , 2005, Manag. Sci..

[31]  Jacob Goldenberg,et al.  From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success , 2004 .

[32]  Carl F. Mela,et al.  The Dynamic Effect of Innovation on Market Structure , 2004 .

[33]  Joris Pinkse,et al.  Spatial Price Competition: A Semiparametric Approach , 2002 .

[34]  Bart J. Bronnenberg,et al.  Using Multimarket Data to Predict Brand Performance in Markets for Which No or Poor Data Exist , 2002 .

[35]  Vijay Mahajan,et al.  Unobserved Retailer Behavior in Multimarket Data: Joint Spatial Dependence in Market Shares and Promotion Variables , 2001 .

[36]  Carl F. Mela,et al.  Managing Advertising and Promotion for Long-Run Profitability , 1999 .

[37]  Michael A. West,et al.  Bayesian Forecasting and Dynamic Models (2nd edn) , 1997, J. Oper. Res. Soc..

[38]  R. Kohn,et al.  On Gibbs sampling for state space models , 1994 .

[39]  S. Frühwirth-Schnatter.  Data Augmentation and Dynamic Linear Models , 1994 .

[40]  C. Manski.  Identification of Endogenous Social Effects: The Reflection Problem , 1993 .