Using a unique data set of F1 team sponsorships in 2007, we advanced research on sponsorship costs by constructing a model of sponsorship rights fees that tested several theory-based elements of the sponsorship relationship and controlled for characteristics of the sponsored team. Results indicated that the level of sponsorship affiliation (p < .01) and ability of the sponsor to trade value-in-kind (p < .01) were significant predictors of the cost of sponsor affiliation. In addition, the sponsor’s congruence with the team, in the form of a shared nationality (p < .05) and product category (automotive and technology) (p < .05), were also significant influences of sponsors’ cost. As control variables, the points scored (p < .01) and years of experience of the sponsored team (p < .01) were also significant influences of the sponsors’ cost. In the concluding analysis, we employed logistic regression to examine the potential contributors to surplus ROI for F1 team sponsors. Only 26 (10.1%) of 257 F1 team sponsors realized exposure value that outweighed sponsorship costs (i.e., surplus ROI). The sponsored team’s points scored (p < .01) and the sponsor’s operation in the automobile industry (p < .01) emerged as significant predictors of surplus ROI.

1. Investigating the Cost-Benefit of Sponsorship:
An analysis of price, exposure,
and marginal returns in motorsports
Jonathan Jensen & Joe Cobbs

2. Introduction
• “Measuring return on investment is an
important challenge. We've got to get better at
it.”
- Adam Harter, Vice President - Consumer
Engagement, PepsiCo (McCarthy, 2013)
• Across its many brands, PepsiCo spends more on
sponsorship rights fees than any other U.S.
corporation, nearly $350 million annually
(IEG, 2012)

3. Introduction
• Quantifying ROI for marketing resources remains
an open debate (Rust, Lemon, & Zeithaml, 2004)
• Economy has raised scrutiny on marketing
expenditures and returns (Cobbs, Groza and
Pruitt, 2012; Cornwell, 2008)
• Non-traditional marketing techniques lack
universal metric (i.e., GRP for traditional media)
to allow comparison across mediums (Wishart,
Lee and Cornwell, 2012)

4. What’s Different About This Study?
• Most studies of sponsorship effectiveness utilize
experimental designs to isolate consumer effects (e.g.,
Levin, Joiner, & Cameron, 2001; Olson & Thjømøe, 2009)
• Utilizes actual data on Formula One (F1) sponsorship costs
and returns (in the form of television exposure data)
• Sponsorship ROI
measure for one of the
world’s most glamorous
(and expensive!) sports

5. Exposure as Benefit of Sponsorship
• Exposure is critical to quantifying the benefits to
sponsorship (Pope & Voges, 1994; Pruitt, Cornwell,
& Clark, 2004)
• Exposure is precursor to brand awareness, which is
an antecedent to brand equity
• Brand equity is the objective of many marketing
tactics, including advertising and sponsorship
(Aaker, 1996; Keller, 1993; Ross, 2006)

6. Influencing Exposure
• Three highly significant models (F = 35.05 to 58.13;
p < .001; N = 55) featuring team points and wins as
significant predictors (p < .01)
• For each team point scored, the team’s sponsors
enjoyed an aggregate increase in brand exposure of
670,000€ (US$822,157)
• Win produced incremental team sponsor exposure
in excess of 21m€ (US$25.8 million)
• Analysis empirically substantiates the brand
exposure power of sponsoring an F1 team that wins
races (thereby earning additional points)

7. Modeling Brand Exposure
TABLE 1
Regression Models Predicting F1 Sponsor Exposure Value (Mil) by Team
Variable Model 1 Model 2 Model 3
Constant 6.62 131.39 31.96
(19.82) (26.29) (28.35)
Year 2006 66.49** 10.06 65.28**
(23.72) (26.34) (23.60)
Year 2007 18.88 -49.15 13.10
(23.77) (26.14) (24.09)
Year 2008 17.05 -43.18 14.27
(23.72) (26.19) (23.69)
Year 2009 20.85 -57.20* 10.96
(24.26) (27.22) (25.41)
Team Points .67** .62**
(.11) (.12)
Team Wins 21.21** 35.61** 20.89**
(3.84) (3.24) (3.82)
Did Not Finish -4.64* -1.93
(1.82) (1.55)
R2 .88 .81 .88
Adjusted R2 .86 .79 .87
* p < .05 ** p < .01; Standard errors in parentheses. Constant is representative of effect of excluded year (2010).

8. Influencing Costs
• Most sponsorship agreements are highly
confidential (Wishart, Lee and Cornwell, 2012)
• Little attention to sponsorship costs (Cobbs, Groza
& Pruitt, 2012; Cornwell, 2008)
• We model sponsorship rights fees to test several
theory-based elements:
• sponsorship level
• value-in-kind
• industry congruence
• nationality congruence
• controlling for characteristics of sponsored team (exposure,
points and experience)

9. Results: Costs
• Utilizing dichotomous variables, level of
sponsorship affiliation (p < .01) and ability of the
sponsor to trade value-in-kind (p < .01) were
significant predictors of the cost of sponsor
affiliation (N = 76)
• Sponsor’s congruence with the team, in the form of
a shared nationality (p < .05) and industry
category (automotive and technology) (p < .05),
were also significant influences of sponsors’ costs.
• As control variables, points scored (p < .01) and
years of experience of the sponsored team (p <
.01) also significant influences of cost in rights fee.

10. Modeling Sponsorship Costs
TABLE 2
Regression Models Predicting Price (mil €) of F1 Team Sponsorship
Variable Hypothesis Model 1 Model 2 Model 3 Model 4 Model 5a
[Constant] 1.95^ 1.58 1.60 1.66 4.90**
(1.06) (1.04) (.98) (1.02) (.72)
Top level sponsor [0,1] H1 5.15** 4.82** 4.71** 4.69**
(.92) (.91) (.88) (.90)
Value-in-kind [0,1] H2 -2.64* -2.43* -2.16* -2.13* -4.86**
(1.02) (1.00) (.97) (.99) (.66)
Industry congruence [0,1] H3 1.55* 1.52* 1.46* 1.46* .48
(.74) (.72) (.69) (.70) (.41)
Nationality congruence [0,1] H4 -.34 -.27 -.40 -.38 -1.20*
(.87) (.85) (.82) (.84) (.49)
R2 .50 .52 .55 .56 .56
Adjusted R2 .46 .49 .52 .51 .52
^ p < .10 a Model 5 excludes top level sponsors.
* p < .05 Notes: N = 76 (except Model 5, where N = 60). Standard errors in parentheses.
** p < .01

11. Results: ROI
• Binary logistic regression (0 = negative ROI, 1 =
positive ROI) to examine the factors influencing
positive ROI (N = 122)
• Only 26 (10.1%) of the 257 F1 team sponsors
realized exposure value that outweighed
sponsorship costs (i.e., positive ROI)
• The sponsored team’s points scored (p < .01) and
the sponsor’s operation in the automobile
industry (p < .01) emerged as significant
predictors of ROI.

12. Modeling ROI
TABLE 3
Logistic Regression Model Analysis of Sponsorship ROI
Variable β Wald's/ χ2 df p e^β (odds ratio)
Constant -1.94 22.30 1 0.00 0.14
Team points 0.01 10.90 1 0.00 1.01
Top level sponsor (0,1) 0.86 2.71 1 0.10 2.37
Automobile industry (0,1) -1.64 6.70 1 0.01 0.19
Model 21.96 3 0.00
-2 Log likelihood 104.45
Cox & Snell R-square 0.16
Nagelkerke R-Square 0.26
Hosmer & Lemeshow
Goodness-of-fit
12.01 8 0.15
Notes: N = 122, No. of F1 team sponsors that received televised brand exposure in 2007.

13. Implications & Limitations
• Be prepared to commit monetary and VIK resources
to top teams if seeking returns via exposure
• Look for nationality congruence but beware of
potential industry congruence markup
• Very limited scope of return objectives measured in
this study (televised exposure)
• Costs frequently expand beyond rights fees

14. ROI: Exposure v. Cost

15. ROI: Exposure v. Cost