Measuring Sponsorships: Making the Business Case and Proving a Clear Path to Positive ROI
Sponsorships can be a powerful marketing tool, but without a clear way to measure success, they can become expensive experiments without an obvious business impact. Too often, brands invest in high-profile partnerships only to struggle to prove a positive return on investment. Understanding what works, what doesn’t and how to optimize future sponsorships requires a structured approach that ties sponsorship activity to meaningful metrics.
Regardless of the partnership type, we’ll discuss how to measure the ROI of your sponsorship marketing efforts.
Direct and Indirect Value of Sponsorship Marketing
Sponsorship agreements do not always offer a direct impact on sales, but there are still ways to quantify their value. For most brands, this requires having a POV on several ‘layers’ of business impact: direct sales impact, short-term indirect impact and long-term indirect impacts on the business.
- Direct Impact – These are sales that happen directly through channels related to a sponsorship. It could be sales of a beverage in a sponsored venue or event or QR scans on sponsorship campaign imagery that directly led to a purchase. These are typically the easiest to measure and evaluate.
- Indirect Short-Term Impact – Incremental sales that occur during the activation period that are attributable to the campaign, but not directly tied to it. These are sales that can come from people seeing sponsorships (i.e., logo placements) and having it impact their ensuing choices related to the brand. For example, someone sees that the brand is sponsoring their favorite team, and they decide to make a purchase from that sponsor the next time they are in store or shopping online. These are more difficult to measure. Attribution can be difficult, particularly for brands that don’t own their purchase cycle (more on that later).
- So, how can a brand know how much of its sales in a period are attributable to the sponsorship versus other marketing efforts? Some companies implement MMMs (Multi-Mix Models) or MTA (Multi-Touch Attribution) models with varying levels of success. We have seen these struggle with internal buy-in or adoption since they can be very technical and challenging to build. It is important to note that the exact methodology used needs to explicitly consider the industry and the business in question.
- Indirect Long-Term Impact – Indirect long-term impacts refer to broader increases in brand strength over and above sales that were realized during a sponsorship activation. These are impacts that outlive the sponsorship activation. This is one piece that many models do not account for (in particular, the MMM and MTA approaches are not really designed to address this part of the equation).
While the long-term effects can be difficult to measure, the real challenge is valuation. If we think about highly experiential activations, it is entirely possible for a branded experience to leave an impression on consumers that lasts long after the event or activation has ended. This can be captured in brand tracking studies as “lift,” in brand affinity or preference during a campaign. In high-performing campaigns, we can see this effect sustaining after the end of the campaign. The best ROI models can account for and place monetary value on these impacts.
Download our sponsorship playbook: The New Rules for Winning with Sponsorships
A comprehensive ROI measurement approach should combine sales data and brand metrics. For many brands, this might include a mix of internal and third-party data. As much as possible, these data sources should be longitudinal in nature and, wherever possible, provide metrics that allow you to identify the sponsorship audience (either directly or by proxy). This includes tracking exposure, audience engagement and overall brand sentiment continuously as much as possible.
Integration with Broader Marketing Initiatives
The right approach to measuring ROI is heavily influenced by your specific business or industry. Many technology or finance companies, for example, are able to build sophisticated MTA models that provide solid results. However, those same models do not apply to all industries. CPG companies, for example, who typically don’t own their purchase cycle, will find it more difficult to connect a purchase or store visit to prior sponsorship exposure.
For companies struggling to measure ROI, one methodology to consider is what we call a Treatment vs. Control approach. This involves identifying a “treatment group” that has been exposed to your sponsorship and comparing it to a lookalike “control” group to assess differences in behavior, brand attitudes, or other key metrics. While direct impact is often easier to measure through transactional data, this approach is especially powerful for quantifying both short-term and long-term indirect effects. It allows you to observe whether exposed audiences are more likely to purchase during the activation window and whether they show stronger brand affinity, intent, or perception shifts that persist beyond the campaign. In doing so, it helps bring structure and credibility to the layers of impact that are typically the hardest to isolate.
This approach has the benefit of being easy to understand and explain to stakeholders across your organization while being sufficiently flexible to adapt to different industries and categories. With the right approach, it can also provide directional guidance even in situations where data is relatively limited.
Getting this right requires thoughtful planning. We have seen studies fail because they don’t control for lurking variables or pre-existing differences between the treatment and control groups. The most credible results come from setting up the treatment vs. control framework before the sponsorship launches, enabling you to observe changes over time, strengthen causal inference, and build internal confidence in the outcomes.
After years of working with leading sponsorship brands, we’ve seen conclusively that sponsorships can deliver real business value. But that value only matters when it can be clearly measured, communicated, and used to inform future decisions. By combining structured methodologies like treatment vs. control testing with the right data inputs across sales and brand metrics, brands can move beyond gut feel and start making sponsorship decisions with the same rigor they apply to other parts of the business. The result isn’t just better measurement—it’s better decision-making and stronger ROI.
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