The challenges of sponsorship measurement, and a few lessons we’ve learned
Welcome to our last newsletter of the year, which gives us the opportunity to send you our best wishes for a peaceful Christmas break and thank you all for your interest and support throughout this incredible year.
Back to business, a few weeks ago one of our Principals, Mick Hedberg, took the virtual stage along with our partners at HP to present at the Advertising Research Foundation (ARF) Attribution and Analytics Accelerator.
The conference focused on the science of marketing performance, and we presented our recent work measuring the impact of a long-term sponsorship deal for HP’s Industrial Printing division. Like many sponsor brands, they lacked clear, quantitative indicators to make decisions on renewal and future investment levels despite positive word of mouth and good feedback from stakeholders.
In the sections below we will take you through some of the key lessons we learned. We hope these will be of use to anyone considering sponsorship as part of their marketing strategy.
Take a long-term view on impact
One of the fundamental challenges with measuring sponsorships is that there are a range of ways that they can affect a brand. In our work with HP there was a strong hypothesis that the sponsorship was impacting business performance in both direct (near-term sales impact) and indirect ways (long term brand and reputation building).
In our experience many sponsorships will not generate a positive Return on Investment simply through near-term sales uplifts; they require a holistic view of impact to justify the investment. However, properly evaluating longer-term/indirect effects can be challenging as their impact on the business may take place over many years. This can make it difficult to generate a point of view on ROI, complicating decisions about future investment levels.
There is no silver bullet solution, but the first step to generating a holistic view on ROI is to ensure that you have viable metrics to track the more indirect/longer-term impacts (whether that be overall brand health, company reputation, etc.). Ideally these should be metrics which are well established within your organization with a proven track record of predicting real business outcomes.
In our work with HP we focused on Net Promoter Score (NPS) and were able to show that the sponsorship had a statistically significant impact on that metric (i.e., customers who became aware of the sponsorship tended to become more likely to recommend the company’s products and services in future surveys). Once we were able to size that impact in % terms, we were able to leverage internal benchmarks and industry data to estimate the dollar value to the business.
Watch out for double counting (and some thoughts on how to account for it)
While it is important to take a holistic view on how sponsorship marketing can impact brand performance, this can also open up new analytical challenges. It is critical to account for the risk of ‘double counting’, which can lead to overstating the total impact and make it difficult to properly plan investment decisions.
Many businesses will have seperate data systems in place for tracking different parts of the business. For example, in the B2B environment it is common to have one Customer Relationship Management (CRM) data system for tracking leads, sales, and touchpoints and another data system for tracking brand health and reputation. Although these systems may exist in silos, in the real world the measures that they track interact in complex ways to explain business outcomes. That is, actual business outcomes will be explained by some combination of the brand’s reputation, the ongoing touchpoints and sales activities, and the other competitive and contextual factors in the market.
To avoid over-stating the total impact of the sponsorship in our recent work, we had to generate a point of view on how much of the overall business was driven by these different areas. We could measure the impact of the sponsorship within multiple data silos, yet we needed to understand how much weight to place on each silo to get to a total view on impact.
Put simply, a metric like NPS will explain some percentage of business outcomes. If that percentage is small, even a massive impact on that metric should not be assigned a high $ value when calculating the ROI of a sponsorship. To resolve this we leveraged industry benchmarks along with a multiple regression analysis to size different business drivers.
Try to understand HOW and not only HOW MUCH
Although the critical question is often how much impact a sponsorship has on the business (and whether it justifies the investment), understanding how (or by what mechanism) it impacts the business can be equally important to the success of a partnership.
In our work with HP we built our models from the bottom-up – meaning that we actually ran the analysis across several different geographies and customer segments and then subsequently aggregated the results into a combined summary of impact. By taking this approach from the beginning of the project, we ended up with a much more actionable set of recommendations when it came time to report the results.
For example, we were able to highlight the fact that the sponsorship disproportionately impacted win rates among new customers (as opposed to winning repeat business with existing customers) and that most of the sponsorship’s impact took place in one key region of the business. As a result of these learnings, HP was able to better understand how the sponsorship should fit alongside its other marketing plans and was able to go back to the rights-holder with a set of new ideas for the future of the partnership.
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