There is a measurement hierarchy in marketing, and sponsorship sits near the bottom of it.
Advertising has econometric modelling, brand tracking, footfall analysis and market research. Digital has multi-touch attribution, data clean rooms, and multivariate testing. Even out-of-home has moved toward audience measurement frameworks with some grounding in actual behaviour. Sponsorship, meanwhile, has spent decades defending itself with a number that tells you almost nothing: advertising value equivalency (AVE)
AVE is the practice of calculating how much it would cost to buy equivalent media space and treating that figure as proof that a sponsorship worked and, in the worst cases, used to calculate ROI (if a sponsor provides £20m of advertising value at £5m cost, the ROI is said to be 3:1).
But it’s not proof of anything. While measuring the amount of exposure a sponsor receives is a critical first step in attribution, AVE is a faulty cost comparison dressed up as business effect. And the fact that it remains the dominant measurement currency in much of the industry is not just a methodological problem, it is a commercial one.
This problem is decades in the making. Rights holders first began to truly scale their commercial operations after the Los Angeles Olympics of 1984 showed they could turn a serious profit, and the people they turned to were talent agents, not marketers.
The big names of the last 40 years — Wasserman, CAA, IMG — came from Hollywood, not Madison Avenue. They didn’t even consider themselves to be in the same industry as Stephen King and Stanley Pollitt, the godfathers of account planning. When they were asked to demonstrate value, they didn’t know much about econometrics.
The tobacco industry’s use of sponsorship in F1 in the 80s and 90s cemented exposure as the metric above all others in the minds of those tasked with selling the deals, and AVE emerged to try to put a financial value on it.
Times have moved on, and there are good strategists working within the big sponsorship agencies. But the exposure-first structure and muscle memory became deeply embedded and continues to dominate sponsorship negotiations, which is why you’ll still meet people working in sponsorship who’ll say they don’t work in marketing.
Why AVE Survives
AVE persists for the same reason bad habits persist: it is easy, it produces large numbers, and it gives both sides of the deal something to put in a slide.
Rights holders use it to justify fee levels, sponsors use it to report back to boards who are not asking hard questions. Agencies use it to show clients an ROI figure without having to do the harder work of establishing what return actually looks like for a given business objective. Everyone gets to feel good about a number that has no agreed methodology, no standard for what counts as equivalent, and no connection to whether anybody bought anything, changed their mind about anything, or felt differently about a brand.
The Chartered Institute of Public Relations banned AVE from its awards process years ago. AMEC, the global measurement body, has been campaigning against it for over a decade. And yet here we are.
What AVE Gets Wrong About Advertising
Before even getting to the attribution problem, AVE rests on a misunderstanding of what advertising actually is.
No brand buys a 30-second TV spot to show its logo drifting in and out of focus while people play tennis in front of it. A television ad is a piece of craft. It has a narrative, a message, emotional architecture, a call to action. It is effective precisely because the advertiser controls every frame of it, and that control is what justifies the rate card.
A perimeter board could never do what an ad does. The exposure is real and has value because sponsorship does generate hundreds of millions of impressions across broadcast, highlights packages, viral clips, and back pages of national newspapers. But the communicative value of that exposure is categorically different from a bought media unit.
Any honest AVE calculation would need to apply a significant downward coefficient to account for the reduction in creative control and message quality. In practice, most do not. The full rate card gets used, the impression count gets multiplied out, and the resulting number gets treated as equivalent value. But it’s not even close.
The MMM Problem
The deeper issue is not that sponsorship uses a bad metric. It is that the industry has not yet built the infrastructure to use good ones.
There is a narrative within the sponsorship sector that we just need someone to finally crack the measurement issue, and find the one number that everyone can agree on that finally communicates the bottom line return on sponsorship. As anyone who has written an IPA paper will tell you, effectiveness isn’t as simple as that.
I remain convinced that MMM is a key component of the answer, and advances in AI will make it much more accessible to more brands. But sponsorship creates a specific structural problem for marketing mix modelling that goes beyond data availability: it is not a single channel.
Advertising can be entered into an MMM model as a discrete variable. Television spend is television spend and TVRs are robust. But a sponsorship of a Premier League club, for instance, generates impressions via broadcast, via stadium attendance, via social video, via press photography, via OOH around the ground, via influencer content, via branded activations. Each of those channels has different reach, different audience composition, different creative quality. You cannot collapse them into a single input without losing most of what is actually happening.
Some MMM practitioners handle this by adding sponsorship as an environmental factor — a background condition rather than an active variable — but that framing does not reflect the exposure logic that AVE is at least trying to capture.
This is a genuinely hard problem. It won’t be solved by dismissing econometrics, which remains the most rigorous tool available for connecting marketing investment to commercial outcomes. But it does require building sponsorship-specific models that can disaggregate the impression footprint by channel, apply appropriate quality weights to each, and use those inputs in a way that reflects the actual mechanics of how the property generates value.
That model then needs to be used within the triangulation of data sources that Les Binet and the IPA has advocated for years — MMM, plus MTA, plus brand tracking, with experiments and tests.
This would enable brands or rights holders to not just isolate the effects of a perimeter board compared to a social content series, but also begin to understand which elements of a sponsorship activation are better for long-term brand effects and short-term sales activation.
And, in turn that would lead to better deals that are more tailored to the marketing objectives of the sponsor.
The Sponsorship Effectiveness Forum
Another thing I’ve observed in the three years since I began working in the sponsorship sector, is the lack of an effectiveness-focused trade body. The European Sponsorship Association (ESA) does encompass measurement, but its remit is far wider than measurement alone. Its awards program, alongside that of Sport Industry Group and the UK Sponsorship Awards, asks for demonstration of results, but the bar for evidence is lower than for the Effie, WARC or IPA awards; exposure and engagement numbers are still prominent.
There is also more of a culture of secrecy. Rights holders often find brands unwilling to share commercial results because at the end of the deal a new round of renewal discussions will take place. If the rights holder knew the true bottom line value,, the price would go up, or so goes the argument.
Which is why, in November 2025, I founded the Sponsorship Effectiveness Forum (SEF). Our first publication, The Sponsorship Effect, used an analysis of 92 case studies to demonstrate the effectiveness of sponsorship using the same taxonomy of business, brand, sales and behavioural effects of the IPA et al. It highlighted that the most effective ‘Tier 1’ cases used 3.1 measurement approaches, on average, compared to 2.4 approaches amongst others, showing that at least some are using the triangulation approach.
My ultimate goal, however, is to create an open cross-industry space to share best practice and measurement approaches, because I believe that’s what will enable the sponsorship sector to overcome its crisis in attribution.
