By Thomas Vladeck, Co-Founder of Recast
A single Super Bowl spot can cost over $8 million for thirty seconds of airtime and that’s before factoring in production, talent, and the media amplification strategy around it. For many enterprise CMOs, this represents the single largest line item of their annual marketing budget and the highest-stakes campaign they’ll deploy all year.
Given the investment, it’s no surprise that companies pour significant resources into measuring whether their Super Bowl campaigns worked (or not). The problem is that much of this measurement is fundamentally flawed – not because the ads failed, but because the measurement approach itself introduces systematic bias.
Three common mistakes consistently lead CMOs to draw the wrong conclusions from their most important campaigns. These aren’t obscure statistical technicalities, but rather, are foundational assumptions baked into most complex marketing measurement methods that actively distort how Super Bowl performance gets evaluated.
The all-too-frequent result is that campaigns that actually worked get under-credited, future budgets get cut, and organizations slowly retreat from upper-funnel investments. Understanding these mistakes is the first step toward a measurement strategy that reflects what actually happened on game day, and what it means for next year’s plan.
Mistake #1: Treating seasonality as something to “control for”
For most Super Bowl advertisers, measurement includes running a marketing mix model (MMM) – a statistical approach that estimates the incremental impact that each marketing channel contributes to business outcomes. But how these models handle seasonality can make or break their read on Super Bowl campaign efficacy.
Many MMMs treat seasonal spikes as noise to be removed from the model. The underlying formula looks something like Sales = Baseline Demand + Marketing Activity + Seasonality.
This is problematic because the formula assumes marketing performs independently of seasonal demand – but that’s never true in practice. Your marketing performs better when demand is high. That’s exactly why you bought a Super Bowl spot in the first place.
When a marketing model “controls for” the Super Bowl as a seasonal event, it attributes the demand surge to the calendar rather than your campaign. The result is that your investment gets systematically under-credited, and the model tells you exactly the wrong thing – that you should spend less during the moments when consumers are most likely to buy.
The solution isn’t to ignore seasonality, but to model it without controlling for it. That means allowing your model to recognize that marketing effectiveness itself changes with context. Your Super Bowl ad didn’t perform in a vacuum; it performed during the Super Bowl, when attention was high and consumers were primed to engage. A good model captures that reality rather than stripping it away. The question you want answered isn’t “How would this ad have performed in March?” but “Given that we ran this ad during the Super Bowl, did we capitalize on the moment effectively?”
Mistake #2: Expecting ROI to be static
Another common issue with complex marketing models is that they produce single incremental ROI estimates per channel, averaged across months or quarters. That can be useful in some cases, but it completely misses the nature of a Super Bowl buy where the impact of a campaign is not consistent over time. Attention is concentrated, engagement spikes, and your message hits differently when it airs during one of the most-watched television events in the world.
A model that assumes static ROI will blur all of that nuance by treating the return on your Super Bowl Sunday spot the same as a rerun on any given Sunday. This flattens the value of the marketing activation and makes it harder to understand whether the campaign worked at the moment when it mattered.
To solve this, the structure of a marketing model must be time-varying, meaning that it should estimate how marketing effectiveness varies by week, or even by day. Maybe your TV ROI on game day was 8.4x, then the week after it fell to 3.8x while Branded Search queries picked up. Insights like these are what let you assess the full context of marketing performance, but are missed without a thoughtful marketing model structure.
Mistake #3: Ignoring how channels interact
Super Bowl campaigns rarely drive conversions immediately. Their impact often shifts across time and over multiple touchpoints (think brand search, social retargeting, affiliate, in-store, etc.) before showing up as a sale. But many models of marketing performance still evaluate each channel in isolation, missing the handoffs that occur between them. This creates a mirage where lower-funnel channels look strong, while the upper-funnel work that created the demand appears weak.
A more accurate timeline looks like the following:
- Your Super Bowl ad airs on Sunday, and drives a spike in search volume and website traffic that continues through the following week.
- In the weeks after, social retargeting and brand search volume increase meaningfully.
If your marketing model doesn’t connect the dots between these conversion paths and channel types, it will consistently misattribute the outcome. The campaign that sparked demand gets largely ignored after the Super Bowl, while the channels that captured it get all the credit.
To fix this, your model must be able to estimate cross-channel effects, namely how upper-funnel activations drive lower-funnel conversions over time.
How to avoid fumbling your measurement strategy
Getting Super Bowl measurement right isn’t about having more data or a perfect model. It starts with asking better questions:
- Does our model structure account for time-sensitive changes in channel performance?
- Are we removing the very signals that we should be measuring?
- Can we trace how different media channels interact and contribute to eventual results?
These questions are especially important to get right because they’ll shape how next year’s media budgets get set. When your marketing model under-credits a campaign like the Super Bowl, it impacts both how your ads are remembered and whether you get the budget to run something similar next year. Over time, these distortions can lead your organization to systematically underinvest in upper-funnel work and fail to recognize the true value of their marketing team.
With game day just around the corner, now’s the time to pressure-test whether your measurement setup reflects how marketing actually works: in spikes of attention, shifts in timing, and campaign effects that ripple across channels. Get it right, and you won’t just prove this year’s investment – you’ll earn the chance to make an even bigger one next year.
