According to CreatorIQ’s “State of Creator Marketing: Trends and Trajectory 2024-2025” report – which surveyed 457 marketing decision-makers at brands – measuring program success has become the #1 challenge in influencer marketing.
And it makes sense: influencer data is messy – scattered timelines, unclear impressions, unpredictable performance. It doesn’t behave like other media channels, and most attribution models aren’t built for it.
But hard doesn’t mean impossible. This article will help you understand where the data breaks, what your models can still tell you, and how to make smart decisions anyway.
4 Reasons why influencer marketing breaks standard measurement models
Influencer marketing breaks the core assumptions most measurement models depend on.
1 – Unclear timing
Influencer deals are almost always bespoke. You might pay $10,000 for “four Instagram Stories and one YouTube mention over the next six months – whenever the creator feels like it.”
That means you’re often guessing when impressions will happen, or relying on interns and AI tools to retroactively piece together campaign timelines from story screenshots and mentions. Not a great foundation for a time-series model.
2 – Targeting precision
Influencer content lacks targeting precision, which means you can’t run a lift test and say, “this influencer is going to show our product only to their followers in Texas and not in California.” The audience sees what they see – broadly and asynchronously.
3 – Performance variance
Influencers are very binary in terms of their effect. Some influencers or posts will do nothing, and others will go viral. It’s not just that results vary between creators – it’s that they vary wildly between posts from the same creator. You’re dealing with 80/20 distributions inside of 80/20 distributions.
4 – Fragmented reporting
Influencer spend and performance often live across spreadsheets, Slack messages, agency decks, or are buried in Instagram DMs. Trying to rebuild a coherent dataset a year later is a nightmare that you can’t just solve by buying better tooling.
What you’re left with is a channel that’s highly variable, loosely structured, and often poorly tracked. It’s not immeasurable, but it is absolutely problematic.
What MMM can do
These are structural problems that your measurement strategy has to accept and work within their constraints. We’ve seen our media mix modeling generate meaningful insights on influencer performance – as long as you calibrate your expectations.
The first step is data hygiene. At a minimum, you want to track:
- Spend by creator
- Actual post dates (or your best estimates)
- Format and platform (e.g., IG Story, TikTok, YouTube mention)
The more your influencer program resembles a traditional channel with predictable patterns, the better the model will perform. You don’t need perfect data, but you do need structured, directional, clean data to feed your MMM.
But even in ideal conditions, you will need to adjust how you interpret the outputs.
MMM is built to estimate average effects. And that’s a feature, not a bug. But transparently, it means that viral posts, which drive “outsized impact” on rare occasions, get downweighted or ignored.
That’s why at Recast, we treat influencer channels with wider uncertainty bands than others. We still model them. But we don’t over-interpret single-week spikes or assume one-time wins will recur. When structured properly, influencer channels contribute directional clarity, and that’s enough to support smarter budget calls.
Decision-making under uncertainty: what marketing teams should actually do
The best marketing teams don’t wait for perfect measurement to act.
They combine multiple imperfect signals – MMM outputs, platform analytics, qualitative feedback from sales teams, and brand lift studies – and use that mosaic to inform decisions. They track what they can, acknowledge what they can’t, and build processes that allow them to move forward anyway.
This means setting clear success metrics upfront (even if they’re directional), documenting spend and timing consistently, and treating influencer performance as a portfolio rather than individual bets.
The work is making uncertainty manageable enough to allocate budget confidently.
TL;DR:
- Influencer campaigns break traditional measurement models because spend, timing, and reach are rarely aligned or clearly tracked.
- MMM can still produce useful insights on influencer performance, but only if spend and post activity are logged with structure and consistency.
- Viral hits often get ignored by MMM as outliers, so results should be interpreted with wider uncertainty bands and not overfit to spikes.
- The best-performing teams don’t chase perfect attribution, they instead take different directional inputs and place bets despite the uncertainty.
