How to Tell Whether a Creator Campaign Actually Worked
Views are the number everyone reports and the one that tells you least. What to measure instead, how to set it up before the post goes live, and why the honest answer is sometimes that you cannot know.
The most common way a creator campaign gets evaluated is that someone reports the view count and everyone agrees it went well. Views are the easiest number to obtain and the least informative one available: they tell you the content was delivered, which was never seriously in doubt.
What you want to know is whether anyone was persuaded. That requires deciding, before anything is published, what would count as evidence.
Decide the measure before the brief goes out
This is the whole discipline, and it is why measurement belongs in the brief rather than in a conversation afterwards.
Once the post is live, the creator wants it judged on reach, whoever commissioned it wants it judged on whatever number looks best, and nobody has an incentive to apply a standard that might return a negative answer. Written down in advance, none of that arises.
Three things to agree:
- What is being counted. Sales, signups, bookings, or something softer.
- How it is tracked. Code, landing page, or platform metric.
- What number would count as working. An actual figure, before you know the outcome.
The third is the one people skip, and the one that makes the exercise honest.
The tracking that actually works
A unique discount code per creator is the most reliable method available to most businesses. It attributes at the point of purchase, survives someone buying three days later on a different device, and needs no analytics setup. Its weakness is that codes get shared and posted to deal sites, which inflates attribution rather than losing it.
A dedicated landing page per creator is cleaner for anything without a checkout. It also shows how many people arrived and did nothing, which is often the most useful number in the exercise: a creator who sent 900 visitors who all bounced has told you something specific about audience fit.
Platform metrics are worth collecting and worth discounting. Reach, saves and sends describe how the platform distributed the content, not how people responded commercially. Saves and sends are the more meaningful of them, for the reasons covered in how the Instagram algorithm actually works.
Self-reported attribution. Add "how did you hear about us?" as a free-text field at checkout. It is unscientific, people forget, and it is still the only method that catches the customer who saw a creator in March and bought in July.
The attribution problem, stated honestly
Creator marketing is systematically under-credited by standard analytics, and it is worth understanding why rather than arguing about it.
Someone sees a post on Tuesday and does not click. On Saturday they search your brand name and buy. A last-click model records that as organic search, and it does so by construction: the model assigns all credit to the final interaction. Google documents the trade-offs between attribution models in its Analytics help, and knowing which model your reporting uses is the difference between a channel looking worthless and looking essential.
This cuts both ways, which is the part usually left out. The same gap that hides genuine creator-driven sales also lets people credit a campaign with sales it had nothing to do with. Neither direction is a reason to abandon measurement; both are reasons to prefer a method that does not depend on the last click.
Two approaches help without pretending to precision:
Watch total volume, not just tracked volume. If overall sales rose measurably in the week a campaign ran and nothing else changed, that is evidence even without clean attribution. The "nothing else changed" is doing real work in that sentence.
Compare across creators rather than against a target. Absolute return is hard to measure. Relative performance between five creators running the same offer with the same tracking is much easier, and it is the number you need for the next decision anyway. The platform's own reporting is worth reading alongside it, and both major ad systems document what their conversion figures do and do not include - Meta's business resources set out the modelling their attribution applies.
What to record afterwards
Whatever the outcome, write down three things while they are fresh:
- The audience fit signal. Click-through rate, and whether the people who arrived behaved like customers.
- The content signal. Did this creator's version outperform the others, and if so what was different about it.
- The cost per outcome you actually care about, alongside the cost per thousand views, so the next negotiation has a benchmark.
That record compounds. After four or five campaigns you have your own benchmarks, which is worth considerably more than any published industry average, because it reflects your product, your price and your market rather than someone else's.
When the answer is that you cannot tell
Sometimes the honest conclusion is that the campaign was too small, the tracking too leaky, or the sales cycle too long to attribute anything with confidence.
Say that. A measurement that reports "inconclusive, and here is what we would need to change to get an answer" is more useful than a number everyone privately knows is decorative. It also tells you what to fix before the next campaign, which a flattering figure never will.
General guidance. Attribution behaviour varies by analytics setup and platform; confirm how your own reporting assigns credit before drawing conclusions from it.