Measure real influencer ROI, not just reach
Likes and EMV don't pay bills. AI can turn your raw spend-and-revenue table into a clean ROI ranking and flag which creators to rebook; the numbers themselves come from your own tracking (unique codes, UTM links, holdout tests) and AI must never invent them.
Fastest path: one prompt, end to end
🤖 AI prompt — paste into ChatGPT / Claude
You are my influencer analytics assistant. I will paste a table of my creators with their spend and attributed revenue. Rank them by ROI and tell me who to rebook.
Here is my data:
[paste table — columns: Creator | Spend $ | Attributed revenue $ (from code/UTM) | Attribution window (days) | New customers | Repeat-purchase rate % | EMV $ (if known)]
Do this:
1. Compute ROI = attributed revenue ÷ spend, and rank creators high to low.
2. Compute each creator's CAC = spend ÷ new customers (only where I gave you a customer count), and compare it to my paid-ads CAC of [paste number, or leave blank]. A creator beating your paid-ads CAC is a rebook even at a modest ROI.
3. Where a big EMV sits next to thin attributed revenue, flag it as "test incrementality," NOT as proof of return.
4. Recommend rebook / renegotiate / drop per creator, based on ROI, CAC vs paid ads, and repeat-purchase rate — not follower count or EMV.
Output ONE markdown table: Creator | Spend | Attributed rev | ROI | CAC | Repeat % | EMV note | Recommendation.
Use ONLY the numbers I pasted — do not invent, estimate, or fill missing cells. If a cell is blank, leave it blank and say what I need to supply. Do not compute ROI or CAC for any creator missing the required figure.
Or do it in 5 steps
- Tie every post to a transaction. Each creator gets one of: a unique promo code, a UTM-tagged link, or an affiliate link. Pick ONE standard per campaign — mixed methods produce incomplete, unrankable data. Codes and links won't catch every sale, but for a small-to-mid program they're accurate enough to rank creators and decide who to rebook.
- Judge revenue AND customer acquisition cost. ROI (attributed revenue ÷ spend) tells you which posts sold; CAC (spend ÷ new customers) tells you whether the creator acquires customers cheaper than your paid ads. A creator at a modest ROI who beats your paid-ads CAC is still a rebook — you're buying customers, not just a return multiple.
- Account for content value, not just clicks. A creator also gives you usage-rightable assets (whitelist-able video, reviews, UGC you can reuse in ads). If you'd have paid a studio to produce that, it's a real offset against spend — count it, but keep it a separate line, never folded into attributed revenue.
- Treat EMV as context, never ROI. Earned Media Value estimates what the reach would've cost as paid media — useful for judging scale, but it is not revenue and should never be the headline number in a stakeholder report.
- Layer in incrementality once spend justifies it. At real scale, run a holdout: withhold the campaign from a comparable segment or region and compare conversion. It's the only method that isolates lift from demand that would've happened anyway (incrmntal.com confirms incrementality is the strongest way to measure influencer performance).
Done looks like: a per-creator ROI-and-CAC ranking on one consistent tracking method, content value counted separately, and EMV kept in its lane.
Worked example: say two creators each cost $1,000
| Creator | Spend | Attributed revenue (30-day) | ROI | New customers | CAC | Verdict |
|---|
| A | $1,000 | $4,200 | 4.2x | 70 | $14.30 | Rebook |
| B | $1,000 | $900 | 0.9x | 12 | $83.30 | Drop / renegotiate |
These dollar figures are a labeled illustration ("say each cost $1,000"). Your real ranking uses your own code/UTM revenue and your own customer counts — never a number you didn't measure. If your paid-ads CAC is, say, $40, Creator A ($14.30) is a clear rebook and Creator B ($83.30) is not.
Standardize the reporting window wording ("30-day attributed revenue") so stakeholders don't read a partial number as the whole result.