Playbook replay
Project influencer campaign ROI
Three scenarios with the formulas exposed, a break-even view count, and the one assumption the whole model turns on.
Run for Lumira, a demo brand we invented for this page. No customer data. Nothing here runs against a live account.
Every session here was run once and saved, against a demo brand or public data. On your own account Naise works from your brand voice, your campaigns and your connected channels, so it reasons about your data instead of a stand-in.
The conversion rate is the number nobody has and everybody guesses. It should be the input the model is most transparent about, not the one buried in a spreadsheet cell.
Building the model with every formula visible so you can change one input and re-run it without me.
The chain, with formulas.
| Step | Formula | Expected |
|---|---|---|
| Views | given | 84,000 |
| Link clicks | views × CTR | 1,176 |
| Conversions | clicks × conversion rate | see below |
| Revenue | conversions × $42 | |
| Gross profit | revenue × 0.62 | |
| Net | gross profit − $2,100 |
You did not give me a conversion rate, so it is the variable I have to assume. Here is the model across three:
| Scenario | Conv. rate | Conversions | Revenue | Gross profit | Net |
|---|---|---|---|---|---|
| Worst | 1.0% | 12 | $494 | $306 | −$1,794 |
| Expected | 2.5% | 29 | $1,235 | $766 | −$1,334 |
| Best | 5.0% | 59 | $2,470 | $1,531 | −$569 |
All three lose money on first purchase. That is not a mistake in the model and it is not unusual. It means this deal only works on repeat.
Break-even on first purchase alone needs 81 conversions, which at 1.4% CTR means a 6.9% conversion rate or 232,000 views. Neither is happening. So the question is not whether the post pays for itself, it is how many months a refill customer has to stay before it does.
At a $17 refill every 8 weeks and 62% margin, the expected-case 29 customers cover the fee at about month 5, assuming none of them churn. That is the number this decision actually rests on.
The assumption the model is most sensitive to is retention, not reach.
Doubling the views moves net by roughly $766. Moving 5-month retention from 60% to 80% moves the lifetime value of those same 29 customers by more than the entire fee. Reach is the input everyone negotiates and retention is the one that decides it.
Two flags on the inputs you gave me.
- The 1.4% CTR is described as estimated. On TikTok with a link in bio rather than in-video, 1.4% is optimistic. At 0.8% the expected case drops to 17 conversions and break-even moves past month 8.
- Average views hides the distribution. If their 84,000 average comes from two posts at 400,000 and eleven at 20,000, your expected case is 20,000, not 84,000. Ask for the median, or check their recent posts yourself.
Where this stops. This is a projection built from your numbers, not a measurement. Naise does not have their real view distribution or your real conversion rate, so treat the shape of the model as the output and the figures as placeholders until you fill in the two flags above.
Naise created an artifact
Formula-by-formula projection showing all three cases lose money on first purchase, and what that means.
The three cases
| Scenario | Conv. | Net on first purchase |
|---|---|---|
| Worst, 1.0% | 12 | −$1,794 |
| Expected, 2.5% | 29 | −$1,334 |
| Best, 5.0% | 59 | −$569 |
All three lose money up front. The deal works on repeat: 29 customers cover the $2,100 fee at about month 5, if none churn.
Most sensitive input: retention, not reach.
Flagged: 1.4% CTR is optimistic for link-in-bio. Average views may hide the distribution, ask for the median.
That was a sample. Run it on your own brand.
Naise reads your brand, your channels, and your market, then does this against your real data.
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