The Economics Call: Forecasting a Reverse Trial's Payoff
Objective: Given StoneCo's current freemium conversion rate and monthly signup volume for a merchant-analytics add-on, forecast a conservative incremental-revenue range from switching to a reverse trial, using the lesson's cited conversion benchmarks.
You're the growth analyst at StoneCo, the Brazilian merchant-payments and fintech platform for SMBs, forecasting whether switching a payments-analytics add-on from standard freemium to a 30-day reverse trial is worth the engineering cost.
Apply the lesson's conversion-rate ranges (2-5% freemium vs. 15-30% reverse trial) conservatively, not optimistically, to a real signup volume and current conversion rate.
Before you start
What you'll need
Free path (everything below is enough to finish)
A simple multiplication table needs nothing more
Paid upgrades (optional, faster/deeper)
Confirms whether the conservative forecast held once real data replaces the estimate
The process
1 step
Step 01 of 01
The lesson's Measuring Success section sets a 15-30% trial-to-paid target for B2B SaaS reverse trials versus the 2-5% freemium baseline, and warns to track time-to-convert and 90-day retention, not just headline conversion.
StoneCo's add-on gets 4,000 free signups a month and currently converts at 3.2% under freemium. Using the low end of the reverse-trial range as the conservative case, how many incremental paid customers per month does the switch forecast, and what's the one metric that could make this forecast wrong?
Procedure
- Row 1, current: 4,000 signups x 3.2% = 128 paid customers/month
- Row 2, conservative reverse trial: 4,000 x 15% = 600 paid customers/month, a 472-customer lift
- Row 3, optimistic reverse trial: 4,000 x 30% = 1,200 paid customers/month, a 1,072-customer lift
- Flag that the forecast assumes 90-day retention holds steady; the lesson warns reverse-trial converts can churn faster if they converted from loss aversion rather than genuine fit
- Recommend using Row 2 (conservative) for any resourcing decision, and instrumenting 90-day retention from week one of rollout
StoneCo add-on forecast (n=4,000 signups/mo) Current (freemium, 3.2%): 128 paid/mo Conservative (reverse trial, 15%): 600 paid/mo (+472) Optimistic (reverse trial, 30%): 1,200 paid/mo (+1,072) Risk flag: forecast is invalid if 90-day retention for reverse-trial converts drops below the freemium baseline
Healthy
The forecast used for a resourcing decision is the conservative 15% case, not the optimistic 30% case.
Unhealthy
A team greenlights engineering spend based on the 30% optimistic scenario without a retention caveat attached.
What this means
A 472-customer conservative lift already justifies most reverse-trial engineering costs; the real open question is whether those converts stay, not whether they convert.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| Forecast built on the 30% optimistic case alone | Rebuild the business case using the 15% conservative case as the floor | 30 min |
| No retention instrumentation planned for the rollout | Add a 90-day retention cohort comparison to the launch checklist before shipping | dev ticket |
Final deliverable
A 3-row conversion-scenario forecast table (current, conservative reverse trial, optimistic reverse trial) with a stated retention risk flag.
See a reference example
Trade Desk, self-serve tool forecast (excerpt) Current (freemium, 2.8%): 84 paid/mo (n=3,000 signups) Conservative (reverse trial, 15%): 450 paid/mo (+366) Risk flag: enterprise buyers in this segment have a 6-month sales cycle, reverse trials may not apply to that portion of signups
Success criteria
You're done when you can:
- Correctly computes all three rows of the scenario table from the given inputs
- Recommends the conservative (15%) case for resourcing decisions, not the optimistic case
- States the retention risk that could invalidate the forecast