ROI-positive
by construction.
Not by scale.
The rule: each enrolled user clears ROI on transaction fees alone, within 12 months, with the incentive fully loaded as a cost. Built on the most conservative input set I could defend to finance. The output was 18.7% ROI per enrolled user, before counting LTV, MTU retention lift, or anything downstream.
The rule that gated the program
Each enrolled user had to be profitable on transaction fees alone, within 12 months, with the full incentive loaded as a cost. Not LTV. Not MTU retention lift. Not strategic upside. Just one user, one year, fees in minus reward out.
That rule did two things. It forced the model to be defensible to finance on the weakest input set we could justify. And it made scale a non-question: ROI is a per-user margin, so if the unit economics work at one user, they work at thirty thousand.
The conservative input set
Every assumption was set at its defensible floor. Where a reasonable analyst could argue lower, we went lower. Where reasonable analysts could disagree on direction, we went neutral. The point was a number finance couldn’t haggle down.
| Input | Value | Why this value |
|---|---|---|
| MTU base (post-initiative) | 330,000 | Actual achieved base. Pre-initiative was ~300K. |
| Average monthly buy | $100 | Conservative floor. Real recurring-buy users transact in this range and above. |
| Fee rate per side | 2.5% | Standard published rate. |
| Sell-side multiplier | 1.0× | Both sides of a trade are fee-bearing. Symmetric is conservative-to-neutral for accumulate products. |
| Churn per period | 30% | Drawn from the weakest historical cohort, not the average. |
| Incentive cap | $150 / user | Hard guardrail. Actual expected spend is far below. |
Per-user unit economics, churn-weighted
The model takes one enrolled user and projects 12 months. Each period applies the churn rate, so revenue and incentive both decline as the cohort thins. Three periods total (0–3, 4–6, 7–12 months).
The model is intentionally agnostic to revenue per user, retention beyond month 12, and any downstream behavior. All three of those are real and material, and all three sit on top of the 18.7%.
Both sides of every trade, churn-weighted over 12 months.
Churn-weighted milestone payouts. Fully loaded as cost.
Net of fully-loaded incentive. Positive at the floor.
On incentive spend. Constant across scale.
The payout bands
Three milestones. The 3- and 6-month milestones are flat. The 12-month milestone is tiered by engagement: the heaviest users get the largest payout, with the long tail getting a small one. Payouts only go to active users at each milestone, so churned users don’t get paid the milestones they missed.
| Milestone | Per active user | Active fraction | Expected / enrolled |
|---|---|---|---|
| Month 3 | $15.00 (flat) | 70% | $10.50 |
| Month 6 | $20.00 (flat) | 49% | $9.80 |
| Month 12 | $9.95 (tiered, see below) | 34.3% | $3.41 |
| TOTAL | — | — | $23.71 |
12-month tier structure
The 12-month payout is split across four engagement tiers. The blended expected value of $9.95 per active user comes out of this distribution:
| Engagement tier | % of active 12-mo users | Payout | Contribution to blended |
|---|---|---|---|
| Top | 1% | $100 | $1.00 |
| High | 10% | $25 | $2.50 |
| Mid | 40% | $10 | $4.00 |
| Long tail | 49% | $5 | $2.45 |
| Blended | 100% | — | $9.95 |
Three modeled cases. Plus what shipped.
The model carried three conversion-lift scenarios pre-launch, then we tracked what actually shipped against them. The per-user economics, and therefore the ROI, are identical across all four columns. That’s the point.
| Scenario | Conv. lift | Enrolled | Revenue | Incentive | Profit | ROI |
|---|---|---|---|---|---|---|
| Base Case | 2× | 6,000 | $169K | $142K | $27K | 18.7% |
| Upside Case | 4× | 12,000 | $338K | $285K | $53K | 18.7% |
| Result Case | 8× | 24,000 | $675K | $569K | $106K | 18.7% |
| Actual Shipped | — | 33,000 | $929K | $782K | $146K | 18.7% |
What the experiment confirmed
The unit economics were the case for shipping. The A/B/n experiments were the case for the structure: which part of the experience moved the needle and how much.
| Phase | Cost | Conversion lift | Note |
|---|---|---|---|
| Education | $0 CAC | 4× | Pure narrative effect of the simulator. No payout. Vs. control. |
| Reward | ~$24 / user | 8× | Incremental lift on users on the fence. Vs. control. |
The reward layer added 2× incremental lift on top of education (8× total vs. 4× from education alone). That confirmed the band design: half the conversion was narrative, half was money applied surgically at the right milestones.
Aggregate adoption moved from 0.2% to 10% of MTUs, a 50× lift in adoption rate. Recurring-buy users moved from 600 to 33,000, a 55× lift in absolute users.
What the 18.7% excludes (the upside)
The 18.7% ROI is the floor, by design. The model deliberately ignores everything that makes recurring buy genuinely valuable:
- LTV beyond month 12. Recurring-buy users transact for years, not one. The model truncates at month 12.
- MTU retention lift on the base. Recurring buy is a retention lever; the MTU base grew ~300K to ~330K over the window. The model attributes none of that to the program.
- Downstream behavior. Recurring-buy users refer more, qualify for the credit card cross-sell at higher rates, and adopt other products. None of that is in the model.
- The compounding behavior itself. Every adopter transacts automatically thereafter without further marketing. That spend efficiency isn’t reflected.
The framework as a reusable artifact
ROI-positive incentive design isn’t a guess. It’s modeled, and the model has to clear on the most conservative input set you can defend. The per-user unit-economics framework here, fees only, 12-month horizon, fully-loaded incentive, became the template every retention and activation incentive the growth org has shipped on since. Same math, different products.
The version of incentive design worth scaling is the one where you can prove the floor before anyone has to believe in the upside.