We use GRI findings to identify where customers get stuck. We test one change and compare it with the baseline.
Interest
The customer takes a meaningful next step.
First value
The customer achieves an outcome, not merely opens the product.
Use
The cohort returns to a useful activity.
Repeat purchase
There is a new payment, not just another visit.
More value
Customer contribution grows after costs and rewards.
Customers return when a new need arises. A return visit and a new purchase are different outcomes.
- 01What does the customer need?
- 02What blocks the next step?
- 03What will we change with AI?
- 04Which action will we measure?
- 05Is there profit after costs?
Customer signal → Hypothesis → Human × AI pilot → Cohort evidence → Economics → Decision
Before launch, we define a metric, timeframe and success rule. We compare with a control or similar group and account for seasonality, discounts, returns, team time and AI costs. Timing alone does not prove an AI effect.
We measure returns, new payments and profit after costs separately. We compare customers over the same period and label forecasts of future customer value as assumptions.
ScaleEvidence supports the effect, with acceptable economics and quality.
IterateThere is a signal, but evidence or economics are not yet sufficient.
StopThe decision rule is not met or guardrails are breached.
We communicate with customer consent and make opting out easy. Targets are benchmarks, not growth guarantees.
Need → value → repeat purchase → profit