We track whether your product meets the goals you set, with KPIs we choose together, and point out where the next change can have the most effect.
Duration: Ongoing, typically 2–4 days per month
What you ship, whether a new feature or a redesign, should show up in the numbers. Together we decide which KPIs matter for your product, set them up and track them. Beside them we set up the signs that users are stuck, such as tasks abandoned halfway or a spike in support tickets, so a problem shows before it reaches the quarterly numbers. Each report shows what changed and why, and how much of that came from the change and how much from, say, the season. Each cycle ends with a short list of opportunities for the next step, weighed on impact and effort.
- KPIs set up
- Signals when users get stuck
- Report per cycle
- Next opportunities
AI lines up the numbers and flags what changed; how much of that came from your change and how much from, say, the season, we judge.
Define
Together we pick the metrics that match your goals, such as conversion, the use of a feature or lead time.
- What we do
- Pick the metrics that match your goals.
- How AI helps us
- Lists what you already measure.
Monitor
We take a baseline and then track the numbers over time, so you can see what a change did.
- What we do
- Set the baseline and watch it over time.
- How AI helps us
- Lines up the numbers and flags what changed.
Report
Each period, what changed and what we think is behind it.
- What we do
- Judge how much came from your change and how much from, say, the season.
- How AI helps us
- Drafts the report each period.
Recommend
The next opportunities, ranked by expected impact against effort.
- What we do
- Rank the opportunities by impact against effort.
- How AI helps us
- Lists the opportunities it sees in the data.
- Whatever you already have, such as GA4, Plausible, Mixpanel or Amplitude. If you are not measuring anything yet, we choose a tool that suits your product together and set it up.
- Monthly suits most products. Faster than that and you are reading noise.
- Rarely with full certainty. We work with a baseline and comparison periods, and say plainly where we are not sure.
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