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The output is well-structured, uses evidence correctly, and covers all required elements; a PM could use it with only minor wording tweaks, without re-checking evidence or redoing reasoning.
Output
To: CEO, Growth Lead From: Head of Product Re: Simmer's North Star
Recommendation
Make the North Star Weekly Active Cookers (WAC): users who complete Cook Mode on two or more recipes in a week.
Both of your metrics matter, but neither measures the thing that drives the business.
Why not MAU. 68% of our 2.1M monthly actives arrive from search, read one recipe and leave. They retain at 4% at month 6. Last year's push test shows the risk: MAU rose 6%, uninstalls doubled, and Cook Mode completions didn't move. A MAU target rewards exactly that trade.
Why not MRR. MRR ($840K, from 140K subscribers at $6) is the outcome we want, but it lags. It can also be moved by a price change or a paywall tweak without making the product better, and it tells a team nothing about what to do this week.
Why WAC. People who cook twice or more a week retain at 61% at month 6, against 9% for savers who never cook and 4% for browsers. And 82% of new subscribers cooked at least three recipes in Cook Mode in their first two weeks. Cooking sits upstream of both retention and revenue, so growing WAC should grow MRR, and teams can influence it within a sprint.
MRR stays as our top-line business outcome, reported alongside WAC. MAU becomes a funnel-health metric, since it is the pool WAC is converted from.
One caveat: these are correlations, and committed cooks may simply be our best users anyway. In the first quarter we should run a holdout experiment, such as a stronger first-week cooking prompt, to confirm that causing more cooking causes better retention and conversion.
Input metrics teams can own
- New-user activation (Onboarding): % of new users who complete a Cook Mode recipe within 7 days, with the 3-recipes-in-14-days rate tracked as the subscription-intent signal.
- Save-to-cook rate (Discovery and Cook Mode): % of savers who cook a saved recipe in Cook Mode within 14 days. Savers who never cook retain at 9%, so this is our biggest leak.
- Browse-to-return conversion (Growth and SEO): % of search-landing visitors who save a recipe or come back within 7 days. This gives Growth a real lever on the 68%, and on MAU quality.
- Planning adoption (Planning): % of monthly actives who build a weekly plan. Only 7% have ever done it, yet weekly planners retain at 54%.
Guardrails against gaming
Against cheap cooking events. Completion is logged when someone reaches the last step, so tapping through counts. - Track a "quality completion" (minimum elapsed time relative to recipe length, or timer use) and report WAC on that basis. - Monitor median time per completion for sudden drops. - Add an occasional "Did you cook it?" prompt to calibrate against self-report.
Against the notification trap. - Set a hard cap on push frequency. - Treat uninstall rate and notification opt-outs as blocking metrics: any experiment that worsens either is not shipped, whatever it does to WAC or MAU.
Against short-term lifts. - Judge any WAC gain by its 30- and 90-day cohort retention, so we don't buy a spike with users who disappear.
Against revenue gaming. - Track subscriber churn, refund rate and cancellation-flow completion alongside MRR. - Don't count MRR gains from price changes or paywall aggressiveness as product wins. - Don't gate Cook Mode so tightly that new users can't reach the 3-recipe habit that predicts subscribing.
Against MAU drift. - Keep reporting MAU, but as a watch metric with no team target. If MAU rises while WAC is flat, we treat it as a warning sign rather than a win.
What each of you gets
- CEO: a metric that leads MRR and that teams can act on, with MRR still reported at the top. If WAC grows and conversion holds, MRR follows.
- Growth lead: ownership of the biggest untapped pool, the browse-only majority. Success is measured by how many of them become cookers, not by how many we can get to open the app.
I'd propose we adopt WAC from next quarter, baseline it in the first month, set targets once we see the quality-adjusted number, and review the causal experiment results at the end of the quarter.
Grades and run details
Decision model 95 · LLM judge 100
Decision model checks
- passUses the supplied evidence correctly57%
- passAddresses the actual decision93%
- passRespects explicit constraints46%
- passIdentifies material uncertainty75%
- partialAvoids unsupported claims24%
- passProduces the required deliverable94%
- passAnswers both proposals with the data100%
- passMeasures value created, not captured100%
- passGets the base of every number right78%
- passBreaks it into inputs teams can move100%
- passSays how it could be gamed100%
Run
- Run
- #1
- API response time
- 22 s
- Submitted
- 5 Oct 2026