Usable with a quick edit11 of 11 checks passed
The output is clear, evidence-based, and covers all required elements; a PM could act on it with minimal editing.
Output
Recommendation: optimize for weekly cooking, not revenue or visits
Our North Star should be Weekly Engaged Cooks: the number of unique users who complete at least two recipe-cooking sessions in Cook Mode within a rolling seven-day period. Count each person once, across free and paid users.
Simmer’s core promise is helping people make meals—not merely find recipes. This metric captures users repeatedly receiving that value.
The evidence points toward cooking: - Users cooking at least twice a week have 61% month-six retention, versus 9% for save-only users and 4% for browse-only users. - 82% of new subscribers cooked at least three recipes in their first two weeks. - Weekly planners also retain well, at 54%, but planning has reached only a small minority of users. It should support cooking, not define value for everyone.
These are associations, not proof that pushing users across a threshold causes retention or subscription. They are nevertheless our strongest starting evidence; we should test whether product changes that increase genuine cooking also improve those outcomes.
Why not MRR or MAU?
To our CEO: MRR remains a critical business outcome. But as the North Star, it could reward price increases or aggressive paywalls even while fewer people get value. Weekly cooking gives teams a customer-value objective with a plausible connection to durable revenue.
To our growth lead: MAU measures reach, but treats a one-recipe search visitor like a habitual cook. Our notification experiment demonstrates the danger: MAU rose 6%, uninstalls doubled, and cooking completions stayed flat. That was more activity, not more value.
Four input metrics teams can own
| Owner | Input metric | Purpose |
|---|---|---|
| Acquisition and discovery | First-cook activation: percentage of new users completing a Cook Mode session within seven days | Turn search traffic and browsing into useful cooking experiences; segment by acquisition source. |
| Onboarding and recipe experience | Second-cook conversion: percentage of first-time cooks completing another session within the next seven days | Build repeat value rather than celebrate a single trial. |
| Retention and meal planning | Weekly engaged-cook retention: percentage of Engaged Cooks who qualify again the following week | Grow a durable habit; test planning and saved-recipe resurfacing as mechanisms. |
| Cook Mode | Successful-session rate: percentage of genuine Cook Mode starts that reach completion | Remove usability and reliability barriers during cooking. |
These are drivers, not independent targets to maximize at any cost. Track their absolute user counts alongside rates so teams cannot improve percentages simply by shrinking eligibility.
Guardrails against gaming
1. Measure cooking, not button presses. Reaching the last step is a proxy, not proof of a cooked meal. Exclude automated activity and duplicate completion events. Audit suspicious completion patterns and periodically validate the proxy through user research. Don’t reward tapping through steps, auto-completion, or cosmetic changes to logging.
2. Protect the whole experience. Monitor uninstalls, notification opt-outs, complaints, and cooking satisfaction. Reject “growth” that increases qualifying activity through nagging while materially worsening these measures. Preserve browse-only usefulness; not every visitor needs to become a tracked cook.
3. Require durable gains. Evaluate experiments on sustained weekly cooking and cohort retention, not just a short-lived threshold crossing. Keep metric definitions stable and report results separately for new, existing, free, and paid users.
4. Keep economics visible. Review MRR, paid conversion, subscriber churn, and contribution margin alongside the North Star. More cooking is not enough if it undermines a sustainable business.
Bottom line: MAU tells us who arrived; MRR tells us what we earned. Weekly Engaged Cooks tells us whether Simmer is becoming useful enough to return to—and pay for.
Grades and run details
Decision model 95 · LLM judge 100
Decision model checks
- passUses the supplied evidence correctly77%
- passAddresses the actual decision64%
- passRespects explicit constraints59%
- partialIdentifies material uncertainty58%
- passAvoids unsupported claims60%
- passProduces the required deliverable96%
- passAnswers both proposals with the data64%
- passMeasures value created, not captured100%
- passGets the base of every number right86%
- passBreaks it into inputs teams can move100%
- passSays how it could be gamed100%
Run
- Run
- #1
- API response time
- 28 s
- Submitted
- 5 Oct 2026