Product metrics
North-star metrics, KPIs, funnels, A/B test design and reading results.
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- Analyse a conversion funnel
Analyses a conversion funnel step by step to find the biggest leak, the segments where it differs, likely causes and the experiments or fixes worth trying first. For PMs and growth teams.
- Build a growth experiment backlog
Builds a ranked growth experiment backlog from a funnel and ideas, with hypothesis, metric, effort, expected impact, minimum sample and run time per test, and flags untestable ideas.
- Check a KPI for perverse incentives
Reviews a proposed KPI or target for ways people could hit it while harming customers, quality or other teams, then adds counter-metrics, review rules and a safer wording.
- Choose metrics for a two-sided marketplace
Chooses metrics for a two-sided marketplace by stage, such as liquidity, match rate, time to first transaction and take rate, with definitions, balance checks and health measures for both sides.
- Define an activation metric
Finds a product's activation moment from usage and retention data, defines an activation metric with an action, threshold and time window, and plans how to validate it.
- Define feature success metrics
Defines success metrics for a feature using HEART and goals-signals-metrics, with baselines, targets, guardrails, decision rules and the event data needed. Use before building or launching.
- Define guardrail metrics
Defines a standing set of guardrail metrics for experiments and launches that catch harm to revenue, performance, trust or support load, with thresholds, owners and actions on breach.
- Define metrics for an internal tool
Defines a small metric set for an internal tool or process change - adoption, time on task, rework, time saved as capacity, staff ease - with baselines and ways to measure without analytics.
- Define a north star metric
Proposes a north star metric with input metrics and guardrails, tests it against the value users actually get, and shows the rejected candidates. Use when setting product goals.
- Define KPIs for a physical product
Defines KPIs for a physical product line - sell-through, return rate, field failure, rating trend, warranty cost and margin per unit, attach rate - with formulas, sources and alert thresholds.
- Define public service KPIs
Defines KPIs for a public or charity service - completion, take-up by channel, cost per transaction, satisfaction, time to outcome and failure demand - split by user group to show who is left out.
- Design an A/B test
Designs an A/B test plan with a hypothesis, primary and guardrail metrics, minimum detectable effect, sample size, duration, randomisation unit, stop rules and an analysis plan.
- Design a holdout experiment
Designs a holdout or long-term experiment that measures the cumulative impact of a feature, programme or channel, with group size, duration, contamination risks and a decision rule.
- Diagnose a metric drop
Investigates a drop in a product metric with a structured tree (data and tracking, segments, platforms, releases, external factors), ranks the hypotheses and gives the queries to run.
- Estimate a feature's impact
Sizes a feature's expected impact before building it, with explicit reach, adoption, effect and value assumptions, a low-base-high range and the cheapest way to tighten the estimate.
- Explain a change in NPS
Explains whether a change in NPS or CSAT between two periods is real, checking margin of error, response rates, segment mix and survey changes before pointing to the reasons behind it.
- Explain SaaS metrics on your numbers
Explains SaaS metrics such as MRR, ARR, NRR, GRR, churn, expansion and quick ratio by calculating them step by step on the user's numbers, with checks and common mistakes.
- Monthly open-source growth review
Runs a monthly growth review for an open-source project, from collecting public numbers to finding the leakiest funnel stage, judging last month's bets and choosing next month's, with approval gates.
- Quiz me on metric pitfalls
Runs a quiz game of short product scenarios that each hide a metric trap, such as Simpson's paradox, survivorship or a shifting denominator, and explains each one after the answer.
- Review launch results
Reviews a launched feature against its success criteria, separates real signal from noise and novelty, and recommends whether to iterate, scale or roll back, with the reasoning.
- Review an open-source project's weekly growth numbers
Turns a week of an open-source project's public numbers (traffic, referrers, downloads, stars, issues, contributors) into what changed, the likely cause and one action for next week. Use every week.
- Set metric targets from a baseline
Sets a commit and a stretch target for a product metric from its baseline, normal variation, seasonality and the realistic effect of planned work, so targets sit outside noise and inside reach.
- Set up growth metrics for an open-source project without telemetry
Defines the handful of public, telemetry-free metrics that show an open-source project's adoption and community health, with collection commands, a weekly archive and leading versus vanity signals.
- Write an experiment readout
Turns a finished experiment's results into a one-page decision record for stakeholders, with a forwardable summary, the result against the prediction, trust checks, the decision and limits.
- Write an analytics tracking plan
Writes an analytics tracking plan with consistently named events and properties, when each fires, the question it answers, privacy notes and QA steps. Use when instrumenting a feature.
Not: general spreadsheet or statistics work (data-analysis domain).