Product metrics
North-star metrics, KPIs, funnels, A/B test design and reading results.
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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Not: general spreadsheet or statistics work (data-analysis domain).