Topic 04 · Learn from outcomes
Work out what changed, why it matters, and what to do next.
Choose useful metrics, understand user behaviour, run sound experiments, and find practical ways to grow a product.
17 articles
Common questions
01
Which metric represents the outcome we care about?
02
What can the available data actually tell us?
03
Which growth mechanism can compound rather than merely spike?
Recommended reading
It covers the basics. The articles below explore related questions in more detail.
A good place to start
01
Filed under
Analytics · Metrics
A practical guide to combining product analytics, user context, and judgement so evidence improves product decisions instead of replacing them.
8 min read
02
Metrics
Diagnose churn as a broken path to value: define the event, compare mature cohorts, combine behaviour with context, and match the intervention to the cause.
Continue03
Growth
Measure product engagement with meaningful actions, eligible users, return intervals, and cohorts. Includes metrics and a worked example.
Continue04
Growth
Map a growth loop from user value to the next qualified opportunity. Test its weakest edge, delay, saturation, and guardrails before adding infrastructure.
Continue05
Metrics
Define cohort unit, anchor, eligibility, maturity, return event, interval, censoring, and composition before comparing retention over time.
ContinueMore on Measurement & Growth
12 more articles
01
Analytics
Change an analytics event without breaking meaning, dashboards, cohorts, privacy controls, ownership, or historical comparison.
02
Analytics
Learn what guardrail metrics measure, with onboarding examples and a decision table for thresholds, monitoring horizons, owners, and stop conditions.
03
Analytics
Diagnose conflicts between research and behavioural analytics by checking population, exposure, units, time and constructs before choosing a follow-up.
04
Analytics
Design a product KPI dashboard around a named audience, decision cadence, trust state, counter-signals, investigation routes, and recorded action.
05
Analytics
Define the effect, population, randomisation unit, exposure, outcomes, power, and analysis before building a controlled product experiment.
06
Analytics
Design a controlled path from product behaviour to a reproducible answer, with explicit identity, time, consent, lineage, quality states, and change control.
07
Metrics
Build product metrics as versioned measurement contracts with clear meaning, provenance, quality limits, ownership, migrations, and incident response.
08
Analytics
Define retention around repeat value, eligible opportunity, and product cadence—then separate behaviour, subscription, revenue, and reactivation.
09
Analytics
Build a durable experiment record across versions, eligibility, assignment, exposure, telemetry health, analysis lineage, decisions, follow-up and privacy.
10
Analytics
Create conversion data product teams can trust by defining outcomes, event semantics, identity, validation, privacy, lineage, monitoring, and change control.
11
Analytics
Build a defensible product funnel by defining its population, paths, clock, evidence limits, downstream outcomes, and decision before optimising a step.
12
Growth
Design a product community around member value, reliable participation, fair governance, contributor care, and a clear company boundary.