Retention cohort review

Retention Cohort Review

Explain who returned for which value and what evidence-backed product action should change. Do not optimize a chart whose denominator or value event is wrong.

Workflow

1. Define the decision, analysis unit, cohort-entry event, eligibility rule, retained value event, product-loop cadence, observation window, time zone, identity model, and comparison population.
2. Read references/retention-cohort-systems.md.
3. Verify current telemetry authority at use: event dictionary/revision, query or dataset version, bot/test filtering, consent and deletion treatment, identity stitching, late events, release calendar, and instrumentation changes.
4. Select and label the metric family: exact-period/classic retention, rolling retention, bracket/bounded return, survival, hazard, renewal, repeat purchase, or frequency. Do not compare different definitions as one series.
5. Audit denominator eligibility, cohort maturity, right censoring, interval boundaries, duplicate identities, reinstall/account merges, and sample uncertainty before diagnosing movement.
6. Decompose the change into acquisition/segment mix and within-segment movement. Inspect activation, feature exposure, performance, content, collaboration, social, lifecycle messaging, pricing, support, seasonality, and version effects.
7. Separate observation from causality. Tie candidate mechanisms to product or operational changes, then rank hypotheses by evidence, reach, reversibility, expected movement, and harm guardrails.
8. Define experiments or observational follow-ups with eligible population, expected cohort movement, measurement window, power/precision requirement, interference risk, and quality/economic guardrails.
9. Produce the metric contract, data-quality verdict, cohort decomposition, diagnosis, hypotheses, experiment plan, and instrumentation handoff.

When not to use

• For product event taxonomy, identity, consent, and metric semantics, use product-analytics-instrumentation-review after specifying the retention measurement requirement. For recurring dataset/pipeline/warehouse freshness, completeness, reconciliation, trust state, or repair, use data-quality-observability-review; this Skill retains the local input-quality verdict needed to interpret the cohort.
• For notification channel, permission, cadence, quiet hours, and message policy, use notification-strategy-review; consume the cohort hypothesis here.
• For pricing/package architecture, use saas-subscription-pricing.
• For broad game/app product architecture, use the relevant design blueprint; this skill owns the retention analysis slice only.
• For a proposed, staged, or live subscription price increase, including its renewal-maturity cohort diagnosis and recovery decision, use subscription-price-increase-retention-review.
• For board reporting, provide a released retention artifact that another owner may consume; do not expand into a full board pack.

Source verification

• Resolve the exact event dictionary and revision, query or dataset version, identity rules, eligibility, bot/test filters, consent/deletion treatment, time zone, late-event handling, release exposure, and observation cutoff for every cohort compared.
• Prefer canonical event contracts, reproducible queries, locked extracts, and observed product/release records. A dashboard screenshot or remembered metric definition is a lead, not current authority.
• Mark immature, censored, underpowered, non-comparable, stale, or mixed-source cohorts explicitly. Do not fabricate causality, statistical confidence, or a current value to complete the analysis.

Guardrails

• Never use app open, login, or an active subscription as the retained action by default when they do not prove recurring value.
• Never count not-yet-observable users as churned or silently drop unfavorable users from the denominator.
• Never infer product causality from a blended pre/post curve alone.
• Never compare cohorts across changed event definitions, identity rules, time zones, or eligibility without a visible comparability break or restatement.
• Never improve short-term return through spam, dark patterns, unwanted notifications, exploitative rewards, or cancellation friction.
• Pair retention with user value, satisfaction/trust, support, refunds, safety, accessibility, performance, margin, and notification opt-out as applicable.

Output

Decision and metric contract:
- unit / cohort entry / eligibility / retained value / cadence / metric type / window

Authority and data-quality ledger:
| Input | Version/source | Status | Comparability issue | Decision effect | Owner |
| --- | --- | --- | --- | --- | --- |

Cohort readout:
| Cohort/segment | Eligible N | Observable N | Retention/hazard | Uncertainty | Delta | Caveat |
| --- | ---: | ---: | ---: | --- | ---: | --- |

Mix and mechanism decomposition:
- acquisition/segment mix / within-segment movement / lifecycle mechanism / evidence

Actions and experiments:
| Hypothesis | Mechanism | Target cohort | Change | Expected movement | Guardrails | Evidence plan |
| --- | --- | --- | --- | --- | --- | --- |

Instrumentation and authority gaps:
- requirement / owning specialist / exact evidence / blocked conclusion