analytics-funnel · git:20260913.b82f6cc · 2026-09-13 · sha256 8659d60abfaa84bf
analytics-funnel git:20260913.b82f6ccA
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--- name: analytics-funnel description: Use when product or gameplay telemetry must be grouped into a versioned funnel with explicit steps, cohort, denominator and observation window so drop-off claims are auditable rather than inferred from screenshots or raw event counts. --- # Analytics funnel Ask the analysis intent: first-session, progression, encounter, commerce, retention, or existing. ## Contract Publish: - stable `funnel_id` and revision - ordered step IDs and the `telemetry-events` schema/version each step consumes - cohort / eligibility predicate - denominator definition - observation window and timezone/session boundary policy - deduplication / late-event policy - segment dimensions allowed for comparison - excluded or unknown population ## Ownership `analytics-funnel` owns funnel definition and aggregation semantics. `telemetry-events` owns event schema/emission, gameplay systems own state truth, and `gameplay-validation` owns qualitative/playability claims. Funnel correlation is not causal proof. ## Rules 1. Do not require events to occur on one universal logic tick; consume the authoritative event timestamp/session identity defined by `telemetry-events`. 2. Event names, step order and denominator are versioned. A schema or eligibility change starts a new comparable revision or is explicitly migrated. 3. Duplicate/retried events are deduplicated by stable event/session identity according to policy. 4. A drop-off rate always names numerator, denominator, cohort, build/ruleset and time window. 5. Privacy/data-minimization rules are project/platform constraints; do not add person-identifying inputs merely to make a funnel easier to join. 6. Heat maps and funnels suggest where to investigate; they do not prove why a player failed, quit or converted. ## Acceptance Given the same event set, funnel revision, cohort and window, aggregation produces the same step counts and denominator independent of ingestion order. A reviewer can reproduce the reported drop-off and identify excluded/late/duplicate events without treating the result as causal gameplay proof.