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Design and analyze activation funnels (AARRR / AAARRR Pirate Metrics) with conversion + drop-off math, bottleneck detection, and Mermaid funnel diagrams. Includes a stdlib Python tool that emits all 6 shared PM output formats.
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| name | activation-funnel |
| description | Design and analyze activation funnels (AARRR / AAARRR Pirate Metrics) with conversion + drop-off math, bottleneck detection, and Mermaid funnel diagrams. Includes a stdlib Python tool that emits all 6 shared PM output formats. |
| license | MIT + Commons Clause |
| metadata | {"version":"1.0.1","author":"borghei","category":"project-management","domain":"pm-execution","updated":"2026-06-15T00:00:00.000Z","python-tools":"funnel_analyzer.py","tech-stack":"aarrr, pirate-metrics, activation, conversion-funnel, growth, north-star"} |
A funnel is the single most useful diagnostic tool a growth or onboarding PM owns. It turns a fuzzy product story ("users drop off somewhere") into a numbered, actionable picture ("76% land, 41% start setup, 9% finish setup, 4% take the activation action -- the biggest drop is between start and finish setup at 32 percentage points").
This skill specifies funnel structures using Dave McClure's AARRR (Acquisition, Activation, Retention, Revenue, Referral) and its broader cousin AAARRR (which adds Awareness on the front), and analyzes them with a stdlib Python tool (funnel_analyzer.py). The tool ingests a JSON funnel definition (stages with counts) and outputs stage-by-stage conversion and drop-off, a Mermaid flowchart, and a bottleneck call-out — in all six SHARED_OUTPUT_SCHEMA formats so the analysis travels into Jira, Linear, Confluence, Notion, or a PR.
The activation step is the centerpiece. Sean Ellis defined the "activated user" as one who has done the thing that statistically predicts retention (Slack's 2000 messages, Facebook's "7 friends in 10 days", Dropbox's "1 file in 1 folder on 1 device"). Pin the activation event before you optimize the funnel that leads to it.
When NOT to use: pre-PMF discovery (use discovery/); top-of-funnel channel attribution (marketing tools); revenue-cohort retention (data-analytics domain); when events are not instrumented.
python scripts/funnel_analyzer.py --input funnel.json --format markdown # conversion + drop-off + bottleneck
python scripts/funnel_analyzer.py --demo --format mermaid # worked SaaS funnel diagram
Load the reference that matches the task — keep this file lean and pull detail on demand:
funnel_analyzer.py reference (flags, input JSON, Mermaid sample), troubleshooting, and success criteria. Read when building or analyzing a funnel.In Scope: funnel definition (events, stages, transitions) via AARRR/AAARRR; conversion + drop-off math and bottleneck detection; Mermaid rendering; activation-event selection (Ellis); counter-metric pairing; cohort-vs-snapshot distinction; all 6 output formats.
Out of Scope: pulling raw event data (input is JSON — use Amplitude/Mixpanel/PostHog/Looker); statistical significance testing (discovery/brainstorm-experiments/, data-analytics); cohort-retention analysis; channel attribution (marketing tools); building the UX/A/B test; forecasting.
Caveats: a funnel implies a linear flow — real products branch and re-enter, so complement with branched analysis. Snapshot funnels mix cohorts and lie; always specify the cohort window. Top-stage gains amplify downstream and can mislead investment — look at both absolute and relative drop. Re-validate the activation event against fresh retention cohorts every 1-2 quarters.
| Integration | Direction | Description |
|---|---|---|
north-star-metric/ | Pairs with | Activation rate is often the NSM or a top input metric in the NSM tree |
brainstorm-okrs/ | Feeds into | Funnel-stage targets become KRs (e.g., "improve activation from 28% to 36%") |
prioritization-frameworks/ | Feeds into | Fix-the-funnel projects ranked by RICE / weighted-score |
discovery/brainstorm-experiments/ | Pairs with | Each funnel leak suggests testable experiments to plug it |
discovery/identify-assumptions/ | Pairs with | "Users will complete step 3 if we shorten it" is an assumption to validate |
status-update-generator/ | Feeds into | Weekly funnel deltas appear in Highlights / Risks |
outcome-roadmap/ | Pairs with | Roadmap items justify themselves by which funnel stage they target |
cycle-time-analyzer/ | Pairs with | Cycle time to fix funnel leaks is part of flow analysis |
data-analytics/ (domain) | Pairs with | Telemetry instrumentation; cohort analysis; stat sig |