activation-funnel
Activation Funnel Expert
Overview
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.
Core Capabilities
- Funnel structure — define stages as events using AARRR or AAARRR; compress to 4-7 stages.
- Conversion + drop-off math — per-stage conversion, absolute and relative drop, cumulative conversion, bottleneck detection (largest absolute vs largest relative drop).
- Activation event definition — Sean Ellis framework to pin the predictive "aha" event (count + window + action).
- Counter-metric pairing — guard every stage against gaming; leading-vs-lagging indicator design.
- Six-format output — render the analysis + Mermaid diagram for Jira, Linear, Confluence, Notion, or a PR.