Funnel analysis
Skill Amey-Thakur/AI-SKILLS/skills/data-science/funnel-analysis
Plug-and-play skills and prompts for every AI coding agent
npx -y skills add Amey-Thakur/AI-SKILLS --skill funnel-analysisAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 19 days oldThe repository was created 19 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Analyze conversion funnels to find where users drop off and why, and size the opportunity of fixing each step. Use when diagnosing where a multi-step flow loses people, or prioritizing what to fix.
SKILL.md
3.1 KB, as published. Nobody here has run it
Funnel analysis
A funnel is a sequence of steps toward a goal (visit, signup, activate, purchase), and users leak out at every step. Funnel analysis finds where the leaks are, how big they are, and which one is worth fixing, turning a vague "conversion is low" into a specific, sized problem.
Method
- Define the funnel steps precisely. Name each step as an observable event with a clear definition and order, and decide the window (does a user have to complete the funnel in one session, a day, ever?). Fuzzy step definitions produce fuzzy, unactionable numbers (see product-metrics' define-once rule).
- Measure conversion at each step, not just end to end. The overall rate hides where the loss happens; the per-step drop-off is the map. A step where 60% leak is the finding, and it is invisible in the aggregate. Show both the step-to-step rate and the cumulative.
- Size the opportunity of each leak. The step with the worst drop-off is not always the best to fix; weight by volume and value. Fixing a 50% leak that only 100 users hit matters less than a 10% leak that 100,000 hit. Prioritize by users-lost times value-per-user, not by the scariest percentage (see prioritize-tasks).
- Segment the funnel to find who leaks. The same step may convert fine for one segment and terribly for another (mobile vs desktop, new vs returning, by source, by geography). Segmenting reveals whether a leak is universal (fix the step) or specific (fix it for that segment), and a bad-fit traffic source shows up as step-one drop-off (see user-activation, correlation-causation on selection).
- Diagnose the why, do not just measure the where. A drop-off number says where, not why. Combine it with qualitative evidence (session recordings, user testing, support tickets) to understand the cause before designing a fix (see usability-testing, empty-and-error-states). Guessing at the cause of a leak wastes the fix.
- Mind the funnel's honesty. Watch for survivorship and selection (the users who reach step 4 are not like those who dropped at step 1, so later-step rates are conditional), and time effects (a "funnel" measured in one session undercounts users who complete it later). State the window and the conditioning.
Boundaries
- Funnel analysis shows where users leave, not why; pair the where (data) with the why (qualitative and experiments) before investing in a fix (see usability-testing, experiment-analysis).
- Not every flow is a linear funnel; users loop, skip, and return, and forcing a messy journey into rigid steps can mislead. Model the real paths where they matter (see user-flows).
- Optimizing a funnel step can shift the problem downstream (more signups of worse-fit users who churn); check the downstream metrics, not just the step you fixed (see churn-analysis, product-metrics' guardrails).