Individual skill

funnel-analysis

Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, comparing segments through a funnel, or optimizing user journeys.

03-data-analysis-investigation/funnel-analysis/SKILL.md

Read pinned source

Source commit 9449d363e1ae43cf1706c73bebc16774e339c427

What was checked

Matches the observed repository HEAD: Yes
Scanner rules are current: No
Repository scan complete: No
Skill scan complete: Yes
Observed behavior: Not checked yet
Last checked: Nov 6, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings): 519
Repository last updated: Nov 7, 2026
License: MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

claude-code Repository topic
codex Repository topic
data-analytics Repository topic
skills Repository topic
engineering Derived category

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files

SKILL.mdcore · 2,734 bytes
assets/funnel_report_template.mddoc · 1,684 bytes
references/funnel_design_guide.mddoc · 2,975 bytes
scripts/funnel_analyzer.pyruns · 5,231 bytes

Read excerpt


# Funnel Analysis

# When to use
- Conversion is low and the team needs to know where users are dropping off
- A product change may have affected a specific funnel step
- Comparing conversion rates across channels, devices, or user cohorts
- Designing an A/B test and needing a baseline to set a meaningful MDE
- Building a regular funnel monitoring report

# Process
1. **Define funnel steps and time window** — list the ordered sequence of events or pages that constitute the funnel. Agree on how long a user has to complete the funnel (session, 24 hours, 7 days). Ambiguous definitions here will invalidate the analysis.
2. **Build the user-level funnel dataset** — for each user who reached step 1, record which subsequent steps they completed and when, within the time window. Use `scripts/funnel_analyzer.py` to compute this from an events log.
3. **Calculate conversion rates** — compute step-to-step conversion (users reaching step N ÷ users reaching step N−1) and overall conversion (step 1 to last step). Record absolute drop-off counts at each step.
4. **Analyse time-to-convert** — for users who completed each step, calculate median, P75, and P95 time between steps. Long gaps can signal friction even without high drop-off.