# Funnel Analysis

Conversion funnel analysis with drop-off investigation.

- Repository: https://github.com/nimrodfisher/data-analytics-skills (512 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-25
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add nimrodfisher/data-analytics-skills --skill funnel-analysis -g -a claude-code -y`
Codex: `npx skills add nimrodfisher/data-analytics-skills --skill funnel-analysis -g -a codex -y`
Cursor: `npx skills add nimrodfisher/data-analytics-skills --skill funnel-analysis -g -a cursor -y`
Gemini CLI: `npx skills add nimrodfisher/data-analytics-skills --skill funnel-analysis -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.
5. **Segment the funnel**, run the funnel separately by channel, device type, user cohort, or other dimensions. Rank segments by overall conversion rate and identify where the worst-performing segment diverges from the best. See `references/funnel_design_guide.md`.
6. **Prioritise and report**, rank drop-off points by absolute users lost × estimated revenue impact. Produce `assets/funnel_report_template.md` with the funnel table, segment comparison, and ranked recommendations.

The rest: https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/03-data-analysis-investigation/funnel-analysis/SKILL.md