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funnel-analysis

funnel-analysis

github.com/nimrodfisherData & databases509266 installsUpdated 5w ago
Repository

About funnel-analysis

funnel-analysis is a skill for measuring ordered user journeys and finding where conversions decline. It supports product and analytics teams with step-level conversion, time-to-convert, segment comparisons, and prioritized reporting.

Key features

  1. 01Defines funnel steps and time windows
  2. 02Calculates conversion and drop-offs
  3. 03Measures time between steps
  4. 04Compares funnel segments
  5. 05Ranks findings for reporting

Use cases

  1. 01Finding where users abandon a journey
  2. 02Assessing a product change's effect on a funnel
  3. 03Comparing conversion across channels or cohorts
  4. 04Setting a baseline for an A/B test

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill funnel-analysis

After installing, type/funnel-analysisin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
funnel_report_template.md
references/
funnel_design_guide.md
scripts/
funnel_analyzer.py

Details

From the project README

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.

Inputs the skill needs

  • Event log data with at minimum: user_id, event_name, timestamp
  • Ordered list of funnel steps (event names in sequence)
  • Time window for funnel completion
  • Segmentation columns if a comparative analysis is needed (channel, device, plan)
  • Estimated revenue value of a conversion (for impact sizing)

Output

  • scripts/funnel_analyzer.py — builds user-level funnel from an event log, computes step conversions, drop-offs, and time-to-convert
  • references/funnel_design_guide.md — how to define funnels, choose time windows, and avoid common measurement mistakes
  • assets/funnel_report_template.md — report template: funnel overview table, drop-off analysis, segment comparison, time-to-convert, recommendations

Comments

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