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funnel-analysis

Analyze conversion funnels to identify drop-offs and generate prioritized experiments.

5|2|Updated Mar 27, 2026
Search Tags:#metrics#conversion#experiments#hypotheses#leakage#funnel-analysis
One-click install
npx skills add https://github.com/tarunccet/pm-skills --skill funnel-analysis-tarunccet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: funnel-analysis
Source: https://github.com/tarunccet/pm-skills/tree/main/pm-data-analytics/skills/funnel-analysis
Command: npx skills add https://github.com/tarunccet/pm-skills --skill funnel-analysis-tarunccet

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze a conversion funnel to identify drop-off points, calculate stage-by-stage conversion rates, generate leakage hypotheses, and recommend concrete experiments to improve flow.

Core Features & Use Cases

  • Stage-by-stage funnel metrics: compute entrants, exits, conversion rates, drop-offs, and volumes.
  • Biggest leaks: rank stages by drop-off volume to focus fixes.
  • Hypothesis generation: create multiple reasons for drop-offs with evidence prompts.
  • Experiment planning: prioritize fixes using ICE scoring and generate a practical action plan.
  • Use cases: diagnose funnel performance, prioritize optimization, design experiments for onboarding, activation, or checkout flows.

Quick Start

Provide the funnel data (stage names and user counts) and optional context, and I will return a complete stage-by-stage analysis with leakage hypotheses and recommended experiments.

Frequently Asked Questions about funnel-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze a conversion funnel to identify drop-off points?▼

Funnel analysis identifies drop-offs by computing per-stage conversion rates and exit volumes from your user count data, highlighting the biggest leaks and generating hypotheses to explain the drop-off behavior.

How do I generate hypotheses for funnel leakage and plan experiments?▼

Generate leakage hypotheses by examining per-stage drop-offs, then plan experiments by prioritizing fixes using ICE scoring to produce a practical action plan for your conversion flow.

Can I diagnose onboarding and checkout funnel performance using user counts?▼

Yes, you can diagnose onboarding, activation, or checkout flows by providing stage names and user counts to quantify drop-offs and calculate conversion rates across user segments and time periods.

What is the best way to prioritize funnel optimization fixes?▼

The best way to prioritize funnel fixes is ranking stages by drop-off volume to find biggest leaks, then applying ICE scoring to recommended experiments to produce a prioritized action plan.

Do I need historical time window data to calculate stage-by-stage conversion rates?▼

No, an optional time window can be applied to diagnose funnel performance across specific periods, but core stage-by-stage conversion rates and drop-off volumes are calculated directly from stage names and user counts.

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