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Funnel Analysis

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nimrodfisher
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.

Overview

Publishernimrodfisher
Repositorydata-analytics-skills
Skill namefunnel-analysis
Stars
493
Forks
87
Bundled files
3
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nimrodfisher on GitHub. Read the source before you install it.

Installation

Install the Funnel Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/funnel-analysis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git /tmp/data-analytics-skills
mkdir -p .claude/skills
cp -r /tmp/data-analytics-skills/03-data-analysis-investigation/funnel-analysis .claude/skills/funnel-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Funnel Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Funnel Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Funnel Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Funnel Analysis AI skill do?

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.

Why use Funnel Analysis on TypingMind?

Because you install it once and use it with any model. Funnel Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Funnel Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/funnel-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Funnel Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Funnel Analysis?

As many as you like. As long as a model supports skills, you can use Funnel Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Funnel Analysis AI skill free?

Yes. It is published on GitHub by nimrodfisher under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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