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funnel-analysis

funnel-analysis

github.com/nicepkgData & databases28866 installsUpdated 40w ago
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About funnel-analysis

A data-analysis skill for examining multi-step conversion funnels across marketing, product, and sales journeys. It calculates step-level conversion, compares segments, visualizes results with Plotly, and supports recommendations for improvement.

Key features

  1. 01Build multi-step conversion funnels
  2. 02Calculate step conversion rates
  3. 03Segment users by attributes
  4. 04Create Plotly visualizations
  5. 05Identify drop-offs and trends

Use cases

  1. 01Finding drop-offs in user onboarding
  2. 02Comparing campaign conversions by device
  3. 03Reviewing sales pipeline or purchase journeys

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nicepkg/ai-workflow --skill funnel-analysis

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

Bundled files

References and scripts installed along with SKILL.md.

examples/
basic_funnel_report.html
basic_funnel.py
segmented_funnel_report.html
segmented_funnel.py
scripts/
funnel_analyzer.py
visualizer.py

Details

From the project README

Funnel Analysis Skill

Analyze user behavior through multi-step conversion funnels to identify bottlenecks and optimization opportunities in marketing campaigns, user journeys, and business processes.

Quick Start

This skill helps you:

  1. Build conversion funnels from multi-step user data
  2. Calculate conversion rates between each step
  3. Perform segmentation analysis by different user attributes
  4. Create interactive visualizations with Plotly
  5. Generate business insights and optimization recommendations

When to Use

  • Marketing campaign analysis (promotion → purchase)
  • User onboarding flow analysis
  • Website conversion funnel optimization
  • App user journey analysis
  • Sales pipeline analysis
  • Lead nurturing process analysis

Key Requirements

Install required packages:

pip install pandas plotly matplotlib numpy seaborn

Core Workflow

1. Data Preparation

Your data should include:

  • User journey steps (clicks, page views, actions)
  • User identifiers (customer_id, user_id, etc.)
  • Timestamps or step indicators
  • Optional: user attributes for segmentation (gender, device, location)
2. Analysis Process
  1. Load and merge user journey data
  2. Define funnel steps and calculate metrics
  3. Perform segmentations (by device, gender, etc.)
  4. Create visualizations
  5. Generate insights and recommendations
3. Output Deliverables
  • Funnel visualization charts
  • Conversion rate tables
  • Segmented analysis reports
  • Optimization recommendations

Example Usage Scenarios

E-commerce Purchase Funnel
# Steps: Promotion → Search → Product View → Add to Cart → Purchase
# Analyze by device type and customer segment
User Registration Funnel
# Steps: Landing Page → Sign Up → Email Verification → Profile Complete
# Identify where users drop off most
Content Consumption Funnel
# Steps: Article View → Comment → Share → Subscribe
# Measure engagement conversion rates

Common Analysis Patterns

  1. Bottleneck Identification: Find steps with highest drop-off rates
  2. Segment Comparison: Compare conversion across user groups
  3. Temporal Analysis: Track conversion over time
  4. A/B Testing: Compare different funnel variations
  5. Optimization Impact: Measure changes before/after improvements

Integration Examples

See examples/ directory for:

  • basic_funnel.py - Simple funnel analysis
  • segmented_funnel.py - Advanced segmentation analysis
  • Sample datasets for testing

Best Practices

  • Ensure data quality and consistency
  • Define clear funnel steps
  • Consider user journey time windows
  • Validate statistical significance
  • Focus on actionable insights

Comments

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