funnel-analysis

This skill provides comprehensive funnel analysis capabilities for understanding user conversion patterns and optimizing business processes.

Overview

The Funnel Analysis Skill is designed to analyze multi-step user journeys, calculate conversion rates, and identify optimization opportunities in various business contexts including e-commerce, marketing campaigns, user onboarding, and content consumption.

Features

Core Capabilities

• Multi-step Funnel Construction: Build funnels from user journey data
• Conversion Rate Analysis: Calculate step-by-step and overall conversion rates
• Segmentation Analysis: Compare funnels across different user segments
• Interactive Visualizations: Create engaging funnel charts with Plotly
• Automated Insights: Generate actionable recommendations

Analysis Types

1. Standard Funnel Analysis: Track conversion through defined steps
2. Segmented Analysis: Compare different user groups
3. Temporal Analysis: Track changes over time
4. Cohort Analysis: Analyze behavior by user cohorts
5. A/B Test Analysis: Compare funnel variations

File Structure

funnel-analysis/
├── SKILL.md              # Main skill definition
├── README.md             # This file
├── examples/             # Usage examples
│   ├── basic_funnel.py   # Simple funnel analysis
│   ├── segmented_funnel.py # Segmented analysis
│   └── sample_data/      # Example datasets
└── scripts/              # Utility scripts
    ├── funnel_analyzer.py # Core analysis functions
    └── visualizer.py     # Visualization utilities

Getting Started

Prerequisites

Ensure you have these Python packages installed:

pip install pandas plotly matplotlib numpy seaborn

Basic Usage

1. Prepare your data with user journey steps
2. Define your funnel steps and metrics
3. Run analysis using the provided scripts
4. Visualize results with interactive charts
5. Generate insights for optimization

Data Format Requirements

Your data should include:

• User ID: Unique identifier for each user
• Step indicators: Boolean flags or timestamps for each step
• Segmentation attributes (optional): Device, gender, location, etc.
• Timestamps (optional): For temporal analysis

Examples

E-commerce Example

# Analyze: Homepage → Search → Product View → Add to Cart → Purchase
from scripts.funnel_analyzer import FunnelAnalyzer

analyzer = FunnelAnalyzer()
results = analyzer.analyze_funnel(data, steps)
analyzer.visualize(results)

Marketing Campaign Example

# Track: Ad Click → Landing Page → Sign Up → First Purchase
# Compare by traffic source and device type

Best Practices

1. Data Quality

   • Ensure consistent user identification
   • Handle missing data appropriately
   • Validate step sequences

2. Analysis Design

   • Define clear, logical funnel steps
   • Consider time windows for user journeys
   • Account for multiple touchpoints

3. Interpretation

   • Look for statistically significant patterns
   • Consider business context
   • Focus on actionable insights

Common Use Cases

• E-commerce: Purchase funnel optimization
• SaaS: User onboarding and activation
• Content Platforms: Engagement and conversion
• Lead Generation: Marketing campaign effectiveness
• Mobile Apps: User retention and feature adoption

Troubleshooting

Common Issues

1. Low Conversion Rates

   • Check data quality and step definitions
   • Verify user journey completeness
   • Consider time window adjustments

2. Segment Size Disparities

   • Ensure sufficient sample sizes
   • Consider combining small segments
   • Use statistical significance tests

3. Complex User Journeys

   • Simplify funnel structure
   • Consider multiple funnel paths
   • Use path analysis techniques

Advanced Topics

Statistical Considerations

• Confidence intervals for conversion rates
• A/B test significance testing
• Cohort retention analysis

Extensions

• Machine learning for funnel prediction
• Real-time funnel monitoring
• Multi-channel attribution modeling

Support

For issues or questions, refer to the examples directory or modify the scripts to suit your specific needs.