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:

bash
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

python
# Steps: Promotion → Search → Product View → Add to Cart → Purchase
# Analyze by device type and customer segment

User Registration Funnel

python
# Steps: Landing Page → Sign Up → Email Verification → Profile Complete
# Identify where users drop off most

Content Consumption Funnel

python
# 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