Funnel Analysis

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:

• Build conversion funnels from multi-step user data
• Calculate conversion rates between each step
• Perform segmentation analysis by different user attributes
• Create interactive visualizations with Plotly
• 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

• Load and merge user journey data
• Define funnel steps and calculate metrics
• Perform segmentations (by device, gender, etc.)
• Create visualizations
• 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

• Bottleneck Identification: Find steps with highest drop-off rates
• Segment Comparison: Compare conversion across user groups
• Temporal Analysis: Track conversion over time
• A/B Testing: Compare different funnel variations
• 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