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