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