funnel-analysis

liangdabiao's avatarfrom liangdabiao

Analyze user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow analysis.

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When & Why to Use This Skill

The Funnel Analysis skill is a powerful data analytics tool designed to visualize and optimize user conversion journeys. It enables users to transform raw behavioral data into actionable insights by calculating step-by-step conversion rates, identifying critical bottlenecks, and generating interactive visualizations. By leveraging advanced segmentation and bottleneck identification, this skill helps businesses improve ROI and enhance user experience through data-driven optimization strategies.

Use Cases

  • E-commerce Checkout Optimization: Analyze the path from 'Product View' to 'Purchase' to identify specific steps where customers abandon their carts.
  • User Onboarding Analysis: Track the multi-step registration process in SaaS platforms to find and eliminate friction points for new users.
  • Marketing Campaign Performance: Evaluate the conversion flow from initial ad clicks to landing page engagement and final lead generation.
  • Product Feature Adoption: Measure how effectively users progress through a new feature's workflow to improve overall engagement and retention.
namefunnel-analysis
descriptionAnalyze user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow analysis.
allowed-toolsRead, Write, Edit, Bash, Grep, Glob

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:

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

# 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

  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