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funnel-analysis

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Funnel Analysis Skill

漏斗分析技能

Analyze user behavior through multi-step conversion funnels to identify bottlenecks and optimization opportunities in marketing campaigns, user journeys, and business processes.
通过多步骤转化漏斗分析用户行为，识别营销活动、用户旅程和业务流程中的瓶颈与优化机会。

Quick Start

快速开始

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
本技能可帮助您：
  1. 基于多步骤用户数据构建转化漏斗
  2. 计算各步骤间的转化率
  3. 按不同用户属性进行细分分析
  4. 使用Plotly创建交互式可视化图表
  5. 生成业务洞察及优化建议

When to Use

适用场景

  • Marketing campaign analysis (promotion → purchase)
  • User onboarding flow analysis
  • Website conversion funnel optimization
  • App user journey analysis
  • Sales pipeline analysis
  • Lead nurturing process analysis
  • 营销活动分析（推广 → 购买）
  • 用户注册流程分析
  • 网站转化漏斗优化
  • 应用用户旅程分析
  • 销售渠道分析
  • 线索培育流程分析

Key Requirements

关键要求

Install required packages:
bash
pip install pandas plotly matplotlib numpy seaborn
安装所需依赖包：
bash
pip install pandas plotly matplotlib numpy seaborn

Core Workflow

核心工作流程

1. Data Preparation

1. 数据准备

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)
您的数据应包含：
  • 用户旅程步骤（点击、页面浏览、操作行为）
  • 用户标识（customer_id、user_id等）
  • 时间戳或步骤指示器
  • 可选：用于细分的用户属性（性别、设备、地域）

2. Analysis Process

2. 分析流程

  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
  1. 加载并合并用户旅程数据
  2. 定义漏斗步骤并计算指标
  3. 进行细分分析（按设备、性别等）
  4. 创建可视化图表
  5. 生成洞察与建议

3. Output Deliverables

3. 输出交付物

  • Funnel visualization charts
  • Conversion rate tables
  • Segmented analysis reports
  • Optimization recommendations
  • 漏斗可视化图表
  • 转化率表格
  • 细分分析报告
  • 优化建议

Example Usage Scenarios

示例使用场景

E-commerce Purchase Funnel

电商购买漏斗

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Steps: Promotion → Search → Product View → Add to Cart → Purchase

步骤：推广 → 搜索 → 商品浏览 → 加入购物车 → 购买

Analyze by device type and customer segment

按设备类型和客户细分进行分析

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User Registration Funnel

用户注册漏斗

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Steps: Landing Page → Sign Up → Email Verification → Profile Complete

步骤：着陆页 → 注册 → 邮箱验证 → 资料完善

Identify where users drop off most

识别用户流失最严重的环节

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Content Consumption Funnel

内容消费漏斗

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Steps: Article View → Comment → Share → Subscribe

步骤：文章浏览 → 评论 → 分享 → 订阅

Measure engagement conversion rates

衡量参与度转化率

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Common Analysis Patterns

常见分析模式

  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
  1. 瓶颈识别：找出流失率最高的步骤
  2. 细分对比：对比不同用户群体的转化情况
  3. 时间趋势分析：跟踪转化率随时间的变化
  4. A/B测试：对比不同漏斗变体的效果
  5. 优化效果评估：衡量优化前后的变化

Integration Examples

集成示例

See examples/ directory for:
  • basic_funnel.py
    - Simple funnel analysis
  • segmented_funnel.py
    - Advanced segmentation analysis
  • Sample datasets for testing
查看examples/目录获取：
  • basic_funnel.py
    - 简单漏斗分析示例
  • segmented_funnel.py
    - 高级细分分析示例
  • 用于测试的样本数据集

Best Practices

最佳实践

  • Ensure data quality and consistency
  • Define clear funnel steps
  • Consider user journey time windows
  • Validate statistical significance
  • Focus on actionable insights
  • 确保数据质量与一致性
  • 定义清晰的漏斗步骤
  • 考虑用户旅程的时间窗口
  • 验证统计显著性
  • 聚焦可落地的洞察