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Conversion Waterfall

Analyze customer conversion through funnel stages. Use for identifying drop-off points and optimization opportunities.
{"translation": "分析漏斗各阶段客户转化,定位流失点与优化机会。"}
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未分类 clawhub v1.0.0 1 版本 100000 Key: 无需
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概述

Conversion Waterfall

Metadata

  • Name: conversion-waterfall
  • Description: Customer funnel analysis and conversion tracking
  • Triggers: conversion, funnel, drop-off, customer journey

Instructions

Analyze conversion rates at each stage of the customer journey for $ARGUMENTS.

Framework

The Funnel Structure

┌─────────────────────────────────┐
│         AWARENESS               │  100,000 visitors
│         100%                    │
├─────────────────────────────────┤
│         INTEREST                │   50,000 engaged
│         50%                     │  ← 50% drop-off
├─────────────────────────────────┤
│         CONSIDERATION           │   20,000 qualified
│         20%                     │  ← 60% drop-off
├─────────────────────────────────┤
│         INTENT                  │    8,000 trials
│         8%                      │  ← 60% drop-off
├─────────────────────────────────┤
│         PURCHASE                │    2,000 customers
│         2%                      │  ← 75% drop-off
└─────────────────────────────────┘

Output

## Conversion Waterfall: [Product/Service]

### Funnel Metrics

| Stage | Volume | Conversion Rate | Drop-off | Value |
|-------|--------|-----------------|----------|-------|
| Awareness | 100,000 | 100% | - | - |
| Interest | 50,000 | 50% | 50,000 | $0 |
| Consideration | 20,000 | 40% | 30,000 | $0 |
| Intent | 8,000 | 40% | 12,000 | $0 |
| Purchase | 2,000 | 25% | 6,000 | $200K |

### Key Findings

**Largest Drop-off**: Consideration → Intent (60% loss)
- Root cause: [Analysis]
- Opportunity: [Potential improvement]

**Best Conversion**: Awareness → Interest (50%)
- Driver: [What's working]

### Recommendations

1. **[Priority 1]**: Address [stage] drop-off
2. **[Priority 2]**: Improve [stage] conversion

Tips

  • Focus on largest drop-offs first
  • Quantify revenue impact of improvements
  • Compare to industry benchmarks
  • Test changes incrementally

版本历史

共 1 个版本

  • v1.0.0 当前
    2026-03-31 01:18 安全 安全

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