Funnel Drop-Off Analysis & Root Cause Identification
Analytics & ReportingintermediateClaude 3.5 Sonnet or GPT-4o. Claude excels at structured analysis and multi-step reasoning through complex datasets. GPT-4o offers faster processing for time-sensitive analysis. Both handle the analytical framework well, but Claude's reasoning is slightly more methodical for identifying non-obvious root causes.
When to Use This Prompt
Use this prompt when you've noticed significant drop-offs in your conversion funnel and need to quickly identify root causes and prioritize fixes. It's especially valuable when you have funnel data but lack the analytical bandwidth to dig into why users are leaving at specific stages.
The Prompt
Analyze the following conversion funnel data and identify root causes for drop-offs at each stage. Provide actionable insights and prioritized recommendations.
## Funnel Data
Provide your funnel metrics in this format:
- Stage Name: [NUMBER] users
- Stage Name: [NUMBER] users
- Stage Name: [NUMBER] users
- Stage Name: [NUMBER] users
Example:
- Landing Page: 10,000 users
- Product Page: 6,500 users
- Add to Cart: 2,100 users
- Checkout: 890 users
- Purchase: 445 users
## Additional Context
Provide any relevant details:
- Industry/Product Type: [DESCRIBE]
- Traffic Source(s): [e.g., Paid Search, Organic, Direct]
- Device Breakdown: [e.g., 60% Mobile, 40% Desktop]
- Time Period: [DATE RANGE]
- Notable Changes: [Any recent updates, campaigns, or technical changes]
- Customer Segment: [e.g., New vs. Returning, Geographic region]
## Analysis Framework
For each stage with significant drop-off (>20% decline from previous stage):
1. **Calculate Drop-Off Rate**: Show the percentage decline
2. **Identify Likely Causes**: Consider technical, UX, messaging, and friction factors
3. **Benchmark Context**: Compare to industry standards if known
4. **User Experience Factors**: Evaluate page load speed, form complexity, trust signals, clarity
5. **Competitive Context**: Consider what competitors typically offer at this stage
## Deliverables
Provide:
- Summary of critical drop-off points (ranked by impact)
- Root cause analysis for top 3 drop-off stages
- Specific, testable hypotheses for each major drop-off
- Prioritized action items with estimated impact potential
- Quick wins vs. long-term improvements
- Recommended A/B tests or experiments
Format the response with clear headers, bullet points, and a prioritization matrix showing effort vs. impact.
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Tips for Best Results
- 1.Include as much context as possible—traffic source, device type, and recent changes dramatically improve root cause accuracy. Generic funnel data produces generic insights.
- 2.Segment your funnel data if possible (new vs. returning users, traffic source, device type). Different segments often have different drop-off patterns requiring different solutions.
- 3.Ask the AI to prioritize by effort vs. impact, not just by drop-off percentage. A 10% improvement on a quick win beats a 30% improvement requiring 6 months of development.
- 4.Follow up with specific A/B test prompts for top recommendations. Use this analysis to inform hypothesis-driven testing rather than guessing at solutions.
Example Output
## Funnel Analysis Summary
**Critical Drop-Off Points:**
1. Product Page → Add to Cart: 67.7% drop-off (CRITICAL)
2. Landing Page → Product Page: 35% drop-off (HIGH)
3. Checkout → Purchase: 49.4% drop-off (HIGH)
---
## Root Cause Analysis
### Stage 1: Landing Page → Product Page (35% Drop-Off)
**Likely Causes:**
- Unclear value proposition or product benefits
- Weak call-to-action messaging
- Mobile responsiveness issues
- Slow page load time (>3 seconds)
**Hypothesis:** Users aren't convinced of product relevance before clicking through.
**Quick Win:** A/B test headline emphasizing top 3 customer benefits vs. current generic headline.
---
### Stage 2: Product Page → Add to Cart (67.7% Drop-Off)
**Likely Causes:**
- Missing or unclear pricing information
- Insufficient product images/videos
- Lack of social proof (reviews, ratings)
- Complex product configuration options
- High shipping costs revealed too late
**Hypothesis:** Users need more confidence in product quality and value before committing to cart.
**Quick Win:** Add customer review section with 4+ star average and video testimonial above fold.
---
### Stage 3: Checkout → Purchase (49.4% Drop-Off)
**Likely Causes:**
- Unexpected costs (shipping, taxes) at final step
- Complex checkout form (>8 fields)
- Limited payment options
- No guest checkout option
- Security trust signals missing
**Hypothesis:** Friction and trust concerns emerge at payment stage.
**Quick Win:** Implement one-click checkout and display security badges prominently.
---
## Prioritized Action Matrix
**High Impact, Low Effort (Do First):**
- Add customer reviews to product page
- Simplify checkout form to 5 essential fields
- Display security/trust badges
**High Impact, Medium Effort (Plan Next):**
- Create product video content
- Implement guest checkout option
- Show shipping costs on product page
**Medium Impact, Low Effort (Quick Wins):**
- A/B test landing page headlines
- Add FAQ section to product page
- Implement exit-intent offer
## Recommended Experiments
1. Test product page with customer reviews (Est. 15-25% lift)
2. Simplify checkout to 5 fields (Est. 10-18% lift)
3. Show shipping upfront (Est. 8-12% lift)
Related Prompts
Related Reading
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Courses, workshops, frameworks, daily intelligence, and 6 proprietary tools — built for marketing leaders adopting AI.
Trusted by 10,000+ Directors and CMOs.