Attribution Model Analyzer
Analytics & ReportingadvancedClaude 3.5 Sonnet or GPT-4o. Both excel at complex data analysis and multi-framework comparisons. Claude handles nuanced business reasoning slightly better; GPT-4o produces more detailed tables. For very large datasets (10k+ journeys), use Claude for efficiency.
When to Use This Prompt
Use this prompt when you need to move beyond vanity metrics and understand true channel contribution to revenue. It's essential for CMOs making budget allocation decisions, especially when current attribution models may be masking underperforming or over-credited channels. Perfect for quarterly business reviews or when planning next year's marketing mix.
The Prompt
You are a marketing analytics expert specializing in attribution modeling. Analyze the following customer journey data and provide a comprehensive attribution analysis.
## Input Data
Customer Journey Data:
- [PASTE YOUR CUSTOMER JOURNEY DATA: Include touchpoints, channels, dates, and conversion values]
- Time period analyzed: [START DATE] to [END DATE]
- Total conversions: [NUMBER]
- Total revenue: [AMOUNT]
## Current Attribution Model
Your organization currently uses: [FIRST-TOUCH / LAST-TOUCH / LINEAR / TIME-DECAY / CUSTOM]
## Analysis Framework
### 1. Model Comparison
Compare the performance of at least 4 attribution models:
- First-Touch Attribution
- Last-Touch Attribution
- Linear Attribution
- Time-Decay Attribution (40-20-40 distribution)
For each model, calculate:
- Credit allocation by channel
- Conversion contribution percentage
- Revenue attributed
- ROI implications
### 2. Channel Performance Insights
For each marketing channel in the data, identify:
- Primary role (awareness, consideration, conversion)
- Average position in customer journey
- Conversion rate when present
- Interaction frequency
- Revenue impact under different models
### 3. Journey Pattern Analysis
Identify and quantify:
- Most common customer journey paths
- Shortest paths to conversion
- Longest paths and their conversion rates
- Channel sequences that drive highest-value conversions
- Channels that appear in winning vs. losing journeys
### 4. Recommendations
Provide actionable recommendations including:
- Optimal attribution model for this business
- Budget reallocation suggestions by channel
- Channels to expand, optimize, or reduce
- Specific insights about channel interactions
- Implementation roadmap with 30/60/90 day milestones
### 5. Risk Assessment
Identify:
- Over-attribution risks
- Under-valued channels
- Data quality issues or gaps
- Seasonal or cyclical patterns affecting attribution
## Output Format
Provide analysis in this structure:
1. Executive Summary (2-3 key findings)
2. Model Comparison Table
3. Channel Performance Rankings
4. Top 5 Customer Journey Paths
5. Strategic Recommendations
6. Implementation Considerations
Be specific with percentages, revenue figures, and actionable next steps. Explain the 'why' behind each recommendation.
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Tips for Best Results
- 1.Include actual customer journey data with specific channels, dates, and revenue values. Generic data produces generic recommendations. Use your actual CRM or analytics export.
- 2.Specify your business model (B2B vs B2C, sales cycle length, average order value). Attribution interpretation differs dramatically—a 90-day B2B cycle needs different models than 7-day B2C.
- 3.Ask the AI to flag data quality issues. Missing touchpoints, inconsistent naming, or tracking gaps will skew attribution. Request specific validation checks before accepting recommendations.
- 4.Request sensitivity analysis: ask 'What if we reallocate 10% of budget from X to Y?' This helps stress-test recommendations before implementation and builds stakeholder confidence.
Example Output
## Executive Summary
Your current last-touch attribution model is crediting email with 42% of conversions, but time-decay analysis reveals social media and content marketing drive 67% of early-stage awareness that enables those email conversions. Reallocating 15% of email budget to content and social could increase overall conversion rate by 8-12%.
## Model Comparison Table
| Channel | First-Touch | Last-Touch | Linear | Time-Decay |
|---------|------------|-----------|--------|------------|
| Paid Search | 8% | 35% | 22% | 28% |
| Email | 5% | 42% | 24% | 18% |
| Social Media | 31% | 12% | 25% | 32% |
| Content | 38% | 8% | 19% | 18% |
| Organic | 18% | 3% | 10% | 4% |
## Top Customer Journey Paths
1. Content → Social → Paid Search → Email → Conversion (23% of conversions, $4,200 avg value)
2. Organic → Email → Conversion (18% of conversions, $3,100 avg value)
3. Social → Paid Search → Email → Conversion (15% of conversions, $5,800 avg value)
## Strategic Recommendations
- Increase content marketing investment by 20% (currently undervalued in last-touch model)
- Optimize email nurture sequences for mid-funnel prospects from social and content
- Implement time-decay attribution model within 60 days
- Reduce paid search spend by 10% and reallocate to content amplification
## Implementation Timeline
- Week 1-2: Audit current tracking and data quality
- Week 3-4: Set up time-decay model in analytics platform
- Week 5-6: Pilot budget reallocation with content and social teams
- Week 7-8: Measure impact and adjust
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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.