revenue-attribution

name	revenue-attribution
description	Analyze marketing and sales touchpoints to attribute revenue credit using multi-touch models and channel contribution analysis
license	MIT
metadata	{"author":"ClawFu","version":"1.0.0","mcp-server":"@clawfu/mcp-skills"}

Revenue Attribution

Determine which marketing and sales activities drive revenue using multi-touch attribution models, enabling smarter budget allocation and campaign optimization.

When to Use This Skill

• Justifying marketing spend to leadership
• Optimizing channel mix allocation
• Evaluating campaign ROI
• Resolving marketing/sales credit disputes
• Building attribution reports

Methodology Foundation

Based on Bizible/Marketo Multi-Touch Attribution and Google Analytics Attribution Models, covering:

• First-touch attribution (awareness credit)
• Last-touch attribution (conversion credit)
• Linear attribution (equal credit)
• Time-decay attribution (recency-weighted)
• Position-based (U-shaped, W-shaped)

What Claude Does vs What You Decide

Claude Does	You Decide
Explains attribution models	Which model fits your business
Calculates credit distribution	How to act on insights
Identifies top-performing channels	Budget reallocation amounts
Shows model comparison	Final attribution policy
Highlights discrepancies	Exception handling

What This Skill Does

1. Model education - Explain different attribution approaches
2. Credit calculation - Apply models to touchpoint data
3. Channel analysis - Compare performance by source
4. Model comparison - Show how results differ by model
5. Optimization recommendations - Where to invest more/less

How to Use

Analyze attribution for this closed-won deal:

Deal: [Company Name]
Value: $[Amount]
Close Date: [Date]
Sales Cycle: [Days]

Touchpoint Journey:
1. [Date] - [Channel] - [Action]
2. [Date] - [Channel] - [Action]
...
[List all touchpoints chronologically]

Questions:
- Which channels deserve credit?
- Compare first-touch vs last-touch
- Recommend budget allocation

Instructions

Step 1: Understand Attribution Models

Model	Logic	Best For
First-Touch	100% to first interaction	Awareness measurement
Last-Touch	100% to final conversion	Direct response
Linear	Equal split across all	Long consideration cycles
Time-Decay	More credit to recent	Sales-assisted journeys
Position-Based	40/20/40 (first/middle/last)	Balanced view
W-Shaped	30/30/30 + 10 remainder	Include MQL moment

Step 2: Map the Customer Journey

Document all touchpoints with:

• Timestamp - When it occurred
• Channel - Source (Paid, Organic, Email, Event, etc.)
• Action - What happened (visit, download, demo, etc.)
• Stage - Awareness, Consideration, Decision

Step 3: Apply Attribution Model

First-Touch Example:

Journey: Paid Search → Email → Webinar → Demo → Close
Revenue: $50,000

First-Touch Attribution:
- Paid Search: $50,000 (100%)
- All others: $0

Linear Example:

Same journey, 4 touchpoints:
- Paid Search: $12,500 (25%)
- Email: $12,500 (25%)
- Webinar: $12,500 (25%)
- Demo: $12,500 (25%)

Position-Based (40/20/40):

- Paid Search: $20,000 (40% - first)
- Email: $5,000 (10% - middle)
- Webinar: $5,000 (10% - middle)
- Demo: $20,000 (40% - last)

Step 4: Aggregate by Channel

Sum attribution across all deals to see total channel contribution:

Channel Performance (Position-Based):
- Paid Search: $500K attributed (35%)
- Events: $300K attributed (21%)
- Organic: $280K attributed (19%)
- Email: $220K attributed (15%)
- Referral: $150K attributed (10%)

Step 5: Calculate ROI by Channel

Channel ROI = Attributed Revenue / Channel Spend

Example:
- Paid Search: $500K revenue / $100K spend = 5x ROI
- Events: $300K revenue / $200K spend = 1.5x ROI

Examples

Example 1: Deal-Level Attribution

Input:

Analyze attribution for TechStart Inc:

Deal: TechStart Inc
Value: $75,000 ARR
Close Date: March 15
Sales Cycle: 62 days

Touchpoint Journey:
1. Jan 12 - Google Ads - Clicked ad, visited pricing
2. Jan 15 - Organic Search - Blog post read
3. Jan 22 - LinkedIn Ad - Whitepaper download (MQL)
4. Feb 1 - Email nurture - Opened 3 emails
5. Feb 10 - Webinar - Attended "Product Demo Day"
6. Feb 18 - Direct - Requested demo (SQL)
7. Mar 5 - Sales - Discovery call
8. Mar 12 - Sales - Proposal review
9. Mar 15 - Direct - Signed contract

Compare first-touch, last-touch, and position-based.

Output:

## Attribution Analysis: TechStart Inc ($75,000)

### Journey Summary
- **Total Touchpoints**: 9
- **Marketing Touchpoints**: 6 (Jan 12 - Feb 18)
- **Sales Touchpoints**: 3 (Mar 5 - Mar 15)
- **Sales Cycle**: 62 days
- **Key Conversion**: LinkedIn whitepaper (MQL), Demo request (SQL)

### Model Comparison

| Channel | First-Touch | Last-Touch | Position-Based | Linear |
|---------|-------------|------------|----------------|--------|
| Google Ads | $75,000 | $0 | $22,500 | $8,333 |
| Organic Search | $0 | $0 | $3,750 | $8,333 |
| LinkedIn Ads | $0 | $0 | $7,500 | $8,333 |
| Email | $0 | $0 | $3,750 | $8,333 |
| Webinar | $0 | $0 | $3,750 | $8,333 |
| Direct (Demo) | $0 | $0 | $7,500 | $8,333 |
| Sales | $0 | $75,000 | $26,250 | $25,000 |

### Position-Based Breakdown (Recommended)

**First Touch (30%): $22,500**
- Google Ads gets full first-touch credit
- Drove initial awareness and pricing interest

**MQL Touch (30%): $22,500**
- LinkedIn Ads: $15,000 (whitepaper = MQL)
- Webinar: $7,500 (influenced SQL)

**Last Touch (30%): $22,500**
- Demo Request: $7,500
- Sales (Close): $15,000

**Middle Touches (10%): $7,500**
- Organic: $2,500
- Email: $2,500
- Other: $2,500

### Insights

1. **Google Ads** initiated the journey - valuable for awareness
2. **LinkedIn Ads** drove the MQL - critical conversion point
3. **Sales** closed but didn't initiate - last-touch overstates
4. **Email** nurtured but didn't convert alone - assist role

### Channel Recommendations

| Channel | Attribution | Action |
|---------|-------------|--------|
| Google Ads | High first-touch | Maintain/increase for awareness |
| LinkedIn Ads | High MQL driver | Invest more for lead gen |
| Webinar | Solid influence | Continue as mid-funnel |
| Email | Assist role | Optimize, don't cut |

Example 2: Multi-Deal Channel Analysis

Input:

Analyze Q1 attribution across these closed deals:

Deal 1: $50K - Google Ads → Email → Demo → Close
Deal 2: $30K - Event → LinkedIn → Demo → Close
Deal 3: $80K - Referral → Demo → Close
Deal 4: $45K - Organic → Webinar → Email → Demo → Close
Deal 5: $25K - Google Ads → Event → Demo → Close

Total Q1 Revenue: $230K

Channel Spend:
- Google Ads: $15K
- Events: $25K
- LinkedIn: $8K
- Email: $3K
- Webinar: $5K
- Referral: $0

Which channels should we invest more in?

Output:

## Q1 Channel Attribution Analysis

### Total Revenue: $230,000

### Attribution by Model

#### Position-Based Attribution (40/20/40)

| Channel | First | Middle | Last | Total | % of Rev |
|---------|-------|--------|------|-------|----------|
| Google Ads | $30,000 | $2,500 | $0 | $32,500 | 14% |
| Events | $12,000 | $5,000 | $0 | $17,000 | 7% |
| LinkedIn | $0 | $6,000 | $0 | $6,000 | 3% |
| Email | $0 | $11,000 | $0 | $11,000 | 5% |
| Webinar | $0 | $9,000 | $0 | $9,000 | 4% |
| Referral | $32,000 | $0 | $0 | $32,000 | 14% |
| Demo | $0 | $0 | $30,500 | $30,500 | 13% |
| Sales/Close | $0 | $0 | $92,000 | $92,000 | 40% |

### ROI Analysis

| Channel | Attributed Rev | Spend | ROI |
|---------|----------------|-------|-----|
| Referral | $32,000 | $0 | ∞ (Best) |
| Google Ads | $32,500 | $15,000 | 2.2x |
| Webinar | $9,000 | $5,000 | 1.8x |
| LinkedIn | $6,000 | $8,000 | 0.75x |
| Events | $17,000 | $25,000 | 0.68x |
| Email | $11,000 | $3,000 | 3.7x |

### Efficiency Ranking

1. **🥇 Referral** - $0 cost, $32K attributed → Infinite ROI
2. **🥈 Email** - 3.7x ROI → High-value nurture
3. **🥉 Google Ads** - 2.2x ROI → Profitable acquisition
4. **Webinar** - 1.8x ROI → Solid mid-funnel
5. **LinkedIn** - 0.75x ROI → Below break-even
6. **Events** - 0.68x ROI → Expensive for return

### Recommendations

**Increase Investment:**
- **Referral Program**: 14% of revenue at $0 cost
  - Formalize referral rewards
  - Target: 2x referral deals in Q2

- **Email Nurture**: 3.7x ROI
  - Expand sequences
  - Add $2K budget for tools

- **Google Ads**: 2.2x ROI
  - Profitable, test 20% budget increase
  - Focus on high-intent keywords

**Optimize/Test:**
- **LinkedIn**: 0.75x is below target
  - Test new audiences before cutting
  - Could be essential for certain segments

**Reduce/Reallocate:**
- **Events**: $25K for $17K attributed
  - Evaluate which events drive pipeline
  - Consider smaller, targeted events
  - Reallocate $10K to Google Ads

### Proposed Q2 Budget Shift

| Channel | Q1 Spend | Q2 Proposed | Change |
|---------|----------|-------------|--------|
| Google Ads | $15K | $20K | +$5K |
| Events | $25K | $15K | -$10K |
| LinkedIn | $8K | $8K | — |
| Email | $3K | $5K | +$2K |
| Webinar | $5K | $6K | +$1K |
| Referral | $0 | $2K (rewards) | +$2K |
| **Total** | **$56K** | **$56K** | Rebalanced |

Skill Boundaries

What This Skill Does Well

• Explaining attribution model mechanics
• Calculating credit across touchpoints
• Comparing models side-by-side
• Identifying channel efficiency

What This Skill Cannot Do

• Access actual CRM/analytics data
• Track offline touchpoints automatically
• Account for brand lift effects
• Prove causation (only correlation)

When to Escalate to Human

• Choosing official attribution model for company
• Budget allocation decisions over $50K
• Complex B2B journeys with multiple stakeholders
• Reconciling attribution across systems

Iteration Guide

Follow-up Prompts

• "How would results change with time-decay model?"
• "What if we excluded sales touchpoints?"
• "Show me channel performance by deal size."
• "Build attribution for all Q1 deals (I'll provide data)."

Attribution Maturity

1. Basic: Last-touch only
2. Intermediate: First and last comparison
3. Advanced: Position-based or custom
4. Expert: ML-based algorithmic attribution

Checklists & Templates

Attribution Report Template

## Attribution Report: [Period]

### Summary
- Total Revenue: $X
- Deals Analyzed: X
- Model Used: [Position-Based]

### Channel Attribution
| Channel | Revenue | % | ROI |
|---------|---------|---|-----|

### Top Insights
1.
2.
3.

### Budget Recommendations
| Channel | Current | Recommended | Rationale |
|---------|---------|-------------|-----------|

Touchpoint Tracking Checklist

• UTM parameters on all campaigns
• CRM synced with marketing automation
• Offline events logged manually
• Sales activities timestamped
• Content downloads tracked

References

• Bizible Multi-Touch Attribution Guide
• Google Analytics Attribution Modeling
• Forrester B2B Attribution Research
• Marketo Revenue Cycle Analytics

Related Skills

• pipeline-forecasting - Predict revenue by source
• lead-scoring - Score by attributed channel
• ad-spend-optimizer - Automate budget shifts

Skill Metadata

• Domain: RevOps
• Complexity: Advanced
• Mode: centaur
• Time to Value: 30-60 min for analysis
• Prerequisites: Touchpoint data, deal values, channel spend