Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Analyze marketing and sales touchpoints to attribute revenue credit using multi-touch models and channel contribution analysis
日本語の概要は準備中です。原文の説明を表示しています。
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Determine which marketing and sales activities drive revenue using multi-touch attribution models, enabling smarter budget allocation and campaign optimization.
Based on Bizible/Marketo Multi-Touch Attribution and Google Analytics Attribution Models, covering:
| 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 |
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
| 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 |
Document all touchpoints with:
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)
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%)
Channel ROI = Attributed Revenue / Channel Spend
Example:
- Paid Search: $500K revenue / $100K spend = 5x ROI
- Events: $300K revenue / $200K spend = 1.5x ROI
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 |
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 |
## 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 |
|---------|---------|-------------|-----------|
pipeline-forecasting - Predict revenue by sourcelead-scoring - Score by attributed channelad-spend-optimizer - Automate budget shiftsまだレビューはありません。使ってみた感想をお寄せください。
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
日本語の概要は準備中です。原文の説明を表示しています。
Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
日本語の概要は準備中です。原文の説明を表示しています。
Assess customer account health using product usage, support sentiment, payment status, and relationship signals
日本語の概要は準備中です。原文の説明を表示しています。
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
日本語の概要は準備中です。原文の説明を表示しています。
Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
日本語の概要は準備中です。原文の説明を表示しています。