本文へ移動
cccskills
無料GitHub で公開

revenue-attribution

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。

guia-matthieu/clawfu-skillsスター 150(GitHub 150 + cccskills 0)

実行スコア 未検証

インストール方法を見る

含まれるファイル(1)

  • SKILL.md11.3 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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 DoesYou Decide
Explains attribution modelsWhich model fits your business
Calculates credit distributionHow to act on insights
Identifies top-performing channelsBudget reallocation amounts
Shows model comparisonFinal attribution policy
Highlights discrepanciesException 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

ModelLogicBest For
First-Touch100% to first interactionAwareness measurement
Last-Touch100% to final conversionDirect response
LinearEqual split across allLong consideration cycles
Time-DecayMore credit to recentSales-assisted journeys
Position-Based40/20/40 (first/middle/last)Balanced view
W-Shaped30/30/30 + 10 remainderInclude 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

レビュー

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。

コメント

ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。

まだコメントはありません。最初のコメントを書いてみませんか。

送信するとすぐに公開されます。メールアドレスや電話番号などの個人情報は書かないでください。URL は 2 つまでです。

0 / 1000 文字

このフォームは reCAPTCHA で保護されており、Google のプライバシーポリシーと利用規約が適用されます。

同じリポジトリのスキル

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。

guia-matthieu/clawfu-skills1502026年10月1日 更新

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。

guia-matthieu/clawfu-skills1502026年10月1日 更新

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。

guia-matthieu/clawfu-skills1502026年10月1日 更新

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。

guia-matthieu/clawfu-skills1502026年10月1日 更新

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。

guia-matthieu/clawfu-skills1502026年10月1日 更新

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。

guia-matthieu/clawfu-skills1502026年10月1日 更新

guia-matthieu のスキルをすべて見る

このスキルの問題を報告する