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attribution

Work out which channels actually drive revenue and reconcile conflicting platform numbers.

Skill by poorvith-mp

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Work out which channels actually drive revenue and reconcile conflicting platform numbers. Use when tracking UTM campaigns, referral sources, or conversion loops.

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/attribution

What's inside

Skill name
attribution
Description
Work out which channels actually drive revenue and reconcile conflicting platform numbers. Use when tracking UTM campaigns, referral sources, or conversion loops.
License
MIT
Instructions
53 lines in SKILL.md · view on GitHub

Files (3)

  • SKILL.md
  • attribution.skill
  • routing.yaml · evals/

SKILL.md (first lines)

# Attribution

Attribution is the science of determining which marketing touchpoints drive business revenue. Platform-reported metrics (Meta Ads Manager, Google Ads) routinely double-count conversions due to overlapping 7-day click and 1-day view attribution windows. A reliable attribution framework combines first-party tracking, blended marketing efficiency ratios (MER), and lightweight multi-touch models.

## 1. The Platform Overlap Problem & Multi-Counting
When a prospect clicks a Google Search ad, views a LinkedIn ad, and later converts via an email link:
- Google Ads claims 100% credit for the sale.
- LinkedIn Ads claims 100% credit for the sale.
- Meta claims 100% credit if they saw an Instagram ad in the last 24 hours.
*Result*: Platform dashboards report 3 conversions for a single customer transaction.

## 2. Core Attribution Methodologies

### A. First-Touch Attribution
- **Credit**: 100% of revenue attributed to the first recorded marketing touchpoint.
- **Best Used For**: Measuring top-of-funnel discovery channels (content, PR, organic search, early brand ads).
- **Weakness**: Completely ignores mid-funnel nurture and closing channels.

### B. Last-Touch (Last Non-Direct Click) Attribution
- **Credit**: 100% of revenue attributed to the final campaign prior to conversion (excluding direct visits).
- **Best Used For**: Evaluating conversion-focused channels (retargeting, promotional emails, brand search).
- **Weakness**: Starves top-of-funnel awareness channels of budget.

### C. Blended Marketing Efficiency Ratio (MER) — The Source of Truth
Avoid getting lost in conflicting click attribution models by tracking aggregate financial return:
$$\text{MER} = \frac{\text{Total Gross Revenue}}{\text{Total Marketing Spend (All Channels)}}$$
$$\text{Blended CAC} = \frac{\text{Total Marketing \& Sales Spend}}{\text{Total New Customers Acquired}}$$
*Rule*: If total ad spend increases by $20,000 and total top-line revenue only increases by $5,000, your incremental ROAS is negative, regardless of what platform dashboards report.

## 3. First-Party Tracking Infrastructure
Capture UTM parameters and click IDs on initial site visit and persist them in user session cookies:
1. Store `utm_source`, `utm_medium`, `utm_campaign`, `gclid`, and `fbclid` in `localStorage` or first-party cookie for 30 days.
2. Upon user registration or checkout, send these persisted attribution parameters into your backend database alongside the user profile.
3. Send server-to-server conversion events (Meta Conversions API - CAPI, Google Enhanced Conversions) matching on hashed email and transaction IDs.

## Critical Rules
1. Never judge marketing channel profitability on in-platform ROAS alone without cross-referencing first-party CRM data.
2. Store the original first-touch attribution parameters permanently on the user record in your production database.
3. Exclude branded search clicks from paid acquisition ROAS calculations to avoid taking credit for organic demand.

## Verification Checklist
- [ ] First-party attribution script captures and persists UTM parameters across multi-page sessions.
- [ ] Backend database records first-touch and last-touch parameters on the user profile.
- [ ] Server-side conversion tracking (Meta CAPI / Google Enhanced Conversions) configured.
- [ ] Marketing Efficiency Ratio (MER) calculated weekly against aggregate spend.
- [ ] Deduplication verified across ad platform conversion tags.

## Anti-Patterns
- NEVER rely on 1-day view-through attribution to justify scaling paid social budgets.
- NEVER allow paid branded search to claim credit for users who already intended to visit the site.
- NEVER change attribution models retroactively without recalculating historical channel baselines.

From poorvith-mp/skills-marketing, MIT.

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Last commit (this folder)
Sep 12, 2026 (1 month ago)
Last release
Oct 2, 2026 (26 days ago)

From GitHub (fetched Oct 24, 2026).

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License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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attribution skill. Agent Mart. https://agentmart.dev/skills/poorvith-mp-attribution (last updated 2026-10-24)