# Attribution Model > Designs marketing attribution models with channel mapping, weighting logic, reporting recommendations, and implementation steps. Use when understanding which channels drive conversions. - Skill: `reaperinvest/attribution-model` (Agent Skill) - Install (CLI): `npx skillmds@latest add reaperinvest/attribution-model` - Raw SKILL.md: https://api.skillmd.com/api/skills/reaperinvest/attribution-model/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: reaperinvest (https://skillmd.com/u/reaperinvest) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/reaperinvest/attribution-model --- # Attribution Model ## When to Use This Skill Use this skill when you need to: - Understand which marketing channels drive the most conversions - Choose an attribution model for budget allocation decisions - Design a multi-touch attribution framework - Report on marketing ROI by channel **DO NOT** use this skill for setting up tracking pixels, configuring ad platforms, or building attribution software. This is for designing the model and reporting framework. --- ## Core Principle PERFECT ATTRIBUTION IS IMPOSSIBLE — PICK A MODEL THAT IS GOOD ENOUGH TO MAKE BETTER BUDGET DECISIONS THAN GUESSING, AND BE HONEST ABOUT ITS LIMITATIONS. --- ## Phase 1: Brief ### Required Inputs | Input | What to Ask | Default | |-------|------------|---------| | **Channels** | "What marketing channels are you using? (paid ads, SEO, email, social, referral, direct)" | Must be provided | | **Conversion type** | "What is a conversion? (purchase, signup, demo booking, lead form)" | Lead form submission | | **Sales cycle** | "How long from first touch to conversion? (same day, days, weeks, months)" | 1-2 weeks | | **Budget** | "Monthly marketing spend across all channels?" | $1,000-$5,000 | | **Tracking setup** | "What tracking do you have? (UTMs, GA4, CRM, pixel tracking)" | GA4 + UTMs | | **Decision need** | "What decision will this help you make? (where to spend more, what to cut)" | Budget allocation | **GATE: Confirm brief before proceeding.** --- ## Phase 2: Design ### Model Selection Present the options with pros and cons for the user's context: | Model | How It Works | Best For | |-------|-------------|----------| | **Last touch** | 100% credit to final channel | Short sales cycles, simple setups | | **First touch** | 100% credit to discovery channel | Understanding awareness drivers | | **Linear** | Equal credit across all touchpoints | Fair baseline, multi-channel | | **Time decay** | More credit to recent touches | Longer sales cycles | | **Position-based** | 40% first, 40% last, 20% middle | Balanced awareness + conversion credit | | **Data-driven** | Algorithmic based on actual paths | High volume, sophisticated setups | ### Recommended Model Logic For most solopreneurs and small businesses: start with **position-based** or **last-touch** with a first-touch overlay report. Graduate to data-driven when you have 500+ conversions per month. **GATE: Present model recommendation and wait for approval.** --- ## Phase 3: Build ### Deliverables **1. Attribution Model Document** - Selected model with rationale - Channel map with all touchpoints and how they are tracked - Weighting logic explained with examples - Known blind spots and limitations **2. Channel-Conversion Path Map** - Typical customer journeys by channel combination - Example paths: Social ad → Blog post → Email → Purchase - Touchpoint definitions: what counts as a "touch" per channel **3. Reporting Template** - Monthly attribution report structure - Metrics per channel: conversions, assisted conversions, cost per acquisition, ROAS - Comparison: attributed conversions vs. platform-reported conversions (they will differ) **4. Implementation Checklist** - [ ] UTM parameters standardized across all channels - [ ] GA4 conversion events configured - [ ] CRM tracking connected (if applicable) - [ ] Attribution window defined (7 days, 30 days, 90 days) - [ ] First report generated and validated --- ## Phase 4: Polish ### Model Validation After 30 days, check: - Do the attribution numbers make intuitive sense given your spend and effort? - Are any channels getting zero credit despite known activity? - Does the model change your budget allocation decisions? ### Quarterly Review Reassess the model fit every quarter as channels, spend, and volume change. --- ## Example 1: Solo Service Business (3 channels, short sales cycle) **Channels:** Google Ads, Instagram organic, referral **Model:** Last-touch (simple, decisive, sufficient for 3 channels) **Report:** Monthly cost per lead by channel, compare to close rate ## Example 2: E-commerce Brand (6 channels, multi-touch) **Channels:** Meta ads, Google ads, email, SEO, influencer, direct **Model:** Position-based (40/20/40) with 30-day attribution window **Report:** Weekly ROAS by channel, assisted conversion count, path analysis --- ## Anti-Patterns - **Platform-reported only** — every ad platform takes full credit. Facebook and Google will both claim the same conversion. Use independent tracking. - **Ignoring assisted conversions** — a channel with zero last-touch conversions may be generating all your first touches. Check assisted conversions before cutting. - **Over-engineering early** — data-driven attribution with 50 monthly conversions is noise, not signal. Match model complexity to data volume. - **Set and forget** — attribution models need recalibration as your channel mix changes. - **Treating the model as truth** — all models are wrong. Use them to make *better* decisions, not perfect ones. --- ## Recovery - **No UTM tracking in place:** Start tagging all links today. You cannot attribute what you do not track. Provide a UTM setup guide. - **Too few conversions for multi-touch:** Use last-touch attribution and supplement with qualitative data ("How did you hear about us?" survey). - **Platform numbers do not match GA4:** This is normal. Document the discrepancy and use one source of truth for decisions. - **User wants to attribute everything perfectly:** Set expectations that 70-80% accuracy is excellent for attribution. Perfect is not achievable.