/attribution-model

name: attribution-model
description: "Design a multi-touch attribution strategy — recommends the best-fit model for the business's sales cycle and data maturity, defines credit-distribution rules and lookback windows, maps platform-specific setup (GA4, HubSpot, Salesforce, warehouse), and documents tracking gaps and known blind spots. Triggers on \"/digital-marketing-pro:attribution-model\", \"set up multi-touch attribution\", \"which attribution model should we use\", \"configure GA4 attribution\", \"how should we credit channels for conversions\". Reads the brand profile and consumes the canonical model taxonomy in skills/funnel-architect/attribution-models.md; to run the models against real conversion data, pair with /digital-marketing-pro:attribution-report."

/digital-marketing-pro:attribution-model

Purpose

Design and recommend a multi-touch attribution model with implementation guidance, credit distribution rules, and platform-specific configuration. Produces a complete attribution strategy tailored to the business's data maturity, sales cycle, and analytics infrastructure.

Input Required

The user must provide (or will be prompted for):

• **Sales cycle length**: Average number of days from first touchpoint to conversion (e.g., 7 days for e-commerce, 90+ days for B2B enterprise)
• **Active marketing channels**: All channels currently running — paid search, paid social, organic search, email, display, video, affiliate, direct mail, events, referral, content marketing, etc.
• **Conversion types**: The key conversion events being tracked — lead form, MQL, SQL, opportunity, customer, revenue, or e-commerce purchase
• **Data maturity level**: Current analytics sophistication — beginner (basic GA4, limited tagging), intermediate (UTM tracking, CRM integration, multi-platform), or advanced (data warehouse, CDI, unified user IDs)
• **Current analytics tools**: Platforms in use — GA4, HubSpot, Salesforce, Adobe Analytics, Mixpanel, custom data warehouse, or third-party attribution tools
• **Touchpoint volume**: Approximate monthly interactions across all channels (thousands, tens of thousands, hundreds of thousands)
• **Offline touchpoints**: Whether offline channels (trade shows, phone calls, direct mail, in-store visits, sales meetings) play a role in the customer journey
• **Budget allocation philosophy**: How budget decisions are currently made — gut feel, last-click data, blended ROAS, executive direction, or existing attribution data
• **Previous attribution approach**: Any existing attribution model in use and its known shortcomings or limitations
• **Key business questions**: What specific decisions attribution data needs to inform — budget allocation, channel investment, campaign optimization, executive reporting, or vendor evaluation

Process

1. **Load brand context**: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply brand voice, compliance rules for target markets (`skills/context-engine/compliance-rules.md`), and industry context. **Also check for guidelines** at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load restrictions and relevant category files. Check for custom templates at `~/.claude-marketing/brands/{slug}/templates/`. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults. 2. **Assess data maturity and touchpoint landscape**: Map all active touchpoints across channels, evaluate tracking coverage (what percentage of interactions are captured), identify user identity resolution capabilities (logged-in vs. anonymous, cross-device stitching), and score overall data readiness on a 1-5 scale. 3. **Evaluate attribution model options**: Score each model in the canonical taxonomy — see `skills/funnel-architect/attribution-models.md` (the single source for model definitions, the selection decision tree, and platform implementation notes) — against the business context on data requirements, accuracy, actionability, and implementation complexity. Do not re-derive the model list here; consume it from that reference. 4. **Recommend primary model with rationale**: Select the best-fit model based on sales cycle length, data maturity, touchpoint volume, and business questions. Provide a clear explanation of why this model fits and where it will still have blind spots. If data maturity is low, recommend a phased approach starting with a simpler model and graduating to data-driven as tracking matures. 5. **Define credit distribution rules**: Specify exactly how conversion credit is allocated — percentage per touchpoint position, time-decay half-life window, position-based weight splits (e.g., 40% first, 40% last, 20% distributed across middle), and rules for single-touch conversions vs. multi-touch journeys. 6. **Design lookback window**: Set the attribution lookback window based on sales cycle data — typically 1.5-2x the average sales cycle length. Define separate windows for click-through and view-through attribution. Justify the window length with sales cycle analysis and explain the tradeoffs of shorter vs. longer windows. 7. **Map implementation steps per analytics platform**: Create platform-specific configuration guides — GA4 attribution settings and conversion path reports, HubSpot multi-touch revenue attribution setup, Salesforce campaign influence configuration, and custom data warehouse query logic. Include step-by-step setup instructions for each tool in the stack. **GA4 truth (state this to the user):** GA4 exposes only **data-driven** and **last-click** as configurable models (the linear / time-decay / position-based / first-click menu was removed in 2023) — any other credit rule must be modelled in the warehouse/BI layer, not GA4. Also account for GA4's new **"AI Assistant"** default channel (referrals from ChatGPT, G