| 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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 exposes only and 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 default channel (referrals from ChatGPT, Gemini, Copilot, Perplexity, etc.) in the channel breakdown so AI-sourced conversions aren't misfiled under Referral/Direct.
Output
A structured attribution model recommendation containing:
- Attribution model recommendation with detailed rationale connecting the model choice to sales cycle, data maturity, and business questions
- Credit distribution rules — percentage allocation per touchpoint position with examples showing how a sample multi-touch journey would be credited
- Lookback window recommendation with sales cycle justification, click-through vs. view-through windows, and tradeoff analysis
- Implementation guide per platform — step-by-step GA4 attribution setup, HubSpot multi-touch configuration, Salesforce campaign influence settings, and custom warehouse query templates
- Touchpoint taxonomy — standardized hierarchy of channel, source, medium, and campaign with naming conventions for consistent tracking
- Data requirements checklist — what must be tracked, tagged, and integrated for the model to function accurately
- Tracking gap analysis — identified gaps ranked by impact on attribution accuracy, with fix recommendations and effort estimates
- Attribution reporting dashboard spec — metrics, dimensions, filters, visualizations, comparison views, and executive summary format
- Model comparison table — 6-7 models compared side-by-side on pros, cons, data requirements, best-fit scenarios, and implementation complexity
- Evaluation framework — quarterly review criteria, model reassessment triggers, and maturity graduation path from simple to advanced models
- Known limitations and blind spots — explicit documentation of what the model cannot measure with stakeholder expectation-setting guidance
- Cross-device and cross-platform considerations — user identity resolution approaches, deterministic vs. probabilistic matching, and platform-specific limitations
- Offline-to-online stitching recommendations — methods for incorporating trade shows, phone calls, direct mail, and in-person interactions into the digital attribution model
Agents Used
- analytics-analyst — Data maturity assessment, attribution model evaluation, credit distribution design, lookback window analysis, platform implementation guidance, tracking gap identification, reporting framework design, and limitation documentation
