attribution-model
FeaturedDesign 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.
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Quality Score: 92/100
Skill Content
Details
- Author
- indranilbanerjee
- Repository
- indranilbanerjee/digital-marketing-pro
- Created
- 7 months ago
- Last Updated
- 4 days ago
- Language
- Python
- License
- MIT
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attribution-model
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.
attribution
Build practical B2B attribution models across first touch, lead creation, opportunity creation, multi-touch, account-based influence, and sales-sourced revenue. Produces model comparison, UTM governance, source-of-truth rules, and channel ROI view. Use when marketing and sales disagree on source or ROI.
attribution-report
Run multi-touch attribution analysis on real conversion-path data — applies two or more models side-by-side (first-touch, last-touch, linear, time-decay, position-based, data-driven), computes per-channel attributed revenue and ROAS, assisted-conversion ratios, path-length and time-to-conversion distributions, and budget reallocation recommendations. Triggers on "/digital-marketing-pro:attribution-report", "which channels actually drive revenue", "compare first-touch vs last-touch", "run an attribution analysis", "is paid social undervalued". Pulls journeys from Google Analytics, Google Ads, Meta, and CRM MCPs and includes GA4's AI Assistant channel; model definitions come from skills/funnel-architect/attribution-models.md, strategy design from /digital-marketing-pro:attribution-model.