attribution-report
starsforks
How to install attribution-report?
indranilbanerjee/digital-marketing-pro/attribution-report
$npx -y skills add indranilbanerjee/digital-marketing-pro --skill attribution-reportRun multi-touch attribution analysis. Use when: first/last-touch, linear, time-decay, position-based revenue allocation.
SKILL.md
| 1 | # /digital-marketing-pro:attribution-report |
| 2 | |
| 3 | ## GA4 AI Assistant channel (added 13 May 2026) |
| 4 | |
| 5 | When generating attribution reports against a GA4 property, the **AI Assistant** default channel group is now a first-class channel. GA4 automatically categorizes sessions referred by ChatGPT, Gemini, Claude, and other recognized AI assistants under this channel (and sets `Medium=ai-assistant`). For any brand running an AEO program, include the AI Assistant channel in the channel set and compare its contribution across all attribution models (first-touch, last-touch, linear, time-decay, position-based, data-driven). |
| 6 | |
| 7 | The model-comparison view is especially informative here: AI Assistant traffic often shows wildly different credit under first-touch vs last-touch because users frequently *discover* a brand via an AI assistant but convert via a later branded search or direct visit. Don't conclude "AI search doesn't drive revenue" from a last-touch number alone. |
| 8 | |
| 9 | Source: [GA4 default channel groups](https://support.google.com/analytics/answer/9164320?hl=en). For the upstream impression-side data, pair with `/digital-marketing-pro:gsc-ai-performance` (GSC AI Performance Report rolled out 3 June 2026, deliberately no click data — so GA4 is your click attribution surface). |
| 10 | |
| 11 | ## Purpose |
| 12 | |
| 13 | Generate multi-touch attribution analysis showing how different marketing channels and campaigns contribute to conversions. Compare multiple attribution models side-by-side, allocate revenue across touchpoints, and provide actionable budget reallocation recommendations based on true channel contribution. This command moves beyond simplistic last-click attribution to reveal the full customer journey — identifying which channels drive awareness, which nurture consideration, and which close conversions — so marketing budgets can be allocated based on actual contribution rather than positional bias. |
| 14 | |
| 15 | ## Input Required |
| 16 | |
| 17 | The user must provide (or will be prompted for): |
| 18 | |
| 19 | - **Attribution models to compare**: Two or more models to run side-by-side — `first-touch` (100% credit to the first interaction that initiated the journey), `last-touch` (100% credit to the final interaction before conversion), `linear` (equal credit distributed across all touchpoints), `time-decay` (exponentially more credit to touchpoints closer to conversion, with configurable half-life — default 7 days), `position-based` (40% to first touch, 40% to last touch, 20% distributed across middle interactions), or `data-driven` (algorithmic allocation based on conversion path patterns and counterfactual analysis). At least two models should be compared to reveal attribution bias |
| 20 | - **Conversion events to attribute**: The conversion actions to analyze — `purchases` (completed transactions with revenue), `signups` (account or trial creation), `leads` (form submissions, demo requests, contact inquiries), or `custom events` (user-defined conversion points with optional revenue values). Multiple conversion events can be analyzed simultaneously with separate attribution for each |
| 21 | - **Time period**: The analysis window — specific date range, relative period (last 30 days, last quarter), or year-over-year comparison. Longer periods provide more conversion paths for reliable model comparison but may include seasonal distortions |
| 22 | - **Conversion window**: The lookback window for attributing touchpoints to a conversion — `7 days` (short-cycle purchases, impulse buys), `14 days` (standard eCommerce), `30 days` (B2B lead gen, considered purchases), or `90 days` (enterprise B2B, high-value purchases with long sales cycles). Touchpoints outside the conversion window are excluded from attribution |
| 23 | - **Channels to include**: Which marketing channels to attribute across — paid search, paid social, organic search, direct, email, referral, display, video, affiliate, or specific campaign groups. All channels are included by default unless the user restricts scope |
| 24 | |
| 25 | ## Process |
| 26 | |
| 27 | 1. **Load brand context**: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply business model context (SaaS, eCommerce, B2B) to set appropriate default conversion window and model recommendations. Check for guidelines at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults. |
| 28 | 2. **Gather conversion path data from analytics MCPs**: Pull multi-touch journey data from connected sources — Google Analytics MCP for conversion paths, multi-channel funnel reports, and assisted conversion data; Google Ads MCP for search attribution reports and cross-network attribution; Meta MCP for view-through and click-through attribution data; CRM MCP for deal stage progression with ma |
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