attribution-report

# /digital-marketing-pro:attribution-report

## GA4 AI Assistant channel (added 13 May 2026)

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).

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.

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).

## Purpose

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.

## Input Required

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

- **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
- **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
- **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
- **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
- **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

## 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 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.
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 marketing touchpoint timestamps. Merge touchpoints into unified customer journeys, deduplicating cross-platform overlap where the same interaction is recorded by multiple sources.
3. **Apply each selected attribution model to the data**: Run every requested model against the unified conversion path dataset. First-touch: assign 100% of conversion value to the first recorded touchpoint in each journey. Last-touch: assign 100% to the final touchpoint before conversion. Linear: divide conversion value equally among all touchpoints (n touchpoints each receive 1/n credit). Time-decay: apply exponential decay from conversion backward with the configured half-life — a touchpoint at one half-life distance receives 50% of the credit of the converting touchpoint, two half-lives receives 25%, and so on, then normalize to 100%. Position-based: assign 40% to first, 40% to last, distribute remaining 20% equally across middle touchpoints. Data-driven: analyze conversion path patterns to identify which channel sequences have statistically higher conversion rates, then allocate credit proportional to each channel's incremental contribution.
4. **Calculate per-channel rev