The methodology of connecting an AI engine citation or AI-referred session to a downstream business outcome (sign-up, opportunity, closed-won revenue).
AI search attribution extends traditional marketing attribution to the AI-engine layer. Traditional attribution models (last-touch, first-touch, multi-touch) work on click-through traffic with clean referrer data. AI search attribution must additionally handle (a) view-through impact when users see an AI answer but don't click, (b) referrer signal loss when AI engines strip headers, (c) cross-session brand recall over weeks or months. The category-wide problem: 73% of brands running AEO programs cannot connect AI citations to revenue today (Conductor State of AEO/GEO 2026).
Without AI search attribution, AEO investment cannot be defended to a CFO. Brands renew AEO contracts on faith, kill them on vibes, or — worst case — get asked by their CEO 'did this work?' and have no answer. Attribution is the bridge from 'we're tracking visibility' to 'we're proving value.'
Lantern's attribution methodology: an AI-referred GA4 session is matched to a HubSpot contact via cookie/email. The contact progresses through deal stages over a 90-day lookback window. When closed-won, the original AI citation receives partial attribution credit (multi-touch model) for the deal value. The monthly ROI report aggregates this across all citations.
The terms in this glossary aren't theoretical — they're what Lantern's product calculates and reports every month for B2B SaaS teams. See yours in 7 days. 14-day free trial.
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