From Prompt to Purchase: How AI Brand Recommendations Move Consumers on the Open Web

Abstract

When a conversational assistant recommends a brand to a user with no recent observed engagement, that user's same-name Google search rises $+4.3$ percentage points (pp) [$3.1$, $5.5$], visits to the brand's own site $+2.4$ pp [$1.4$, $3.5$], and brand-specific retailer-page visits $+1.0$ pp [$0.3$, $1.7$] over matched backward placebos. Recovering that estimate is the work. The mention creates a brand exposure no web log attributes to the assistant, and the naive all-mention funnel that seems to measure it is confounded: many mentions are incidental references to brands the user already uses ("your Netflix download"), whose downstream visits are that existing customer's own behavior and surface as a brand-specific pre-trend. We measure off-platform response on a panel that joins opt-in clickstream to the same users' ChatGPT, Claude, and Gemini conversations, and isolate the effect with a pre-trend event study, a stance classifier, non-customer conditioning, and a within-response same-category control: incidental name-drops then move behavior far less ($+1.8/+1.1/+0.3$), and the named brand moves far more than unnamed same-category brands in the same response. The downstream path is mostly search-mediated and reaches both own sites and retailer pages, with a destination mix that tracks baseline brand-directed behavior rather than redirecting toward either. The design is observational and we do not observe transactions, so retail is purchase-adjacent. Standard referrer-based and last-click measurement miss this upstream exposure: assistants move observably-unengaged users into open-web brand navigation along a path attributed elsewhere -- an acquisition touchpoint at the head of the customer journey that journey models and last-click attribution do not see.

10 pages, 4 figures, 9 tables