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

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 lastclick 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.

Introduction. Ask ChatGPT “what running watch should I buy?” and it answers “Garmin, Coros, or Polar.” That sentence is now a routine on-ramp to the web: the user reads three brands they did not type, and some of them go looking. The assistant has become a step in the path to commerce that no web log records. Last-click attribution sees the eventual Google search or retailer visit and credits organic channels; the antecedent, the brand the model named, is invisible. As conversational assistants—the text-based LLM chat interfaces ChatGPT, Claude, and Gemini, as distinct from voice assistants and from search-embedded AI like Google’s AI Overviews, which we exclude (§3)—absorb a growing share of information-seeking [7, 12, 23], that blind spot widens: the brand the model named pulls the user toward it along a path no log attributes to the assistant. We measure that path on a deployed measurement panel that joins opt-in pageview clickstream to the same panelists’ conversations under strict aggregation constraints, and which has run in production at Scrunch AI since early 2026: a same-name search, a visit to the brand’s own site, a brand-specific retailer-page visit (Figure 1). The headline is an acquisition-like effect.1 When the assistant recommends a brand to a user with no recent observed engagement with it, the user searches it (+4.3 pp), visits its site (+2.4 pp), and reaches it at a retailer (+1.0 pp), all over a within-user backward placebo. The design is observational and we do not observe transactions; the retailer-page visit is purchase-adjacent. Standard web analytics attributes almost none of these to the assistant. This clean number has to be separated from a confounded one, and that separation is the contribution. Pooling every mention gives an attractive but confounded funnel (+2.08/+2.37/+0.52 pp); it is not the estimand we want. The pooled lift averages genuine recommendations with incidental references to brands the user already uses (“you can use Samsung Pay,” “your Netflix download”), and for those the downstream visits are the user’s existing-customer behavior, already underway, so the pooled discovery and retail lifts are biased upward as estimates of what a mention causes. An event study makes the bias visible. The named brand’s own-site activity is already rising in the days before the response, while same-category brands the response did not name stay flat. Conditioning on noncustomers and on genuine recommendations removes this pre-trend and recovers the acquisition effect above; incidental name-drops then move behavior two to three times less, and the measurable effect concentrates among users with no recent observed engagement (among already-engaged users the mention coincides with an active episode without clearly accelerating it). A reverse path runs the other way, with existing brand-directed demand flowing into the conversation, and the same conditioning removes it so the forward prompt-to-purchase contrast becomes cleaner.

Contributions. The claim in one line: conversational assistants create unlogged brand exposures that move observably-unengaged users into open-web brand navigation. (1) A method for measuring the off-platform behavioral response to an LLM-generated brand exposure under observational constraints, and the acquisition-like effect it recovers: on a joined clickstream–conversation panel, an AI recommendation to a non-customer is followed by more same-name search, own-site discovery, and purchase-adjacent retail, almost none of it visible to last-click attribution (§4.1). (2) A decomposition that separates this effect from two confounds, incidental namedrops and existing-customer behavior (a brand-specific pre-trend), using a pre-trend event-study, non-customer conditioning, a stance classifier, and a within-response same-category control (§4.2–§4.4). (3) Practical measurement corrections and scope conditions: pathaware retail matching and platform-brand separation recover a retail effect that host-only matching reads as near-null, while heterogeneity analyses show category-specific destinations but no reliable familiarity or mention-position gradient (§4.6–§5). The framing is for customer-journey optimization: the assistant is an unlogged acquisition touchpoint at the awareness-to-acquisition boundary — it moves people who were not already in the funnel — so journey models and uplift measurement that omit it misattribute acquisition to downstream organic channels. In customer-lifecycle terms the effect sits where broad-audience brand awareness feeds first-time acquisition; the recommendation × non-customer contrast is an uplift estimand (incremental lift on the observably-unengaged), and the existing-customer analysis separates acquisition-like reach from mature-user activity.

Related work. AI-mediated web behavior. A growing body of panel work documents how conversational assistants reshape web traffic in aggregate. LLM adoption shifts the volume and concentration of where users go [12, 23], and SERP-level AI summaries depress organic click-through [7]. Closest to a commercial outcome, Kaiser and Schulze [18] measure observable ChatGPT referral traffic and transactions reaching e-commerce sites through in-answer links, and find the channel emerging but still small. Our setting is complementary: we measure brand mentions that usually generate no observable assistant referral, and show they move users through ordinary search and brand-navigation paths that last-click attribution assigns elsewhere. These are aggregate, short-run channel flows: they establish that assistants redirect traffic, but not where a specific mention sends a specific user, nor whether it moves people who were not already headed there. We fill that gap at the level of the individual exposure.

Web attribution and incrementality. The central problem of digital attribution is that the click which converts is rarely the touch that caused the conversion. Last-click analytics credits the final channel and is blind to upper-funnel exposure; multi-touch attribution spreads credit across the path [4], and the incrementality literature shows, in large field experiments, that observational credit and true causal lift diverge by an order of magnitude, with paid search and display often capturing demand that would have converted anyway [5, 13, 20]. That divergence motivated ghost-ad and intention-to-treat designs that measure the counterfactual directly [16]. Television and other advertising exposures have long been studied through downstream search, purchase, or response footprints [17, 24]. The AI mention is a new member of this family of hard-to-log upper-funnel touches, with a twist: it is not even recorded as an exposure. Our central methodological move, separating a mention’s effect from the pre-existing demand it co-occurs with, is the incrementality question in a setting where the treatment is unlogged.

The consumer search journey and omnichannel retail. Our three stages, search, own-site, and retailer, trace the recall, consideration, and purchase spine of the decision-journey literature [9, 19], and the multi-surface routing we observe (a brand site for some categories, a third-party retailer for others) is the omnichannel reality a single funnel number hides [27]. Online product recommendations move consumer choice when presented as recommendations [25]; the assistant’s mentions are different in kind, incidental and in prose, and we separate the ones that function as recommendations from those that do not.

Method. Panel and disclosure. An opt-in clickstream panel covers two English-speaking markets with pageview-level capture, joined per user at timestamp granularity to the same panelists’ conversations with ChatGPT, Claude, and Gemini. This measurement layer has run in production at Scrunch AI since early 2026, supporting reportform analyses of AI-mediated consumer journeys; the paper reports a disclosure-safe aggregate of the same deployed pipeline (§7). We do not report aggregate panel size, per-analysis user counts, or percell sample sizes; these are commercially sensitive. Effect sizes, confidence intervals, rates, and ratios are reported throughout; every headline estimate is a user-clustered bootstrap over the full eligible panel rather than a sampled subset. Every headline cell clears the platform’s minimum-disclosure threshold on distinct users, distinct brands, and user–response–brand rows by a wide margin, so no reported interval rests on a thin cell. Search-embedded surfaces (Google AI Overview, AI Mode) are excluded: their “prompt” is a search the user just typed, so a same-name search outcome would be mechanical.

Unit, outcomes, identification. The unit is a (user, response, brand) row for a curated cross-category brand lexicon (high recognition, low homograph ambiguity) spanning consumer categories; three additional single-category verticals are introduced only for the generalization test in §5. Three outcomes are measured over the seven days after a response: recall (brand recall, operationalized as a Google search whose query contains the brand—not the information-retrieval metric), discovery (a visit to the brand’s own site), and retail (a visit to a retailer page whose URL path identifies the brand). For each row we difference the outcome over (T,T+w) against the same user’s rate over same-width windows at T−14, T−21, T−28 days. Matched widths are necessary: differencing a 24h outcome against a 7-day placebo biases the contrast negative by construction, so every contrast uses windows of equal width. Formally, for an eligible exposure set E (e.g. recommendation × non-customer), the lift over window wis Cleaning the contrast. The novelty filter blocks prior search but not prior site or retail activity, so it does not by itself remove existing-customer behavior; the controls that do are introduced with the results they support (§4.1–§4.4). We classify each mention’s stance toward the named brand (recommend / neutral / caution) with a small language model, and define a non-customer of a brand as a user with no recent observed engagement: no prior search, own-site, or retail pageview in the prior window. This is a behavioral definition, not a commercial one. Such a user may still own the product, buy offline, or engage through channels the panel does not capture. We call such a user a non-customer for readability, but the estimand is precisely a user with no recent observed brand engagement (an observably-unengaged user), and we read the effect as acquisition-like (§8). Our cleanest observational contrast is a recommendation to such a user.

Discussion. Implications for practice. First, last-click tooling misattributes the whole prompt-to-purchase path to organic search and “direct” retailer visits; an honest accounting of an assistant’s commercial role has to instrument the mention. Second, the assistant’s measurable effect is acquisition-like: it concentrates among users with no recent observed brand engagement, while among already-engaged users it coincides with an active episode without clearly adding acceleration, so its established role is reach. Third, a recommendation moves two to three times more than an incidental name-drop, and the carrying channel is category-specific, so measurement must track both own-site and retailer surfaces. We report these as measurement implications, not as advice to manufacture mentions. Translating these purchase-adjacent visits into lifetime value or acquisition cost is the natural next step for practice. Pairing the reach we measure with downstream conversion and retention data would let an advertiser price assistant-driven exposure against existing channels, turning the incrementality estimate here into a bid-level uplift signal; the transaction joins this requires are left to future work.

Conclusion. Conversational assistants create unlogged brand exposures that move observably-unengaged users into open-web brand navigation. The path from prompt to commerce runs through the open web, and most of it is unlogged. Once the existing-customer episodes and incidental name-drops are separated out, the measurable pattern is acquisition-like reach: the assistant moves observably-unengaged users to search, to the brand’s site, and to a retailer, and last-click analytics attributes almost none of it to the assistant.

Limitations. What we do not claim. The panel observes web navigation, not attention: a user may read a recommendation and never search, search in a channel we do not see, act inside an app, buy offline, or move outside the seven-day window, so the rates are a lower bound on influence. “Non-customer” means no recent observed engagement, not the absence of any brand relationship. We do not observe transactions; retail is a purchase-adjacent product-page visit, and “purchase” refers to that funnel direction, not a logged sale. The design is observational. Our defenses against confounding are layered: the same-category control (category intent and activity surges), the pre-trend event-study and non-customer conditioning (existing-customer behavior), the stance split, and the no-priorsame-category stratum (§4.2). None fully rules out a brand-specific intent shock that, in the same session, both prompts a recommendation and drives action. The same-category stratum narrows it sharply, since the effect holds for users with no observed prior samecategory lexicon-brand engagement, yet a within-session shock specific to the named brand remains unidentifiable on observational data; a randomized or encouragement design would be needed to close it.