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.

翻译：当对话助手向近期无观测互动的用户推荐某品牌时，该用户对同一品牌的谷歌搜索量提升+4.3个百分点（pp）[3.1, 5.5]，品牌官网访问量提升+2.4 pp [1.4, 3.5]，品牌特定零售商页面访问量提升+1.0 pp [0.3, 1.7]，相较于匹配的安慰剂对照期。该估计值的恢复正是本研究的工作。推荐行为创造了任何网络日志均无法归因于助手的品牌曝光，而看似衡量该效应的朴素全推荐漏斗存在混淆：许多推荐是用户已使用品牌的附带提及（例如“你的Netflix下载”），其后续访问是该现有客户自身行为，并表现为品牌特有的前趋势。我们通过一个面板测量了站外响应，该面板将选择加入的用户点击流数据与其ChatGPT、Claude和Gemini对话记录相连接，并通过前趋势事件研究、立场分类器、非客户条件限制以及同一响应内的同类商品控制来分离效应：附带提及对行为的影响显著较小（+1.8/+1.1/+0.3），而被提名品牌的行为变化远超同一响应中未被提名的同类品牌。下游路径主要通过搜索中介，同时到达品牌官网和零售商页面，其目的地组合追踪了基线品牌导向行为，而非转向任一渠道。该设计为观察性研究，且我们未观察到交易数据，因此零售行为仅与购买相邻。基于引荐来源和最后一次点击的标准测量会遗漏这一上游曝光：助手将未观测互动的用户引向开放网络上的品牌导航，且该路径被归因于其他来源。