发表机构
Aiso Boost Ltd.（Aiso Boost有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究审计真实AI对话日志，发现推荐内容可观察性远高于买家后续决策，揭示测量局限性，而非说服率估计。
AI 中文摘要
生成式助手多久会说服某人购买，或说服其不购买？对话日志包含推荐内容，但不一定记录后续决策。我们审计了来自Aiso专有研究数据库的317条历史交互记录，这些记录基于许可、同意且去标识化的与商用AI助手的对话。单智能体AI辅助筛查识别出68条购买导向记录；合并一个共享前缀副本后，保留67条事件，日期从2023年4月至2025年7月。助手回复在52条事件（77.6%）中提供候选选项、获取渠道或条件性偏好。一条事件包含对指定住宿候选的条件性重定向。没有事件被编码为建议放弃或推迟购买类别。仅18条事件（26.9%）在相同购买相关任务中包含后续用户回合，而23条（34.3%）包含任何后续用户回合。因此，仅使用对话深度会高估此后续可用性27.8%。在47条保留的用户后续消息中，未观察到明确的建议后购买承诺、已完成购买报告或购买类别放弃声明。这些零值描述的是记录的陈述，而非转化率或说服率。本文提供了操作性定义、无文本注释和可复现的描述性结果。其核心发现是一个测量局限性：推荐内容比买家的后续决策更常可观察。所选历史样本、未验证的AI注释和缺失的交易结果不支持对说服力的总体或因果估计。
英文摘要
How often does a generative assistant persuade someone to buy, or persuade them not to buy? Conversation logs contain recommendations, but they do not necessarily record subsequent decisions. We audit 317 historical interactions from Aiso's proprietary research database of licensed, consent-based, de-identified conversations with commercially available AI assistants. Single-agent AI-assisted screening identifies 68 purchase-directed records; collapsing one shared-prefix copy yields 67 retained episodes, dated April 2023 to July 2025. Assistant responses provide candidate options, acquisition channels, or conditional preferences in 52 episodes (77.6%). One episode contains conditional redirection away from a named accommodation candidate. No episode is coded as advice to abandon or defer the purchase category. Only 18 episodes (26.9%) contain a subsequent user turn within the same purchase-related mission, compared with 23 (34.3%) that contain any later user turn. Using conversation depth alone therefore overstates this follow-up availability by 27.8%. Across 47 retained user follow-up messages, no explicit post-advice purchase commitment, completed-purchase report, or purchase-category abandonment statement is observed. These zeros describe recorded statements, not conversion or persuasion rates. The paper supplies operational definitions, text-free annotations, and reproducible descriptive results. Its central finding is a measurement limitation: recommendation content is observable much more often than a buyer's subsequent decision. The selected historical sample, unvalidated AI annotations, and missing transaction outcomes do not support a population-level or causal estimate of persuasion.
Comments16 pages, 6 figures, 4 tables. Exploratory observational audit using Aiso's proprietary research database; text-free annotations and reproducibility code included as ancillary files
