生成式营销组合建模：将GEO与GEM关联到业务影响的因果推断框架

中文摘要 AI 辅助

生成式人工智能改变了企业触达客户的方式，但标准营销数据并未记录用户在生成式回答中看到并注意到企业名称的频率。我们开发了生成式营销组合建模（GMMM），以估计生成式引擎优化（GEO）和生成式引擎营销（GEM）的因果效应。对于GEO，GMMM将重复生成的回答与问题数量、各生成系统中的使用份额以及注意概率相结合。对于GEM，它将赞助展示记录与注意概率相结合。GMMM比较了在替代处理序列下的预期业务响应，并建立了识别由此产生的效应的充分条件。我们使用英语和日语的产品推荐模拟回答来研究所提出方法的实证性能。

英文摘要

Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares of use across generative systems, and notice probabilities. For GEM, it combines records of sponsored placements with notice probabilities. GMMM compares expected business responses under alternative treatment sequences and establishes sufficient conditions for identifying the resulting effects. We investigate the empirical performance of the proposed method using simulated answers to product recommendation in English and Japanese.

发表机构

• The University of Tokyo（东京大学）
• Mizuho-DL Financial Technology Co., Ltd.（瑞穗-DL金融科技有限公司）
• NP-hard

机构由 AI 辅助整理，请以论文原文为准。