生成式营销组合建模：度量 GEO 与 GEM 商业因果效应的分析框架

My Thoughts on Translating this AI Marketing Paper

Okay, so the challenge is to accurately translate this paper on generative AI in marketing into simplified Chinese. My initial assessment reveals this is squarely in the domain of marketing and data science, specifically focusing on causal inference and marketing mix modeling within the context of generative AI. The source text describes a new model, Generative Marketing Mix Modeling (GMMM), which intrigues me. I need to be precise with the terminology.

The core idea is that generative AI reshapes how companies reach customers, but traditional marketing data don't capture the visibility of a company's name in AI-generated answers. That's a critical point to convey. The paper introduces GMMM to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, the GMMM combines repeat answers, question counts, usage shares across different AI systems, and notice probabilities. For GEM, it considers sponsored placements and notice probabilities.

Essentially, I need to communicate that GMMM compares business outcomes under different intervention or treatment strategies – a common framework in causal inference – and establishes conditions to accurately identify these effects. The paper then evaluates the model’s performance using simulated product recommendations in both English and Japanese. My goal here is a translation that is technically sound and reads naturally in Chinese, capturing the essence of the original text.

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的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模型（GMMM），用于估计生成式引擎优化（GEO）和生成式引擎营销（GEM）的因果效应。针对 GEO，GMMM 将重复生成的回答与提问数量、各生成式系统的使用份额以及注意概率相结合。针对 GEM，它将赞助展示位记录与注意概率相结合。GMMM 比较了不同干预序列下的预期业务响应，并确立了识别由此产生效应的充分条件。我们使用英语和日语的产品推荐模拟回答，检验了所提出方法的实证表现。