E-GEO: A Testbed for Generative Engine Optimization in E-Commerce

• URL: http://arxiv.org/abs/2511.20867v1
• Date: Tue, 25 Nov 2025 21:28:40 GMT
• Title: E-GEO: A Testbed for Generative Engine Optimization in E-Commerce
• Authors: Puneet S. Bagga, Vivek F. Farias, Tamar Korkotashvili, Tianyi Peng, Yuhang Wu,
• Abstract summary: generative engine optimization improves content visibility and relevance for generative engines.<n>Current GEO practices are ad hoc, and their impacts remain poorly understood.<n>E-GEO is the first benchmark built specifically for e-commerce GEO.
• Score: 9.66101096555058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rise of large language models (LLMs), generative engines are becoming powerful alternatives to traditional search, reshaping retrieval tasks. In e-commerce, for instance, conversational shopping agents now guide consumers to relevant products. This shift has created the need for generative engine optimization (GEO)--improving content visibility and relevance for generative engines. Yet despite its growing importance, current GEO practices are ad hoc, and their impacts remain poorly understood, especially in e-commerce. We address this gap by introducing E-GEO, the first benchmark built specifically for e-commerce GEO. E-GEO contains over 7,000 realistic, multi-sentence consumer product queries paired with relevant listings, capturing rich intent, constraints, preferences, and shopping contexts that existing datasets largely miss. Using this benchmark, we conduct the first large-scale empirical study of e-commerce GEO, evaluating 15 common rewriting heuristics and comparing their empirical performance. To move beyond heuristics, we further formulate GEO as a tractable optimization problem and develop a lightweight iterative prompt-optimization algorithm that can significantly outperform these baselines. Surprisingly, the optimized prompts reveal a stable, domain-agnostic pattern--suggesting the existence of a "universally effective" GEO strategy. Our data and code are publicly available at https://github.com/psbagga17/E-GEO.

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