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

TL;DR

This paper introduces E-GEO, a benchmark for evaluating and optimizing generative engine strategies in e-commerce, revealing a stable, domain-agnostic approach that improves content relevance and visibility.

Contribution

It presents the first e-commerce-specific GEO benchmark, conducts a large-scale empirical evaluation, and proposes an optimization algorithm that uncovers a universal GEO strategy.

Findings

Optimized prompts significantly outperform heuristics.

A stable, domain-agnostic GEO pattern exists.

E-GEO dataset captures rich shopping intent and context.

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.