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

Puneet S. Bagga, Vivek F. Farias, Tamar Korkotashvili, Tianyi Peng, Yuhang Wu

TL;DR

The paper introduces E-GEO, the first e-commerce GEO benchmark with over 7,000 rich product queries and 52,000+ product listings, enabling the empirical study of GEO in a practical setting. It systematically compares 15 heuristic rewriting strategies and demonstrates that a lightweight prompt-meta-optimization approach can outperform these baselines, revealing a stable, potentially universal rewriting pattern. The work frames GEO as a tractable optimization problem within a retrieval-augmented generation pipeline and shows that optimization-driven rewriting translates into measurable ranking gains with economic significance. It also highlights directions for future research, including equilibrium effects, multi-modal extensions, and platform-level design considerations around GEO adoption.

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.

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

TL;DR

The paper introduces E-GEO, the first e-commerce GEO benchmark with over 7,000 rich product queries and 52,000+ product listings, enabling the empirical study of GEO in a practical setting. It systematically compares 15 heuristic rewriting strategies and demonstrates that a lightweight prompt-meta-optimization approach can outperform these baselines, revealing a stable, potentially universal rewriting pattern. The work frames GEO as a tractable optimization problem within a retrieval-augmented generation pipeline and shows that optimization-driven rewriting translates into measurable ranking gains with economic significance. It also highlights directions for future research, including equilibrium effects, multi-modal extensions, and platform-level design considerations around GEO adoption.

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.

Paper Structure

This paper contains 23 sections, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Review
  3. GEO in E-Commerce
  4. Generative Engine
  5. The GEO Process
  6. Dataset: E-GEO
  7. Queries
  8. Products
  9. Experiments
  10. Implementation Details
  11. Generative Engine.
  12. GEO Module.
  13. Evaluation Metric.
  14. Prompt Meta-Optimization
  15. Initial Prompts and Performance
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Generative Engine in E-Commerce.
  • Figure 2: The GEO process. A GEO module rewrites product descriptions to enhance placement in generative-engine rankings.
  • Figure 3: Feature presence heatmaps: Initial (left) vs. Optimized (right) prompts. Red indicates absence; green indicates presence. Optimized prompts consistently incorporate key features such as ranking emphasis, user intent alignment, competitiveness, and external evidence, suggesting a universally effective rewriting strategy. Notably, most optimized prompts maintain the factuality requirement present in the initial prompts, indicating that the meta-optimization process reliably identifies factuality as an important feature.