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

2025-11-25

AI Summary

Generative engines are supplanting traditional search in e-commerce retrieval, yet generative engine optimization (GEO) lacks systematic investigation and standardized benchmarks. Method: We introduce E-GEO, the first dedicated GEO benchmark for e-commerce, comprising over 7,000 real-world multi-sentence queries paired with corresponding product lists. We formally formulate GEO as a tractable optimization problem—departing from heuristic-based approaches—and propose a lightweight iterative prompt optimization algorithm. Leveraging large-scale human-constructed data and empirical analysis, we identify robust, cross-domain effective prompt patterns. Contribution/Results: Our method achieves statistically significant improvements over 15 diverse baseline strategies across multiple evaluation metrics. To foster reproducibility and community advancement, we publicly release both the benchmark dataset and implementation code.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Prompt Engineering / Prompting
• Constraint Satisfaction and Optimization: Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Addressing the lack of systematic optimization methods for generative engine visibility in e-commerce
• Evaluating empirical performance of 15 common rewriting heuristics for e-commerce GEO
• Developing lightweight prompt-optimization algorithms to outperform existing GEO baselines

Innovation

• E-GEO benchmark for e-commerce GEO optimization
• Evaluated 15 rewriting heuristics in empirical study
• Developed lightweight iterative prompt-optimization algorithm