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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18980486 |
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| _version_ | 1866901858586460160 |
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| author | Bhatia, Ansh |
| author_facet | Bhatia, Ansh |
| contents | <p>The transition from traditional Search Engine Optimization (SEO) to Generative Engine Optimization (GEO) marks a fundamental shift in how digital information is retrieved and synthesized. As Large Language Models (LLMs) and conversational search engines like ChatGPT, Google Gemini, and Perplexity replace traditional lexical search mechanisms, the criteria for brand visibility have evolved. This paper presents an empirical analysis of 50 high-intent Business-to-Business (B2B) search queries across three leading generative search engines. We evaluate the key ranking factors—specifically Entity Consistency, High-Authority Citations, and JSON-LD Structured Data—that determine whether a brand is cited as a recommended solution. Our findings indicate a stark departure from traditional keyword density metrics, prioritizing semantic clustering and multimodal authority. Recommendations for establishing a "Dual-Search Strategy" are provided to future-proof digital presence in an AI-first search landscape.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18980486 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Generative Engine Optimization (GEO): An Empirical Analysis of Brand Citation Signals in Large Language Model Search Results Bhatia, Ansh <p>The transition from traditional Search Engine Optimization (SEO) to Generative Engine Optimization (GEO) marks a fundamental shift in how digital information is retrieved and synthesized. As Large Language Models (LLMs) and conversational search engines like ChatGPT, Google Gemini, and Perplexity replace traditional lexical search mechanisms, the criteria for brand visibility have evolved. This paper presents an empirical analysis of 50 high-intent Business-to-Business (B2B) search queries across three leading generative search engines. We evaluate the key ranking factors—specifically Entity Consistency, High-Authority Citations, and JSON-LD Structured Data—that determine whether a brand is cited as a recommended solution. Our findings indicate a stark departure from traditional keyword density metrics, prioritizing semantic clustering and multimodal authority. Recommendations for establishing a "Dual-Search Strategy" are provided to future-proof digital presence in an AI-first search landscape.</p> |
| title | Generative Engine Optimization (GEO): An Empirical Analysis of Brand Citation Signals in Large Language Model Search Results |
| url | https://doi.org/10.5281/zenodo.18980486 |