GEO

GEO stands for Generative Engine Optimization. It is the practice of optimising content so that it is surfaced, cited, and referenced within generative AI search results. Unlike traditional SEO that aims to rank web pages on search engine result pages, GEO focuses on ensuring that generative models select a business’s content as part of their synthesised answers.

For example, when a user asks a generative search engine about "best accounting software," GEO strategies help ensure that your product is mentioned in the AI’s response, even if your page is not ranked first on the SERP.

Advanced

GEO relies on understanding how generative search engines such as Google’s Search Generative Experience (SGE), Bing Copilot, or ChatGPT-powered search tools evaluate and cite information. These systems prioritise semantically rich, authoritative, and structured content.

Advanced GEO practices include using schema markup to provide machine-readable context, building topical authority with content clusters, and maintaining E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals across sites. Long-form, conversational content aligned with natural language queries is also more likely to be referenced by generative models. Unlike traditional SEO, GEO must anticipate how engines summarise, paraphrase, and synthesise information rather than just how they rank links.

Relevance

• Ensures visibility in AI-generated search results where fewer citations appear.
• Builds brand authority by being included in trusted AI summaries.
• Future-proofs content strategy as generative AI transforms how people search.
• Creates opportunities to capture high-intent traffic without relying only on SERPs.

Applications

• Structuring product content for inclusion in generative shopping recommendations.
• Publishing FAQ-rich blog posts that align with conversational queries.
• Enhancing thought leadership content for citation in AI-generated summaries.
• Using schema markup to improve content discoverability by AI engines.

Metrics

• Frequency of brand mentions in generative search outputs.
• Referrals and engagement from AI-driven search experiences.
• Share of voice compared to competitors in generative engines.
• Increases in brand trust linked to AI-cited content.

Issues

• Lack of transparency in how generative engines choose sources.
• Risk of content being paraphrased without driving traffic back.
• Difficulty measuring direct attribution from generative search results.
• Rapidly evolving algorithms and uncertain long-term standards.

Example

A health brand publishes authoritative, medically reviewed articles with structured data. When a generative engine answers a query about "natural remedies for stress," the brand is cited in the AI summary. This exposure increases trust, organic traffic, and conversions.