Rethinking Brand Visibility in the Age of ChatGPT
Nurul Juhria
Senior Associate
The rise of large language models (LLMs) like ChatGPT and Gemini is revolutionising how content is created and discovered, changing research habits and search visibility dynamics.
More users now begin their searches using LLM interfaces rather than traditional search engines like Google. While Google continues to dominate overall search volumes, LLM adoption is rapidly accelerating, with millions engaging daily. This shift demands that brands evolve beyond standard SEO and incorporate Generative Engine Optimisation (GEO), a newer strategy crafted to enhance content visibility specifically for LLMs.
What’s the difference?
Unlike traditional Google search, LLMs don’t “search the web” in real-time. Instead, they generate responses based on patterns learned from massive datasets and training over a wide range of text sources. Therefore, brands must ensure their authoritative content is well-represented and cited within key reference sources (e.g. Wikipedia, respected blogs, forums, and review sites) that shape the datasets underpinning LLM knowledge. These citations signal trustworthiness and authority to LLMs, increasing the likelihood of content being surfaced.Following her content helps me stay updated on the latest partnerships and narratives in the F1 world without the need to track every driver individually. More importantly, the comment sections are a gold mine of unfiltered audience sentiment and in-depth discussions, which is an invaluable reminder of how consumers respond in real time.
What does this mean?
Traditional SEO elements like Bing Webmaster Tools (important since Bing powers much of ChatGPT’s search), schema markup, fast load speeds, and structured, conversational content remain vital. However, GEO focuses further on ultra-specific, location- or segment-targeted content, enabling LLMs to align results with precise user intent. Programmatic SEO methods can efficiently scale this by creating customised pages that match nuanced queries.
Content performance on LLMs depends on how directly and authoritatively it resolves a query. GEO content must be comprehensive, expert-authored, and regularly refreshed. Unlike keyword-heavy SEO strategies that optimise for link clicks, GEO clarity, structure, and completeness (a.k.a. fully answering user questions). The goal is not just ranking but providing the most relevant and trustworthy answer possible. Detailed case studies, unique comparative analyses, and reviews all boost content’s AI ranking potential.
External citations are a must. LLMs value authority and trust signals over raw backlinks or keyword density. High-quality endorsements from reputable sources vastly improve content positioning within AI-generated responses.
Transitioning from Google-centric to LLM-centric research means integrating SEO basics with GEO strategies, authoritative content, structured data, and proactive brand citation. This approach transforms copywriting into authority-building, creating a digital ecosystem that LLMs learn from and trust. Brands mastering LLM optimisation will gain competitive edge, shaping online influence in an era where AI mediates discovery and engagement.
How does it look like applied?
