I've built and scaled a marketing agency by betting on what's next. Let me tell you what's next: your board is going to ask why ChatGPT is recommending your competitor, and your marketing director won't have an answer.
They'll blame the algorithm. They'll say it's a black box. They are wrong. It's not a black box; it's a new rulebook. And your team is still playing the old game.
People don't 'search' on ChatGPT or other generative Large Language Models (LLMs). They ask, they converse, they prompt. They seek definitive answers, not a list of ten blue links. The psychological posture is entirely different. Search Engine Optimisation (SEO) is predicated on ranking in a list of options. Generative Engine Optimisation (GEO) is about becoming the single, synthesised, authoritative answer.
This is a fundamental platform shift. Trying to 'do SEO' for ChatGPT is like trying to optimise your MySpace profile for TikTok. The mechanics, the signals, the entire philosophy is redundant. The game is no longer about winning a click. The game is about embedding your brand's facts, data, and perspective into the foundational knowledge of the internet. It's about becoming a 'ground truth' source upon which the AI draws its conclusions.
At Creative Marketing Group, we're not hedging our bets. We are going all-in on an agentic future. This isn't a theory; it's our entire operational strategy. And it starts with GEO.
Forget everything you think you know about ranking. What follows is not a list of tactics. It is a strategic framework for rewiring your brand's digital presence for an AI-native world. It's how you move from being a page in the index to a fact in the knowledge base.
Keywords are dead. The new unit of currency is the 'entity'. Stop thinking about 'personal finance tips' and start thinking about the entire conceptual universe your brand operates in. For a fintech brand, this includes entities like 'Interest Rates', 'ISAs', 'Credit Scores', and 'Mortgage Affordability'.
Your job is to map this entire semantic field. What are all the objects, concepts, people, and relationships that constitute your domain? You are not optimising for strings of text; you are optimising for the AI's understanding of your reality. This is the foundational layer. Get it wrong, and nothing else matters.
Once you have your domain, you must map it. A Knowledge Graph is a model of a knowledge domain, representing entities and the relationships between them. Google's is the most famous, with over 500 billion facts on 5 billion entities. You need to build your own.
Start by identifying the core entities in your domain. Then, define their attributes (e.g., entity: 'Kasim Javed', attribute: 'founder of', value: 'Creative Marketing Group'). Then, crucially, define the relationships between them. Use tools like Miro or, for more advanced applications, graph database software like Neo4j to visualise this. This map becomes your strategic guide for every piece of data you create.
3. Identify & Influence 'Ground Truth' Sources
LLMs are trained on vast datasets: Common Crawl, Wikipedia, academic papers, patents, government data, and industry reports. Your goal is to identify the most authoritative 'ground truth' sources in your specific domain and systematically get your data and perspective included.