How Our Clients Did Last Month
Month-over-month traffic trends. Source: Google Search Console
Table of Contents
Key takeaways:
- AI-driven search engines are focused on solving problems, and less so on keyword matching.
- Intent matters more than keywords in GEO , so understanding user needs is crucial.
- GEO keywords are natural, question-based, and conversational.
- GEO works best when layered on top of solid SEO foundations.
What's Generative Engine Optimisation (GEO) and Why Does it Matter?
Known by many other names—like large language model optimisation (LLMO) and answer engine optimisation (AEO)—GEO is how we create content that gets seen on AI-powered search engines like Google Search Generative Experience, ChatGPT, Copilot, Perplexity, etc. Like traditional search engine optimisation, the goal of GEO is to increase your brand’s search visibility, either through mentions or citations.
Why should we care about GEO? Because with its ability to directly answer user queries, all signs point to AI becoming a major disruptor in the landscape of traditional search. Of course, that isn’t to say that SEO is going anywhere. In fact, you should be bolting your GEO strategies onto your existing SEO strategies (because they still work) rather than replacing one with the other.Â
What Is a GEO Keyword and Intent Strategy?
A GEO keyword and intent strategy involves discovering the user intent behind a query and finding relevant keywords that are natural, conversational, and often framed around a question. GEO differs from traditional SEO by prioritising content relevance and user intent tailored for AI engines, rather than focusing solely on keyword optimisation and SERP rankings.
This approach not only enhances user experience but also aligns more closely with modern search engine algorithms.
How Does GEO Keyword Research Differ from SEO?
SEO Keyword Research
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Takes a query-first approach
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Ranks in search engines
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Arranges similar keywords into clusters
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Optimised for web crawlers
GEO Keyword Research
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Takes an intent-first approach
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Used in AI-generated answers
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Creates a map of semantic intent
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Optimised for LLMs and user intent
What Is Search Intent?
Understanding search intent helps you create content that’s relevant to the search query. It can be broken down into four categories:
Categorising user intent this way works for Google, Bing, and other traditional keyword-based search engines. However, AI engines require you to dig a bit deeper to understand user motivation patterns, and therefore deliver more relevant and comprehensive content.Â
Generative AI uses a combination of sophisticated technology to better understand the real intent behind a query:
How Do Large Language Models Take Search Intent Further?
ContextÂ
Context is everything. AI tools consider contextual factors about the user in order to deliver the most relevant content.
For example, take location: if you live in Kugluktuk, Nunavut and search ‘winter boots’, your AI search engine will probably recommend boot brands designed for extreme weather conditions and the arctic cold while foregoing boot brands that are suitable for warmer climates.
Other factors that influence context are time of day, the device you’re using, and your search history (yes, sorry, we know that privacy is an issue).
Natural Language Processing (NLP) and Semantics
NLP helps GenAI understand the nuances of human language. It can analyse sentence structure, context, and the relationships between words in a search. Specific NLP techniques help it to recognise entities (usually proper nouns), sentiment, and main themes—all of which deepen AI’s understanding of user intent.
Similarly, semantic analysis looks at the relationships between entities and the concepts behind them, even if the word being used can have a myriad of meanings. That’s what allows an AI tool to understand that a search for ‘best cream for crow’s feet’, probably doesn’t mean you’re looking for a product to moisturise bird claws.
Behavioural Cues
Not to ring the alarm bells, but AI does use your past online behaviour to help enhance its understanding of your real intent.
By looking at your behaviour, your favourite AI engine gets a better picture of your preferences, needs, and interests.
It will assess behaviour such as the length of time you spent on a page, which links you clicked on, and which pages you left immediately.
What Does All This Have to Do With Creating a Content Strategy
Simply put, all these contextual factors, data, and NLP techniques culminate in a keyword strategy that focuses not on a single keyword, but on delivering a comprehensive, helpful, contextually rich, and easy-to-understand answer.Â
So, when a user asks, ‘What’s the average cost of living in Madrid?’, GenAI doesn’t just provide the answer at face value. It will also consider things like,Â
- Is this user moving from abroad?Â
- Are they a single-income household?Â
- Are they comparing Madrid to other Spanish cities?
How to Develop a Keyword Strategy Based on AI Search Intent
Similar to traditional SEO, you’ll need to do some background research on your ideal customer to better understand their needs, wants, goals, and pain points. But your prospects aren’t typing high-volume keywords into their favourite AI tool. Instead, they’re starting conversations with a question.
You don’t need to sit there and guess the most semantically related queries or relevant long-tail keywords. Keyword research tools like Semrush, Answer the Public, and Google’s People Also Ask feature can streamline and accelerate the keyword research process. This opens your eyes to how your clients are searching for things on these platforms.
AI-powered research tools can analyse an eye-watering amount of data, so why not capitalise on these platforms to help you identify industry trends, anticipate user queries, and suggest content topics?
Do you know what your client really wants?
When developing a GEO-focused keyword and intent strategy, paying attention is an essential skill. How are they actually talking about their problems (the kind that your business can solve, of course).
In practice, this sometimes looks like stepping out from behind your keyword tools and doing a little more fieldwork: investigating Reddit threads, reading reviews, going over social posts, and even asking your team for sales transcripts.
The goal of this investigative work is to dig a little deeper. You want to learn how your audience is asking their questions, because that understanding will ultimately help you optimise your content for AI visibility.
If your content mirrors the language of your target audience, it’s far more likely to be surfaced in AI-generated answers.
In traditional SEO, closely related keywords are grouped into clusters (e.g. ‘best stand mixer,’ ‘top stand mixers for amateur bakers,’ ‘stand mixer comparison’).Â
But in GEO, it makes more sense to group together semantic intent—the more contextual meaning behind what your audience is trying to figure out.Â
Lucky for you, you can recruit those very same GenAI tools to help you conduct what experts call the fan-out methodology. This approach breaks up a single query into multiple mini-queries, each of which explore different possible angles of potential user intent.
For example: when you plug a question you lifted from a transcript into ChatGPT, such as ‘How long will it take to reverse soil erosion after planting vetiver crops?’
And then ask, ‘What are five natural follow-up questions someone might ask after this?’, you might get:
- What’s the best way to plant vetiver for erosion control on a slope?
- Can vetiver be combined with other plants or techniques to speed up erosion reversal?
- How do I know if the soil is actually recovering or just stabilising?
- Does vetiver work in dry or semi-arid climates with minimal rainfall?
- How much maintenance does vetiver require after planting?
These follow-up questions help you simulate the fanning out path of intent. This mirrors the path an AI engine follows when answering questions, and it’s a method you can use to help you write well-rounded pages.
LLMs are taught to pick up on the subtle complexities of human speech, so your conversational queries should sound just as if a real person is talking. People aren’t typing in keywords the way they do in Google; instead, they’re phrasing questions as they would when speaking to another person. Â
Instead of building content around an SEO keyword like ‘best stand mixer UK 2025′, your GEO keyword strategy will encourage you to focus on real questions, i.e.:
- ‘What’s the top-rated stand mixer in the UK this year?’
- ‘Which stand mixer should I get as an amateur baker in the UK?’
These kinds of long-form, conversational queries are exactly what large language models are trained to recognise.Â
You Have Your Conversational Keywords. Now What Do You Do?
Now it’s time to write your content so that it’s easily understood by AI search engines and readers alike. As a bonus, when you optimise your content for GEO, you’re also bolstering your SEO efforts.
Best practices:
- Use short, concise paragraphs wherever possible.
- Use conversational and query-based headers and sub-headers.
- If you ask a question in a header, answer it in the first sentence below.
- Make every content section, or ‘chunk’, a self-contained unit, and be sure to place the most important information at the top.
- In larger sections, facilitate understanding by summarising the key points below the headers. This can be accomplished in a single italicised sentence or with bullet points.
- Use schema markup to make it even easier for AI crawlers to understand your content
Keyword & Intent Strategy Checklist
Use this checklist to ensure your GEO keyword strategy is aligned with how users search and how AI engines interpret those searches.
- Target audience segments and their core informational needs are clearly defined
- Real-world language has been collected from chats, reviews, sales calls, forums, etc.
- Questions reflect different stages of the buyer journey (informational, comparative, transactional)
- Follow-up and next-step questions have been anticipated and mapped
- Motivations behind the query (e.g. researching, troubleshooting, comparing) are clearly understood
- Keywords are framed as natural, conversational questions
- Long-tail and intent-rich phrases are prioritised over high-volume terms
- Semantic variations and related questions are included to expand topic coverage
- AI tools (e.g. ChatGPT, Perplexity, Claude) have been used to simulate real user queries
- Traditional SEO tools (e.g. Semrush, Ahrefs, AnswerThePublic) support keyword validation
- Keywords are mapped to specific user goals or intent categories
Final Thoughts
GEO and SEO are superhero and sidekick, not fated enemies. That is to say, while you’re working on boosting organic traffic with your tried-and-tested SEO strategies, you’ll also need to consider and accept that GEO requires a stronger focus on user intent.Â
The most effective GEO content strategies are built on a foundation of understanding your clients’ true motives, mapping their queries, and delivering helpful content in a conversational and comprehensive way.Â
When used in conjunction with top SEO strategies, your research-backed GEO keyword and intent plan will not only improve AI visibility but create a better user experience.