What is GEO (Generative Engine Optimization) and why your brand needs it
Imagine a potential customer asking ChatGPT: "What's the best digital marketing agency in Barcelona?" or "Which management software is most reliable for SMEs?" If your brand doesn't appear in that answer, you've just lost a business opportunity without even knowing it. Welcome to the era of GEO: Generative Engine Optimization.
According to Gartner, the volume of traditional search engine queries will drop by 25% by 2026
Google AI Overviews already appear in 57% of long-tail searches. The question is no longer whether your company needs GEO, but how much you're losing by not having it.
What exactly is GEO?
GEO (Generative Engine Optimization) is the process of structuring and optimising your digital presence so that AI-based search engines—ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude—cite, mention, or recommend your brand when users ask questions relevant to your industry.
Unlike traditional search engines, which display a list of links, generative engines analyse information from multiple sources simultaneously, cross-reference, evaluate consistency patterns, and generate a unique, direct answer.
Your content no longer competes for a click in a list of ten blue results; it competes to be the source that AI chooses to cite.
GEO vs. SEO: What's the difference?
Traditional SEO optimises your website to get clicks from Google's search results pages. GEO optimises your content to be cited within AI-generated responses. They are complementary disciplines, not mutually exclusive.
- SEO: measures ranking positions, clicks, and organic traffic from search engines.
- GEO: measures brand mentions, linked citations, and participation in AI responses.
- SEO: relies on Keywords, Backlinks, and technical optimisation.
- GEO: relies on topical authority, content structure, citable data, and brand consistency across the digital ecosystem.
Research from Brandlight reveals an alarming fact: the overlap between Google's top links and AI-cited sources has fallen from 70% to less than 20%. This means that ranking well on Google no longer guarantees AI will mention you.
How do LLMs decide which brands to mention?
Language models don't choose brands randomly. They follow identifiable patterns that, when systematically worked on, produce cumulative results. These are the key factors:
1. Source authority and trust
LLMs no longer rely on a single source, no matter how well-positioned it is. Approximately 48% of citations in AI searches come from user-generated and community sources: Reddit, LinkedIn, Wikipedia, YouTube, and academic repositories like arXiv are among the most cited for brand mentions.
2. Entity recognition
Models don't just look for exact Keyword matches; they infer relationships between your brand, your category, your features, your customers, and the sources that describe you. If these signals are weak, inconsistent, or outdated, your visibility suffers even if your website is technically impeccable.
3. Content structure and freshness
Pages not updated quarterly are three times more likely to lose citations. Furthermore, well-structured sequential headings and the use of structured data (schema markup) correlate with 2.8 times higher citation rates.
4. Content depth
One-third of ChatGPT's citations come from deep pages within a website's structure—practical guides, product comparisons, specialised articles—not from homepages or category pages. AI seeks concrete answers, not generic corporate messages.
5. Dual signals: mention + citation
Brands that receive both a mention and a linked citation are 40% more likely to reappear in consecutive queries. The combination of both signals creates a reinforcing effect.
The pillars of GEO: what to optimise
E-E-A-T enhanced for AI
Google's E-E-A-T framework (Experience, Expertise, Authority, and Trust) gains even greater importance in the context of generative AI. AI engines apply stricter scrutiny to YMYL (Your Money, Your Life) topics: finance, health, security, and professional services. To maximise your visibility:
- Experience: include practical cases, proprietary data, and real testimonials that demonstrate direct experience.
- Expertise: sign your content with identifiable authors who have verifiable professional profiles (LinkedIn, academic publications).
- Authority: get mentions and citations in industry media, professional directories, and reference platforms.
- Trust: always cite primary sources with publication year, link to recognised studies, and keep your information updated.
Structured data (Schema Markup)
Structured data markup in JSON-LD format helps AI systems interpret your content unequivocally. Implement at a minimum:
- Organization Schema: name, logo, location, social media.
- Product/Service Schema: for each product or service, with prices, availability, and features.
- FAQ Schema: turn your industry's frequently asked questions into citation opportunities.
- Article Schema: for editorial content, with author, publication date, and update date.
- LocalBusiness Schema: if you have a physical presence, include address, opening hours, and contact details.
Topical Authority
LLMs favour sources that demonstrate deep and consistent mastery of a topic. It's not enough to publish an isolated article: you need a cluster of interconnected content that covers a topic from multiple angles. If your company sells accounting software, you should have content on tax regulations, tool comparisons, implementation guides, and success stories, all coherently linked.
Practical guide: 8 steps to improve your AI visibility
Step 1: Audit your current visibility
Before optimising, you need to know where you stand. Ask questions relevant to your business on ChatGPT, Perplexity, Gemini, and Claude. Record whether your brand appears, in what position, with what sentiment, and if it includes a link. Do this for at least 20-30 representative queries in your sector.
Step 2: Answer directly in the first 50 words
AI systems using real-time retrieval (Perplexity, Google AI Overviews) primarily evaluate a page's relevance by its initial content. The first 200 words of any article should directly answer the main query, without circumlocution or empty introductions.
Step 3: Use question-format headings
AI systems match headings with queries. A heading "How much does a CRM for SMEs cost?" is much more likely to be cited than "CRM Prices." Rephrase your H2s and H3s as questions that reflect how your customers actually speak.
Step 4: Include specific, citable data
AI models favour content with concrete data. The statement "AI marketing campaigns generate 20-30% higher ROI" is much more likely to be cited than "AI improves marketing results." Include statistics, percentages, figures, and measurable results in your content.
Step 5: Implement comprehensive structured data
Ensure all your main pages have the appropriate schema markup. Validate your JSON-LD with tools like Google's Rich Results Test and ensure the structured information is consistent with the visible content.
Step 6: Build presence in community sources
With almost half of AI citations coming from community sources, your strategy cannot be limited to your website. Actively participate in specialised forums in your sector, answer questions on platforms like Reddit or Quora, publish valuable content on LinkedIn, and gain presence in directories and reference listings.
Step 7: Update your content quarterly
Outdated content loses citations. Establish a quarterly review schedule for your most important pages. Update data, add new information, and modify the update date in the schema markup.
Step 8: Monitor and adjust continuously
GEO is not a one-off project but an ongoing process. Monitor your AI visibility monthly, identify which queries you've gained or lost, and adjust your strategy accordingly.
How is AI visibility measured?
Measuring visibility in generative engines requires different metrics and tools than traditional SEO. These are the fundamental metrics you should track:
- AI Visibility Score: a composite score that aggregates appearance frequency, sentiment of mentions, prominence (first mention vs. secondary appearance), and breadth of query categories where you appear.
- Share of Voice in AI: percentage of AI responses that mention your brand compared to the total responses for your target queries. Leading brands capture 15% or more in their primary categories, and up to 25-30% in specialised niches.
- Mentions vs. Citations: mentions indicate that AI knows your brand; citations (with a link) indicate that it trusts your content as a source. Both metrics are important but reflect different levels of authority.
- Answer Inclusion Rate: the percentage of times your brand is included in the AI's response for a given set of queries. This is the primary KPI for GEO.
Specialised tools like Otterly, AIclicks, LLMrefs, or Sistrix (which already incorporates an AI visibility index) allow for automated tracking. However, many companies start with manual audits before investing in paid platforms.
Real cases: brands already winning with GEO
A 2024 academic study analysed thousands of interactions between language models and online content, concluding that pages optimised for entity clarity, structure, and contextual flow were cited up to 58% more in AI-generated summaries compared to unoptimised pages.
The case of Tally, an online form tool, is particularly illustrative: ChatGPT became its number one referral traffic source, above Google, simply by having well-structured content, a consistent community presence, and a clearly defined value proposition.
In the field of spas and wellness centres, a study published by Search Engine Journal documents how the implementation of specific GEO strategies—including structure optimisation, structured data, and presence in community sources—significantly increased brand mentions in AI responses over a period of a few weeks.
These cases share a pattern: it's not about technical tricks, but a comprehensive strategy that combines quality content, clear structure, citable data, and consistent presence across multiple channels.
The future is now: the window of opportunity
ChatGPT already handles 12% of Google's search volume. Perplexity, Gemini, and Claude are growing rapidly. Y Combinator's predictions suggest that traffic from generative engines will replace 50% of traditional search volume by 2028.
But here's the good news: most companies are not yet optimising for AI. This means there's a real window of opportunity for brands that act now. Just as companies that adopted SEO in its early years gained a lasting competitive advantage, those who invest in GEO today will be positioned to dominate AI visibility for years to come.
Most brands start seeing improvements in their citations within 4 to 8 weeks after implementing structural changes and schema markup. Perplexity usually responds faster due to its bias towards recent content, while ChatGPT and Google AI Overviews require a little more time.
Take the first step: measure your AI visibility
At ZDS, we have been helping companies position themselves in the digital environment for years, and AI visibility is the natural evolution of that mission. That's why we have developed our own AI Visibility Tracker: a tool that automatically monitors how your brand appears in responses from ChatGPT, Gemini, Perplexity, and Claude, measures your Share of Voice against competitors, and tracks the evolution of your mentions and citations over time.
If you want to know how AI engines perceive you today—and what you can do to improve that perception—contact our team. We will show you exactly where your brand stands in the AI ecosystem and design a GEO strategy tailored to your industry and objectives. Because in a world where AI answers your customers' questions, what matters is not just being on Google: it's being in the answer.
