GEO: definition and differences from SEO

The search market is changing fast. Traditional search relied on indexing web pages and presenting a list of links (“blue links”), while conversational AIs such as ChatGPT, Gemini or Perplexity generate synthetic answers from multiple sources. This new discipline is called Generative Engine Optimization (GEO), and is partly opposed to Search Engine Optimization (SEO).

SEO and GEO: definitions

SEO optimizes a site so that it appears in a good position on the results pages of conventional search engines. It is based on keyword research, the creation of relevant content and link building. The SEO.ai article reminds us that SEO “focuses on optimizing sites so that they rank higher in the SERPs” n(seo.ai).

GEO, on the other hand, is about getting your brand or content quoted or integrated into AI-generated responses. According to Boulder SEO Marketing, AI engines “synthesize information from multiple sources” and GEO is about aligning your content with these algorithms. Envisionit adds that GEO is “the practice of optimizing your content for use by linguistic models and generative systems”.

Fundamental differences between SEO and GEO

1. Target motors. SEO addresses engines that list pages, while GEO targets generative engines. SEO.ai explains that SEO targets classic search results, while GEO optimizes for complete answers produced by AIs like Google SGE or BingChat (seo.ai).

2. How information is retrieved. AIs use the query fan-out technique, described by Semrush: the AI “divides a user’s query into several sub-queries, collects information for each and merges the relevant elements into a single answer”(semrush.com). This decomposition enables us to cover different intentions, making it crucial to respond to all facets of a subject.

3. Content format. SEO values complete pages optimized for robots (titles, meta, backlinks). In GEO, content must be broken down into clear blocks, with precise definitions, lists, tables and FAQs. Envisionit notes that AIs “require a more explicit format: concise summaries, bulleted lists and FAQs”.

4. Performance indicators. SEO measures impressions, clicks and average position. GEO introduces new KPIs: citation rates, brand mentions and share of voice in AI responses. An article by Walker Sands lists key metrics: traffic referred by AIs, leads generated, engagement, brand perception, mentions and citations, share of voice and content crawl by AI bots.

5. Localization and corporate data. AI engines draw on several external sources to provide a localized response. TNG Shopper points out that users no longer consult a brand’s website: the AI “answers directly” and proposes a list of stores without necessarily displaying a link. A simple store locator is no longer enough: you need to provide consistent data on Google Business Profile, Apple Maps or Yelp, and enrich your local pages so that the AI selects you.

Why GEO and SEO are complementary

SEO remains essential: it provides the technical structure, authority and backlinks needed for AIs to spot your site. Walker Sands emphasizes that SEO “reinforces the technical and contextual signals” needed by AIs. GEO, on the other hand, builds on these foundations to develop content that AIs can reuse. Query optimization, content quality, information freshness and local data consistency increase the likelihood of being cited.

SEO vs GEO comparison chart

UGC and local data: new priorities

Generative engines value reviews and user-generated content (UGC). TrustRadius points out that AIs “look for first-hand accounts” and that verified reviews provide detailed insights that models use to build credible narratives. The article emphasizes that authentic UGC content serves as evidence and builds trust with engines. In a local context, TNG Shopper shows that the consistency of NAP (name, address, phone) data across all directories and the presence of reviews are decisive factors for AI to recommend a store.

Conclusion

Statistics and trends 2024-2025

Several studies show just how much the dynamics of search are changing. According to a report by AI News Hub, AI platforms are already capturing 63% of total website traffic, rivaling traditional engines. This transition is driven by the exponential popularity of assistants; ChatGPT has reportedly reached 300 million weekly active users, while Google still processes 8.5 billion queries a day and SEO remains responsible for around 53% of site traffic. Other figures confirm the rise of AIs: ChatGPT generates over 4.5 billion monthly visits, Perplexity records around 500 million queries per month, and AI-referred sessions have increased by 527% in just five months (akselera.tech). At the same time, some studies estimate that 60% of Google searches no longer result in a click (“zero-click search” phenomenon), showing that direct response, and therefore GEO, is becoming increasingly important.

Beyond mass adoption, these trends are changing the structure of the customer journey: users use an average of five different platforms (ChatGPT, Google SGE, Perplexity, YouTube, TikTok) to search for information. Brands must therefore optimize their presence across multiple channels to remain visible.

Preferred sources for AI engines

Each generative algorithm has its own favorite corpus. An analysis by the Frase site reveals that ChatGPT cites articles from Wikipedia (47.9%), illustrating the importance of being referenced on neutral encyclopedias. Perplexity relies heavily on community platforms such as Reddit (46.7%) and values the freshness of information, favoring recently updated content. Google AI Overviews (SGE) uses mostly content that already dominates SEO results, but its algorithms give high weight to credibility and data structure. According to Akselera, we also need to understand the preferences of the Claude model, which relies more on long, nuanced texts to provide perfect context.

These differences imply a multi-platform strategy: developing encyclopedic pages for ChatGPT, participating in community discussions for Perplexity, strengthening SEO and E-E-A-T for SGE and publishing contextual articles for Claude.

GEO challenges and limitations

Despite its opportunities, GEO is not without its challenges. The models are not entirely transparent: their selection of sources is probabilistic and can vary from query to query. Companies must also manage the risk of inaccurate generated content or quotes taken out of context, which can damage the brand. Finally, increased reliance on AI platforms accentuates the need to protect data and verify the reliability of information.

Combined best practices

To take advantage of both SEO and GEO, it is recommended to :

• Maintain a solid SEO foundation: technical optimization, loading speed, backlinks and authority remain essential to being discovered by AI robots.

• Structure and enrich content: use hierarchical headings, lists, tables and schema markup to facilitate extraction by AI.

• Gather social proof: solicit customer reviews (UGC), integrate testimonials and case studies.

• Update information regularly: an updated page is 3.2 times more likely to be cited than out-of-date content.

By applying these best practices, companies can ensure increased visibility across all search engines and support the transition from SEO to GEO.