What is Generative Engine Optimization (GEO) & How To Do It
Generative Engine Optimization (GEO) seems to be the name of a new branch of SEO (Search Engine Optimization) which is profoundly changing the way users access and consume information online. Traditionally, through SEO, search engines like Google worked by returning a list of links that users had to navigate to find the relevant information.
This dynamic has changed with the emergence of AI-powered search engines, such as ChatGPT, Perplexity, or SearchGPT.
Indeed, these engines no longer present a list of results but rather synthesize information from multiple sources to provide direct and contextually relevant answers to user queries. This means that the end user often no longer needs to click multiple links to get the information they need and receives a complete and immediate answer, which radically changes how content is consumed.
For example, platforms like Google’s AI Overview or Microsoft’s Copilot are designed to interpret and combine information to provide comprehensive summaries or deep analyses without requiring the user to dig through multiple sources. This requires content creators to adopt GEO strategies to ensure that their content is visible and considered authoritative and relevant enough to be included in these AI-generated answers.
History of SEO and Emergence of Generative Engine Optimization
The Beginnings of SEO
SEO was born in the 1990s, in parallel with the arrival of the first search engines like AltaVista, Yahoo! and, later, Google. At that time, search engine ranking algorithms were relatively simple, based mainly on keywords’ frequency and meta tags’ presence. The goal of SEO was to improve the visibility of websites by optimizing these elements so that they would be ranked higher in search results.
Over the years, as search engines became more sophisticated, SEO evolved to include a range of more complex strategies. Emphasis shifted to backlinks, content quality, user experience, and other technical factors like page speed and mobile optimization. Google, in particular, played a major role in this evolution, introducing algorithm updates like Panda, Penguin, and Hummingbird, all of which sought to improve the relevance of search results and reduce ranking manipulation.
The Emergence of AI and the Arrival of GEO
As AI has gained prominence, especially with advances like Google’s RankBrain, search engine optimization has started to integrate elements of machine learning. However, the real turning point has come with the arrival of advanced language models like GPT-3 but especially GPT-4 which is positioned as the reference among others.
At that time, we were not yet talking about a search engine but rather a chatbot capable of answering your questions more precisely without any notion of a search engine. Indeed, at that time, it was impossible to interact with online results as with a classic search engine.
However, it is these technologies, which are called LLM ( large language model ), which have given rise to AI-powered search engines, which are capable of combining a search engine with a conversational agent.
GEO emerged as a response to these new AI search engines, which, like SearchGPT, Perplexity, or Arc, no longer simply rank web pages but generate complete answers by synthesizing information from multiple sources, all in an interface similar to what can be found with a traditional search engine. This new form of research requires different optimization strategies focused on creating content that is not only relevant and authoritative but also well-structured and easy to integrate into these generative answers.
SEO and GEO: Two Different Approaches
The arrival of GEO marks a break with traditional SEO strategies. While SEO strives to maximize visibility in search engine results pages (SERPs) by optimizing technical but also semantic factors and specific content, GEO focuses on how AI search engines use content to answer users’ questions directly.
As mentioned above, AI engines like Google AI Overview or Bing Copilot no longer limit themselves to ranking results according to the best SEO criteria. Instead, they will select and combine the most relevant information to generate comprehensive answers. To succeed with GEO, content creators must focus more on the quality, authority, and contextual relevance of their content rather than on the technical aspects of traditional SEO.
The differences between SEO and GEO
The question everyone is asking about GEO is whether AI-powered search engines rely on the same criteria as SEO to determine what content to display in their responses or not. The answer is both yes and no, because the two strategies have similarities and are based on fundamentally different principles.
- Traditional SEO focuses on technical elements such as crawlability, crawl budget, page structure, and semantic and authority elements to improve a page’s ranking in search results. The goal is to meet the criteria of traditional search engine algorithms to maximize visibility in SERPs (search engine results pages)
- GEO, on the other hand, focuses more on the intrinsic quality of the content. AI search engines value the ability of content to directly and effectively answer users’ questions. They use natural language processing algorithms to understand the context of queries and prioritize content that is not only informative but also well-structured, readable, and relevant. Thus, GEO places less emphasis on the traditional technical aspects of SEO and more on the contextual relevance and depth of the content.
Impact of GEO on SEA
The arrival of GEO with AI search engines can significantly impact SEA (Search Engine Advertising). As a reminder, SEA consists of placing paid ads in search engine results pages to increase website visibility. However, with the rise of AI search engines, how ads are perceived and integrated into search results will likely change.
The Contradictory Nature of Today’s SEA in AI Interfaces
The very concept of SEA in search engines is based on the idea that users will see and click on ads strategically placed in search results. However, in an interface where AI generates and presents answers, traditional ads risk appearing intrusive or even discordant compared to the fluid and contextualized content produced by AI. AI search engines aim to provide direct and efficient answers to users’ questions, reducing the importance of additional navigation where SEA ads could play a role.
This contradiction could decrease the effectiveness of SEA ads, as users could perceive these ads as an interruption to their search experience. Additionally, in an environment where AI generates contextually relevant content, ads that are not aligned with that context could be ignored or, worse, harm the user experience.
GEO: Towards a Reinvention of SEA?
Faced with these challenges, AI search engines will need to innovate to reinvent SEA in this new context. Strategies such as native advertising, where ads are designed to blend into the surrounding content, could be explored further. In addition, transparency in the presentation of ads will become crucial not to alter user trust in AI-generated answers.
Another solution could be to allow companies to pay to have their content prioritized or featured in AI-generated responses. This approach would offer a new form of advertising, where brands could sponsor specific responses or ensure that their content is included in a preferred way in AI-generated responses.
However, this option raises questions of transparency and ethics. It would be essential to maintain user trust by ensuring that any advertising integrated into AI responses is clearly indicated as such, without compromising the objectivity or quality of the information provided.
Impact of GEO on CRO
CRO (Conversion Rate Optimization), which aims to increase the percentage of a website’s visitors who convert into customers or complete a desired action, is also affected by the arrival of GEO. With an alleged increasing use of AI search engines, the way users navigate and interact with content is fundamentally changing.
For example, if a user gets a complete and satisfying answer directly through an AI search engine, they may not even need to visit the source site. This can reduce direct traffic to sites, making it harder for CRO experts who rely on visitor flow to test and optimize conversions.
To meet these challenges, it is crucial to understand new user expectations. Today’s users are looking for fast, accurate, and personalized answers.
Understanding new user needs
The shift toward AI search engines is also changing how users browse and what they expect from search results. Rather than going through a series of links to find information, users increasingly expect immediate, contextual, and comprehensive answers. This is also why voice search is gaining more and more ground.
Internet users increasingly value content that is informative and engaging, shares an experience (example of UGC – User Generated Content), and is directly relevant to their specific needs. This means that content strategies need to focus more on personalization and contextual relevance. This is also something that Google is looking to implement in its SEO algorithm since November 2023 with the “Hidden Gems” update.