'The Zero Click Era': Why Marketers Should Pay Attention to GEO
The paradigm of search has completely changed. While search engines of the past listed numerous webpage links to encourage users to explore on their own, Search Generative Experience (SGE) now provides answers that directly synthesize and summarize user questions.
If search engines of the SEO era were like 'complex shopping malls' that neatly arranged stores selling the products users intended to purchase , AI engines (LLM) of the GEO era act as 'personal shoppers' that identify users' tastes and needs to recommend products . While the decision-making still rests with the user, AI functions as a powerful assistant and tool for exploring and recommending information.
There is heated discussion regarding how our brands can gain exposure on this new channel, which has the closest influence on users' purchasing decisions. While various concepts such as AEO, AIO, GEO, and SXO are emerging, the core remains the same. In the era of GEO, I will introduce which brand content AI engines cite and what content strategies marketers should employ to respond.
👉 View the GEO Checklist and Guide , reinterpreted from the perspective of Elifunt
In the era of AI search, the value of 'clicks' changes.
Impressions Increase, Clicks Decrease: The Introduction of the Crocodile Chart
With the advent of the AI search era, traditional marketing strategies centered on website traffic have reached their limits as users obtain necessary information without directly visiting websites. While brand exposure in search results is rapidly increasing, the number of clicks leading to websites is decreasing.
Unlike the past, when impressions and click-through rates were directly proportional, the introduction of the AI Overview has led to charts where impressions increase while clicks actually decrease. This phenomenon is also referred to as a "crocodile chart," likening it to the shape of a crocodile opening its mouth.
In fact, global marketing intelligence tool Ahrefs analyzed 300,000 keyword search results and published research findings showing that having an AI Overview reduces click-through rates by 34.5%.
In the GEO era, the value of 'clicks' grows. The Paradox of the Click
While many marketers express concern about declining traffic, this actually presents a new opportunity. This is because 'clicks' from AI search results are not merely a single inflow, but high-quality leads that demonstrate a conversion rate 23 times higher than traditional traffic
While search results in the SEO era were limited to sequentially listing web pages that matched specific keyword intent, AI engines in the GEO era consider the user's question situation and context to cite and interpret the most relevant web pages.
Therefore, simply having our brand content interpreted and cited in AI search results can form initial brand awareness among users. Furthermore, AI search engines prompt a series of questions in the form of chatbots; when our brand content is cited in such search situations, userscome to trust us as a 'reliable source of information.' As a result of this trust, users click on the cited page.
This is precisely why traffic generated through AI engines is highly valuable. Therefore, you should not compare the number of clicks coming through an AI engine with simple incoming traffic on a one-to-one basis. Since traffic generated through AI engines represents an opportunity to acquire high-quality leads, you should evaluate its performance with a higher weighting.
the website traffic analysis tool SimilarWeb, the average time spent on AI search engines is about 6 to 7 minutes, during which users create or view an average of 4.26 chats.
Clicks via AI engine = Opportunity to acquire high-quality leads
In fact, Ahrefsrevealed through its own research that traffic from AI search channels yielded the highest final conversion performance. Even though the absolute number of clicks decreased, it effectively became the channel that contributed the most to the website's conversion rate. Although traffic from AI search engines accounted for only 0.5% of the total, it recorded a high conversion rate of 12.1% for new sign-ups. This is a conversion rate that is a whopping 23 times higher than that of existing organic traffic.
This is a phenomenon observed not only on Elifunt website but also in the data from the websites of Elifunt's clients. While click-through rates for traffic-oriented content that previously aimed for the top spot in keyword search results (SERP) have drastically decreased, conversion-oriented content planned by specifically defining the problem situations of target customers—such as by industry or job function—has seen an increase in traffic from AI search engines.
So, what kind of content is cited in AI search results? And what strategy should be used to develop content? First, it is necessary to understand how AI search engines recognize information.
How does AI recognize content (information)?
👉 View the GEO Checklist and Guide , reinterpreted from the perspective of Elifunt
AI does not understand information intuitively. Large-scale Language Models (LLMs) process information structurally, identifying key entities and relationships within sentences based on learned patterns. This approach is similar to the process of breaking down and connecting information like Lego blocks, and some generative AI systems combine this with structured external data or knowledge graphsto construct more sophisticated responses. Ultimately, for our brand content to be effectively conveyed to AI, the information must be presented in a structural manner.
What is an Entity?
It refers to the core concepts or unique entities that constitute our brand, such as the company name, product name, service features, core values, and founder.
Representing Information Structurally: Structured Data Markup
What does it mean to represent information structurally? It is akin to defining a brand using a consistent language in the web world. It is crucial to clearly define our brand's core entities (e.g., company name, product name, service features, core values, founder, etc.) and manage them so that they are recognized as consistent information across the web (our website, social media, news, review sites, etc.). Taking technical measures to ensure that AI search engines can also understand these defined brand entities is called "structured data markup.".
B2B Enterprise Entities – Schema Markup Mapping Table
| Entity item | Schema | explanation |
|---|---|---|
| Company Name | Organization | It is a standard representing corporate information. |
| Product/Service Name | Product, Service | Suitable for describing products or services. |
| Product Category | Product (category attribute), Service (category attribute), CollectionPage, WebPage | Use the category attribute (text) within the Product or Service schema, mark up the category page as a CollectionPage or WebPage, and link the list of products belonging to that category. |
| Client Type | Audience, Business Audience | It is suitable for specifying target customers and can be used with the audience attribute within the Service or Product schema. |
| Founder / Team | Person | It is a standard for representing person information. |
| Office Location | Postal Address, Place | It is suitable for representing physical location information. |
| Document content (blogs, guides, etc.) | Article, BlogPosting, NewsArticle | You can select and use it appropriately depending on the content type. |
| Frequently Asked Questions (FAQ) FAQ Page (Includes Questions and Answers) | This is a standard method for marking up FAQ structures. | |
| Detailed description by function (technical features, etc.) | HowTo, WebPage (or properties within Product/Service) | If it is a page that simply explains a feature, we recommend WebPage, and if it is a page that explains how to use a feature, we recommend HowTo. |
| Webpage hierarchy | BreadcrumbList | It is a standard representing the navigation path of a website. |
| Contact/Request Demo (CTA) | ContactPoint (using potentialAction) | You can use ContactPoint to mark up contact information, and for specific interactions such as demo requests, you can link a form URL using the appropriate Action type (e.g., CommunicateAction) within the potentialAction property of the Organization or Service schema. |
| Awards/Certifications/Partnerships | Award, Organization | Award history uses Award, while affiliates or certification bodies use Organization. Certification information can be linked through relevant attributes (such as alumniOf) in the Product or Organization schema. |
| Customer Case Study | Article, WebPage (Utilizing the about attribute) | You can mark up customer cases as Articles, WebPages, etc., and use the about attribute to connect related Organizations (clients) with Products/Services. |
| Price Information | Offer, Price Specification | It is suitable for expressing detailed price information for products/services. |
View all structured data markup ><참고 : 구글
Connecting with Structured Data: Creating a Knowledge Graph
A knowledge graph is a collection of structured data (information on brand entities, attributes, and relationships distributed across the web) connected in a network format. This serves as the 'knowledge network' for search engines and AI, and is utilized for various queries and recommendations.
To put it simply, if the sitemap of the SEO era served as a table of contents showing the structure of the web, the knowledge graph of the GEO era serves as a map connecting the web and the world.
Structured data markup is the core material for creating knowledge graphs, and a knowledge graph is the result of semantically connecting this data. The better structured data markup is implemented, the better it can be reflected and utilized in the knowledge graphs of AI and search engines.
Google's major Knowledge Graph channels
| item | explanation | Purpose/Effect | Registration URL |
|---|---|---|---|
| Google Business Profile (GBP) | Company information, location, phone number, service registration | Location-based search exposure, official entity registration, review management | google.com/business |
| Google Search Console | Website Ownership Verification and Indexing Optimization | The technical starting point of entity-based awareness: understanding schema/SEO status | search.google.com/search-console |
| Google Publisher Center (News Registration) | Submit your company blog or newsroom to Google News | Exposure of corporate content in news feeds (enhanced authority) | publishercenter.google.com |
| YouTube brand channel | Official video content platform + entity integration possible | Prioritizing videos in search results, enhancing brand search | youtube.com |
| Google Knowledge Panel Claim (Permission Request) | If the Google Knowledge Panel was automatically generated, request direct ownership | Increased probability of entity confirmation and Google Graph registration | Click “Are you the owner of this business?” in the relevant knowledge panel |
| Google Developer Site + Schema Registration | Test after applying schema markup | Structured Data Recognition, Key to AEO Response | schema.org + Rich Result Test |
| Google Scholar (for individual use by representatives/experts) | Registration of papers, contributions, and society materials by representatives or members | Strengthening the connection between individual entities and company entities (particularly effective for B2B tech companies) | scholar.google.com |
Content formats that LLMs 'like and cite'
If you understand how AI search engines operate, you need to structure and format your content in accordance with those principles. The content (web pages) cited by AI search engines shares certain commonalities in terms of structure and format. AI cites content that possesses high structural reliability as well as strong information delivery capabilities. Just as masterpieces and academic papers are recognized for their depth through their logical organization and structural stability, AI also prefers clear structures to identify the value of content. The following are research findings on the key principles that influence how AI evaluates and cites content.
1. Optimization based on text reliability and quality
According to a paper published by researchers at Princeton University (GEO: Generative Engine Optimization, 2024), AI search engines consider the “credibility” of content to be the most important factor, and to this end, content that meets the following three formats is more likely to be cited by AI search engines.
- Credible Style: When pages related to product/feature descriptions are revised into a more persuasive and professional format, LLM citation probability improves by an average of 21.3%.
- Quantitative figures and statistical data: Simply including figures or statistics in your content increases visibility in generative engines by 25.2%.
- Source Citations and Quotes: Inserting trusted external sources into content improves visibility by 24.6%, and inserting quotes alone improves it by 27.5%.
- Negative effects of keyword stuffing: Simple keyword stuffing actually caused a decrease in LLM recommendation accuracy of more than 10%. This suggests that LLM responds by understanding the context and structure of the text, going beyond simply detecting keywords.
2. Elements that clarify the 'structure' of the content
These are structural elements that enable AI to extract key information from content.
- Logical Document Structure: Structures the document logically and visually by utilizing elements such as heading tag hierarchy (H1-H3), lists (bullet points), tables, and emphasis (bold text).
- FAQ/Q&A Structure: Anticipated questions and answers are organized into a Q&A format, structuring it so that the AI can directly utilize the relevant information in its responses.
- Utilize summarized introductions/conclusions: Place the key message at the beginning, even in long texts, so that AI can quickly grasp core values.
- Ensuring Consistency in Third-Party Channels: It is important to manage and maintain consistent and positive information about our brand not only on our own website but also across various third-party channels, such as communities, review sites, wikis like Wikipedia and Namuwiki, news articles, and social media. This can be integrated with the Knowledge Graph to increase visibility in AI search engines.
In the GEO Era, a Content Funnel Strategy Leads to Results: Traffic-Conversion-Core Content Strategy
In the AI era, past content strategies that targeted only organic traffic are no longer effective. In the GEO era, a strategic design centered on inflow, conversion, and core content is required.
Informative content aimed at driving traffic must go beyond simple keyword repetition and provide a comprehensive summary of the relevant information or an examination from multiple perspectives. Furthermore, it must be able to encompass follow-up questions or problem situations.
Users continuously ask questions, and search engines understand the context of these questions to interpret and cite the most relevant content (web pages). For this reason, content planning must also define a clear target and include solutions that address specific problems. The more specifically and detailedly you answer a customer's problem situation, the higher the likelihood that it will be interpreted and cited in AI search results that match that context.
Amidst these changes, Elifunt proposes an AI-friendly strategy by dividing content into three funnels: 'Traffic,' 'Conversion,' and 'Core.' The goal is to maximize our brand's 'citation value' and 'high-quality clicks' within AI responses.
1. Incoming Content: Quoted in AI's Summary Answer
- target
- We maximize LLM citation rates by providing optimal answers to broad and general questions. Our goal is for AI to summarize and present our content when users search for specific information.
- characteristic
- Concise and clear definitions, comprehensive summary of concepts, Frequently Asked Questions (FAQ) format, use of lists and tables, presentation of objective information based on statistics and data.
- example
2. Conversion Content: Gaining an 'Overall Advantage' in AI Recommendations
- target
- We guide AI to 'select' our products/services during the user's decision-making process. When AI is asked for recommendations regarding specific product categories or problem-solving, we ensure our brand is mentioned as a strong candidate.
- characteristic
- Clear value proposition, presentation of solutions focused on solving customer problems, competitor analysis, specific success stories.
- Application of persuasive case studies, competitive positioning, and a customer-centric explanatory structure
- example
3. Core Content: Building thought leadership based on expertise and trustworthy content
- Goal: To establish our brand as an 'authoritative source of information' in a specific field, so that AI cites our content when requiring in-depth analysis or expert opinions.
- Features: In-depth research data, exclusive insights unique to our company/product, know-how based on real-world experience, and clear, professional explanations of complex concepts. Authoritative and persuasive writing style, citation of reliable sources, and inclusion of quotes.
- example
Conclusion: GEO is not AI search optimization, but a strategy to convey a brand's philosophy.
GEO is not merely a technical tactic to get cited in AI search results. Rather, it is closer to the realm of strategy that connects and spreads a brand philosophy across the web world. In the early days of SEO, simply listing keywords was enough to expect top search rankings and performance. However, search algorithms have evolved to identify valuable web pages, and now keyword stuffing actually leads to disadvantages.
While GEO is still in its early stages, the trend of this change is clear. An era is fast approaching where achieving results is difficult with technical knowledge alone. We must define GEO not to create content simply to be selected by AI search engines, but to 'effectively convey' our brand's values and philosophy clearly and authentically. The key is to design it so that the brand's intrinsic values are communicated even more clearly through the powerful channel of AI.
Change always brings both crisis and opportunity. Rather than viewing the Zero Click era solely as a crisis, we must seize this opportunity to leverage the new tool of AI to spread our brand's knowledge and values more widely and deeply, and to build a new level of trust.
Elifunt believes that this GEO strategy will provide a key direction for companies in the 'AI-First' era to move beyond simple optimization and build sustainable brand growth and deep connections with customers.
