KPIs for GEO / AI Search – TL;DR
- Traditional SEO metrics are becoming obsolete – AI summaries reduce click-through rates by up to 34.5%, with organic traffic projected to drop 25% by 2026, making traditional SEO metrics like CTR increasingly irrelevant.
- GEO focuses on AI visibility over clicks – Unlike traditional SEO that seeks clicks, GEO prioritizes brand presence and authority directly within AI-generated answers and summaries across platforms like Google AI Overviews and AI chat systems.
- New KPIs are essential for measuring success – Key metrics include brand visibility in AI overviews, attribution frequency in AI outputs, snippet retrieval frequency, and sentiment analysis in AI answers rather than traditional ranking positions.
- Measurement faces significant challenges – AI systems lack transparency in how they select content, different AI platforms have varying formats, and there’s currently no standardized, auditable data for tracking GEO performance consistently.
- Early adoption provides competitive advantage – With over 60% of searches expected to incorporate AI by 2026, CMOs who proactively implement GEO strategies and measurement frameworks now will gain lasting competitive advantages before the industry standardizes these practices.
The digital search landscape is in a state of fundamental transformation. AI-driven search and chat systems have radically changed the way people access information, making traditional SEO metrics like CTR (Click-Through Rate) increasingly less relevant.
A recent study by Ahrefs and Amsive revealed that AI summaries reduce the CTR of the first result by up to 34.5% (Ryan Law, 2025).
This raises a critical question for CMOs: how to demonstrate the Return on Investment (ROI) of content strategies in an environment where organic traffic volume could drop by 25% by 2026, according to Gartner projections?
The answer is not just in traditional search engine optimization (SEO), but in Generative Engine Optimization (GEO).
While traditional SEO focuses on metrics like keyword density and domain authority for traditional search engines, generative engine optimization goes further. Traditional search sought clicks, but GEO seeks direct visibility. This new engine optimization approach prioritizes brand presence and brand visibility in AI-generated answers, unlike traditional SEO.
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The Changing Role of CMOs in the Age of AI
The role of a CMO in this new paradigm is not to understand just what an AI system is, but rather how to ensure the brand appears in the conversations that truly matter. It is about seeking brand visibility and brand presence in these new environments, a goal already supported by the market trends that show a significant portion of B2B searches involving AI responses.
Fundamentals of GEO: Understanding LLMs and Search Intent
Generative engine optimization is based on key principles of large language models (LLMs) and user intent. Understanding how these AI models work is crucial for measuring success in GEO. It is about how the generative engine processes content.
What is Retrieval Augmented Generation (RAG)?
Retrieval Augmented Generation (RAG) is a fundamental concept in AI search. It is the process by which AI models retrieve information from external sources to AI-generated answers.
What is the importance of search intent?
Search intent is the key to generative engine optimization. It’s not about not just keywords, but about understanding why a user makes a query. User behavior shows that users want comprehensive answers. Generative systems do not just look for keyword matches; they analyze the context and purpose behind the query, which demands a more holistic digital strategy.
What are the Generative Engine Optimization KPI?
In the context of generative engine optimization (GEO), success is defined by key metrics that go beyond simple rankings. The focus shifts to visibility, brand authority, and traffic quality-performance metrics a CMO can directly link to business objectives. By understanding user intent, the content becomes more valuable. Therefore, the content strategy relies on much more.
1. Brand visibility in Google’s AI Overviews.
Beyond traditional rankings, AI search has created a new challenge for AI-driven search. GEO now focuses on AI visibility, specifically on whether content is selected by AI systems to answer a user’s question.
The frequency with which a part of your content appears in these AI overviews measures its penetration into new AI search channels, a key metric for any generative engine optimization strategy.
2. Perceived Brand Authority.
AI engines prioritize content from trusted and authoritative sources. The inclusion of data, statistics, and expert quotes demonstrates the depth of the content. Recent studies have shown that content with multiple citations from authoritative sources is significantly more likely to be used by AI.
Credibility is built not only with data, but also with the type of information that AI can verify. Therefore, the inclusion of industry experts, case studies, or original data becomes an invaluable asset.
3. Snippet Retrieval Frequency.
This key performance indicator (KPI) measures how often the search AI selects and uses website content to generate its answers. A high number indicates that the content is relevant and well-structured for these AI systems.
4. Attribution rate in AI outputs.
This refers to the frequency with which an AI-generated answer explicitly cites a brand, product, or piece of content as a source. This establishes the brand as an authority and a trusted source in the AI ecosystem.
5. Presence in Zero-Click Surfaces.
This KPI tracks whether content appears in AI results that do not require the user to click to visit the site. This ensures the brand remains visible in AI-generated answers that do not lead to blue links.
6. Sentiment Analysis in AI Answers.
This KPI evaluates the tone (positive, neutral, or negative) of brand mentions in AI answers. User queries that include the brand allow for direct analysis of perception. To measure this, CMOs can use metrics like the Conversational Engagement Rate (CER), which reflects user interactions.
7. Content Consistency Score.
These performance metrics measure how consistently an AI model uses a site’s data across different queries.
8. Traffic Quality and Conversion.
Since AI summaries often answer high-intent queries, the search traffic to the website is of higher quality. Therefore, to increase the likelihood that users will stay and take action, the content must be well-structured. User behavior metrics indicate whether users find the content useful., and micro-conversions, such as dowloads, signups, or internal link clicks, are the new key metrics for monitoring user engagement.
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Measuring GEO Success: Challenges and Solutions
Measuring success in generative engine optimization still faces limitations. Unlike traditional search, where metrics are standarized, GEO tools are new.
a) Lack of transparency.
AI systems, such as ChatGPT, Gemini, and Bing Chat, do not reveal exactly how AI engines select brands, which limits traceability. This technological opacity forces marketing teams to be more creative in their measurement methodologies.
b) Differences in AI formats.
While AI summaries like AI Overviews have a structure that can be tracked with some consistency, AI responses in Bing Chat are dynamic and hard to replicate.
c) The problem with “Audible data”
As SparkToro’s Rand Fishkin warns, “The problem with measuring viewability in generative engines is that we don’t yet have consistent, auditable data. We’re building the plane as we fly it”. This forces CMOs to complement GEO metrics with more traditional search measurements.
Key Tools for Generative Engine Optimization
GEO requires monitoring many new KPIs to measure its effectiveness. CMOs cannot rely solely on Google Search Console or Google Analytics.
a) AI simulation tools.
Use tools that simulate the behavior of AI engines to predict how the content will be cited. An AI tool could show how a blog post would be used in an AI generated response. An example of this type of technology is the Perplexity AI Labs platform, which allows marketing teams to explore how AI responds to different queries and uses sources, offering a preview of how a blog post might be cited in an AI-generated response.
b) AI monitoring and attribution platforms
Look for solutions that specialize in analyzing AI behavior and tracking visibility in generated results. Platforms like Otterly.ai focus on tracking brand mentions and links in AI responses.
Choosing the right tool is a critical component of a successful digital strategy. Tools like Ahrefs and Semrush, while valuable, are adapting their functionality. At the same time, new AI platforms promise more precise monitoring, offering a clearer view of rankings in this new channel.
Conclusion: The Future of Digital Strategy
With the rise of generative engines, algorithms no longer only reward keyword-optimized content. They also value authority, clarity and relevance in AI-generated responses.
The numbers are clear: according to a BrightEdge study, over 60% of online searches will incorporate some level of AI-driven search by 2026. This requires redifining how ROI is measured.
The key question for CMOs is no longer how many clicks their site gets, but how often and in what context their brand appears in AI-generated results.
The ultimate goal of GEO is revenue and conversions. CMOs who embrace this transition will build a lasting competitive advantage. A content strategy can no longer rely solely on traffic from blue links, but rather on a digital strategy that ensures visibility in AI-generated results.
For midsize companies, the lack of standardization in GEO measurement methodologies represents both a challenge and a great opportunity. The challenge is the uncertainty generated by the lack of a clear framework.
However, the opportunity lies in being a pioneer. By adopting a GEO measurement model early, companies can combine it with traditional conversion and brand awareness metrics. This allows them to incorporate emerging tools to audit their presence in AI briefs and chats.
As Lily Ray of Ansive states: “CMOs can’t afford to wait for GEO metrics to become standarized. The winners will be those who test, measure, and adapt their strategies before the industry catches up“.
This proactive approach reinforces the need to experiment with a variety of AI tools and implement continuous manual testing as part of continuous learning and adaptation.
Frequently Asked Questions (FAQs)
What practical metrics can CMOs use right now?
CMOs should focus on AI-driven visibility in AI-generated results. Key metrics include the frequency of brand mentions in AI Overviews and AI chats, as well as a comparison with competitors. Beyond traditional organic traffic metrics, they must track conversions and leads directly to user behavior within AI interactions.
What tools do CMOs need for GEO?
A combination of existing and new tools is needed. While Google Search Consoles and Google Analytics are still important, they are not enough for generative engine optimization. For monitoring brand mention tracking use platforms like Otterly.
Is GEO only for large companies?
No. Generative engine optimization is crucial for any company that wants to maintain its brand visibility. For mid-sized companies, the immediate value lies in benchmarking brand mentions and testing prompts to identify opportunities that competitors have not yet explored. By focusing on niche authority through structured data and high-quality blog posts, even a small company can become a trusted source for AI engines, giving them a competitive edge in AI search.
How do CMOs justify investing in GEO to the board of directors of the company?
CMOs should present GEO as a strategic measure to avoid losing digital share of voice. They can leverage statistics, such as “60% of online searches will incorporate AI by 2026”. The goal is to build a strong business case focused on business-level performance metrics rather than just organic traffic, demonstrating that failure to act today means losing competitive ground that will be costly to regain later.
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References
- Danny Goodwin (2025). “New data: Google AI Overviews are hurting click-through rates”. Search Engine Land. https://searchengineland.com/google-ai-overviews-hurt-click-through-rates-454428
- Rand Fishkin (2025). “In a Zero-Click World, Traffic is a Terrible Goal”. SparkToro. https://sparktoro.com/blog/in-a-zero-click-world-traffic-is-a-terrible-goal/
- Duane Forrester (2025). “12 new KPIs for the generative AI search era”. Search Engine Land. https://searchengineland.com/new-generative-ai-search-kpis-456497
- Aron van Gilst Rodríguez (2025). “How to measure success in generative search: key GEO KPIs”. Seeders. https://seeders.com/blog/how-to-measure-success-in-generative-search-key-geo-kpis/
- Ken Marshall (2025). “GEO KPIs to Measure Success”. RevenueZen. https://revenuezen.com/geo-kpis-to-measure-success/
