Defining GEO KPIs: Share of Model, Citation Rate, and Sentiment
Introduction
The era of measuring success solely by "Rankings" and "Click-Through Rate" is ending. In the Generative Engine Optimization (GEO) landscape, users consume answers directly on the search page, often without clicking. To prove value in this "Zero-Click" environment, agencies must pivot to three new core metrics: Share of Model (SoM), Citation Rate, and AI Sentiment.
These KPIs do not measure where you rank in a list of blue links, but how you are perceived, synthesized, and recommended by the AI models themselves. This document defines these technical standards for your agency's reporting framework.
Share of Model (SoM): The New Market Share
Share of Model (SoM) quantifies a brand’s visibility within AI-generated responses for a specific category of intent.
Unlike traditional Share of Voice (SOV), which broadly measures ad spend or social buzz, SoM is hyper-specific to Large Language Models (LLMs). It answers the question: "When a user asks an AI about [Topic], how often is my brand part of the answer?"
Calculation Formula
Why It Matters
The "One True Answer" Effect: AI often synthesizes multiple sources into a single narrative. If you aren't in the model's answer, you are effectively invisible, regardless of your organic ranking.
Brand Recall: High SoM ensures your brand is consistently associated with key industry terms in the model's neural network.
Citation Rate: The Currency of Trust
Citation Rate (or Reference Rate) measures the percentage of AI responses where your content is explicitly linked as a source.
While SoM tracks mentions (text), Citation Rate tracks verification (links). In platforms like Google AI Overviews or Perplexity, a citation is the primary driver of high-intent traffic.
Feature
Brand Mention (SoM)
Citation (Reference)
Format
Plain text within the narrative
Footnote, tooltip, or "Learn More" card
Value
Brand Awareness, Authority
Traffic, Click-Through, Verification
Driver
Entity strength, frequency in training data
Schema markup, data density, unique stats
Optimization Strategy
To increase Citation Rate, content must be structured for machine readability. AI models prioritize sources that provide:
Original Data: Unique statistics or primary research.
Expert Quotes: Verified insights from recognized authorities.
Structured Format: Lists, tables, and clear headings that define relationships.
Sentiment & Entity Association: The Qualitative Metric
AI Sentiment Analysis evaluates the context and tone in which an AI model discusses your brand.
It is not enough to be mentioned; you must be mentioned correctly. Users often perceive AI outputs as objective "facts." If an AI hallucinates a negative attribute (e.g., claiming your service is "expensive" or "outdated" without basis), it damages conversion rates immediately.
The Scoring Spectrum
Positive (+1): The AI recommends your brand as a top solution.
Example: "For enterprise SEO, Agency X is the leading choice due to their proprietary tech."
Neutral (0): The AI lists your brand as an option without qualifiers.
Example: "Agency X also offers SEO services."
Negative (-1): The AI warns against the brand or cites disadvantages.
Example: "Users have reported slow support times with Agency X."
Agency Action: Negative sentiment requires immediate "Correction Campaigns"—flooding the ecosystem with fresh, authoritative content to retrain the model's association.
The "Zero-Click" Reality Check
Agencies must educate clients that a drop in traffic does not always mean a drop in revenue.
As AI answers more queries directly (Zero-Click Searches), top-of-funnel traffic will naturally decline. However, the traffic that does click through via a Citation is significantly more qualified.
Old World: 1,000 visitors → 2% conversion = 20 leads.
GEO World: 200 visitors (via Citation) → 15% conversion = 30 leads.
Your reporting must pivot from "Volume of Traffic" to "Quality of Influence."
FAQs
1. Can we track these KPIs manually?
No. AI results are highly personalized and dynamic. Manually checking ChatGPT or Google Gemini yields inconsistent data. You must use automated tools (like DECA or specialized AI monitoring software) to run thousands of permutations and average the results.
2. How often should we report on SoM?
Monthly. AI models update their indices and weights frequently (especially RAG-based systems like SearchGPT). A monthly cadence allows you to spot trends without reacting to daily volatility.
3. Does traditional SEO still matter for these metrics?
Yes. Traditional SEO provides the "raw material" for AI. High-ranking content is more likely to be ingested by RAG systems. SEO is the foundation; GEO is the optimization of that foundation for the new interface.
4. What is a "good" Citation Rate?
Benchmarks vary by industry. For informational queries ("How to..."), a 10-20% citation rate is excellent. For transactional queries ("Best agency for..."), aim for 40%+.
5. Can we influence Sentiment if the AI is "hallucinating"?
Yes. This is called "Feedback Loop Optimization." By publishing contradictory evidence on high-authority 3rd party sites (news, reviews, Wikipedia), you can force the RAG system to update its "truth" during the next retrieval cycle.
References
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