Share of Model (SoM) is the New KPI: How to Measure AI Visibility

Why Clicks Are a Decaying Metric

With 60-70% of searches ending without a click, measuring success by website traffic is fundamentally flawed. In 2026, AI engines (ChatGPT, Google AI Overviews, Perplexity) answer queries directly in the interface. If you measure traffic, you think you are losing. If you measure Share of Model (SoM), you see the real picture.

Traditional metrics like Click-Through Rate (CTR) and ranking positions were built for a "link economy." The modern search landscape is an "answer economy," demanding entirely new KPIs.

The Core Metrics of AI Search

Success in Generative Engine Optimization (GEO) relies on measuring visibility and influence within synthesized answers. The primary KPIs include:

• Citation Frequency: How often your brand, website, or experts are explicitly cited or linked within an AI-generated response.
• Share of Model (SoM): Your brand's citation frequency relative to your competitors across major LLMs for category-level queries.
• Entity Visibility Score: How well AI models map your brand to key industry concepts within their knowledge graphs.
• Sentiment & Framing: Whether the AI describes your brand accurately, positively, and in the correct context, rather than just merely mentioning it.

Continuous Monitoring and Attribution

Because AI models are non-deterministic—meaning they can generate slightly different answers to the same prompt—one-off visibility checks are meaningless. Brands must implement continuous tracking protocols that run prompts at scale.

Furthermore, because AI discovery often happens early in the journey without an immediate click, B2B marketing teams must adapt to multi-touch attribution and incrementality testing to connect early AI visibility to downstream sales pipeline.