TL;DR: Traditional SEO metrics like traffic and rankings are becoming dangerously obsolete as AI reshapes the discovery landscape. To win in this new era, leaders must adopt a new set of forward-looking KPIs focused on "LLM Visibility." At Mercury Technology Solutions, we use a proprietary four-part framework—measuring Prompt Recall, Anchor-Pair Frequency, Citation Mapping, and Distributed Entity Presence—to provide our clients with a true understanding of their authority and influence within AI-powered search.
I am James, CEO of Mercury Technology Solutions.
I've spoken with numerous CMOs who are grappling with a disconcerting new reality: their Google Search Console (GSC) data, once the bedrock of their SEO strategy, no longer tells the full story. It shows clicks, impressions, and traffic, but it's fundamentally a reactive report on a world that is quickly being superseded. It tells you what Google lets you see, not what your future customers are actually doing inside AI tools.
To navigate this new landscape, we must adopt a new measurement stack. Traditional SEO was about tracking rankings; Generative AI Optimization (GAIO) is about measuring influence. Here are the four essential GAIO KPIs we use for our SaaS and B2B clients at Mercury. These metrics are forward-looking, "trainable," and designed for the new reality of AI's pattern-based understanding.
1. LLM Prompt Recall Rate (LPRR)
- The Core Question: "How often does an AI tool recall and recommend our brand from its own 'memory,' without seeing our website in a real-time search?"
- What It Measures: This is not about ranking; it's about memorability. It assesses whether your brand has become so synonymous with a category that it's embedded in the AI's foundational knowledge.
- How We Measure It: We simulate 20-50 high-intent, non-branded buyer prompts, such as:
- "Best AI-powered CRM solutions for remote sales teams"
- "Top alternatives to Notion for corporate knowledge management"
- "Best LLM SEO Provider in Hong Kong"
- The Analysis: We then track the AI's response: Are we mentioned directly? Are we included in a "tools like..." list? Or are we completely absent? If you are not being recalled from the AI's "memory," you are not yet in the buyer's headspace via AI discovery.
2. Anchor-Pair Frequency (APF)
- The Core Question: "What specific 'semantic anchors'—competitors, integrations, use cases—does the AI associate with our brand?"
- What It Measures: This goes beyond simple mentions to measure contextual relevance. LLMs don't rank; they infer relevance based on the density of associated pairs. This KPI tracks how well you are "training" the neural pattern that defines your brand (e.g., Brand X = Category Y + Use Case Z).
- How We Measure It: We test prompts that combine categories with specific features, use cases, or competitors:
- "Free email marketing tools with a native HubSpot integration"
- "Top project management platforms that have robust onboarding workflows"
- "Tools similar to [Major Competitor] but designed for solo entrepreneurs"
- The Analysis: We reverse-engineer the co-mentions. Are we appearing alongside the right competitors? Is our brand contextually anchored to our most important integrations and use cases? This tells us how well the AI understands our precise position in the market.
3. Synthetic Prompt-to-Citation Map (SPCM)
- The Core Question: "Which specific content formats, on which specific domains, are most likely to be cited by AI for our industry?"
- What It Measures: This provides a data-driven citation blueprint, removing the guesswork from your content strategy. It maps the landscape of what AI deems a citable source.
- How We Measure It: We run a large set of 100+ controlled prompts across multiple AI platforms (ChatGPT, Perplexity, Claude, Gemini). These prompts include variations of five key query types: direct solution-seeking, comparisons, feature-centric questions, use case-specific problems, and "alternative to..." queries.
- The Analysis: We meticulously document every single source that the AI cites. We're looking for patterns: Is Reddit prioritized over G2? Do listicles on blogs outperform official documentation? Which format, hosted on which type of domain, wins most often? This map becomes our guide for where and how to publish content for maximum AI visibility.
4. Distributed Entity Presence Score (DEPS)
- The Core Question: "How 'present' and authoritative is our brand in the third-party digital ecosystem where LLMs form their understanding?"
- What It Measures: This KPI assesses your distributed trust. AI models cite consensus, not just ownership. If seven credible, independent sources all mention your tool as a solution, the AI is far more likely to cite you, even if your own website's content is merely average.
- How We Measure It: We look for:
- Authentic mentions in Reddit comments from real users.
- Conversations on X (Twitter) where people discuss switching from a competitor to your tool.
- Your integrations or use cases being embedded in third-party documentation, templates.
- The Analysis: We track the frequency of these high-quality, distributed mentions and then correlate it with how often those same sources get cited by AI tools. This gives us a score that represents your brand's authority outside of your own website.
Conclusion: A New Measurement Stack for a New Era
These four KPIs provide a forward-looking, strategic view of your brand's true influence in the new AI-driven search landscape. While traditional metrics in Google Search Console are reactive, these GAIO KPIs are predictive and "trainable." They allow you to move beyond simply reacting to traffic data and start proactively shaping how the next generation of discovery engines perceives and recommends your brand.
This is the new strategic stack for SEO. It requires a more sophisticated approach, but as we've proven with our clients, the results are faster and more durable than those achieved with traditional methods alone.
