When AI Answers, Inclusion Beats Position: New KPIs for SEO

When AI Answers, Inclusion Beats Position: New KPIs for SEO

As AI-driven search reshapes how users find and choose businesses, the metrics that matter are changing too. Frank Olivo’s recent piece for Search Engine Land highlights a critical shift: “This being the case, the focus of AI search really should be on “inclusion in the consideration set” – not necessarily being “the first mentioned in that set” – as well as crafting what AI is saying about us.” SEOs and marketers must adapt KPIs and tactics to measure and earn inclusion within AI-generated answers, not just chase traditional rank positions.

Why inclusion matters more than position

Olivo’s research — which observed user behavior across ChatGPT and Google’s AI Mode — found that AI users consider an average of 3.7 businesses before deciding who to contact. That behavior contrasts with classic Google search, where moving from the No. 2 to No. 1 position can yield large traffic gains. In AI responses, being listed is the first hurdle; how the model describes your business is what drives conversions.

What the underlying systems mean for visibility

Google’s chief AI scientist, Jeff Dean, explains that these LLM-driven systems still rely on staged retrieval and ranking pipelines: “You identify a subset of them that are relevant with very lightweight kinds of methods.” In practice, that means content must first clear eligibility and quality thresholds to enter the candidate pool before a model synthesizes answers. Classic SEO fundamentals — crawlability, relevance, and freshness — still matter, but they’re part of a broader play that includes messaging and structured data.

Key points to track: new KPIs for AI visibility

• Inclusion rate: How often your pages are referenced, cited, or used by AI platforms in responses for targeted queries.
• AI referral rate: Traffic or conversions that originate from AI assistants or synthesized answers (measured via UTM, referral identifiers, or platform APIs).
• Attribution of fit: Signals that show whether the AI presented your brand as a “best fit” (examined through scraped AI responses or third-party monitoring tools).
• Message lift: Changes in phrasing the AI uses about your business — track excerpts to ensure core value propositions are preserved.
• Candidate pool eligibility: Technical signals (structured data validity, crawl frequency, page quality score) that affect whether content is considered at all.

Actionable steps to improve AI inclusion

1) Optimize for interpretability: Provide clear, structured content and use schema.org markup to make facts and attributes machine-readable. Short, scannable summaries near the top of pages help AI surface the right lines when synthesizing answers.

2) Prioritize messaging, not just ranking: Test headline and snippet copy with the aim of improving perceived fit. If AI phrasing makes another provider sound like a better match, rewrite your content to foreground the attributes that matter to users.

3) Monitor AI outputs: Use a combination of manual sampling and monitoring tools that scrape AI responses for your queries. Track inclusion instances and the exact language used about your brand.

4) Maintain technical eligibility: Ensure pages are indexable, fresh when needed, and have complete structured data. As Jeff Dean notes, the staged pipeline favors pages that survive early retrieval and reranking stages.

5) Expand content surface: Provide concise answers for common buyer questions across product and service pages. AI systems favor sources that clearly match intent; creating many small, focused blocks of content increases chances of inclusion.

How to measure impact and prove ROI

Combine inclusion metrics with conversion tracking. If an AI referral yields higher-intent traffic (as other industry data suggests), include value-per-conversion in your reporting. Use controlled experiments where possible: update messaging on a set of pages and compare inclusion and conversion rates versus a control group.

Final thoughts

AI search doesn’t eliminate the need for strong SEO — it expands it. Inclusion in AI responses depends on both traditional eligibility signals and clear, persuasive messaging the model can relay. As Frank Olivo’s reporting shows, the priority should be inclusion and how your business is presented within AI outputs, not only where you land on a ranked list. SEOteric helps clients align technical SEO with messaging strategies to win inclusion in AI-driven discovery and convert those mentions into business outcomes.

Original Search Engine Land article: https://searchengineland.com/ai-search-kpis-inclusion-469338