Question 1

What is 'share of model' as an AI search measurement metric?

Accepted Answer

Share of model is a measurement framework that tracks how often a brand appears in AI-generated answers across a defined set of relevant prompts, on the major AI assistants. The metric is calculated as the share of target prompts where the brand is cited, mentioned, or recommended, sampled across ChatGPT, Claude, Gemini, Perplexity, and Google's AI surfaces. The framework borrows from share-of-voice in classic advertising measurement but adapts to AI by focusing on prompt-level inclusion rather than impression-level exposure. The strength of the metric is that it ties measurement to actual user queries rather than to ranking position. The weakness is that AI answers are stochastic — the same prompt can produce different answers across runs — so the metric requires multiple samples per prompt to be reliable.

Question 2

Which AI search metrics are vanity metrics and which are real?

Accepted Answer

Vanity metrics include raw citation count without prompt context, screenshots of brand mentions presented as 'AI visibility,' tool-generated visibility scores with no business outcome tie-in, share-of-voice estimates extrapolated from tiny samples, and dashboard charts disconnected from revenue or pipeline. Real metrics include share of model on a defined high-value prompt set, citation quality assessment (correct claims vs. wrong claims vs. missing brand), competitor citation share on the same prompts, downstream branded search lift correlated with AI mention exposure, direct-from-AI traffic attribution, and qualified pipeline influenced by AI citations. The distinction is whether the metric connects to business outcomes or stops at vanity surface metrics. Many AEO tools sell dashboards that lean heavily on the vanity side because vanity is easier to measure and visualize.

Question 3

How do you measure direct traffic from ChatGPT, Claude, or Perplexity?

Accepted Answer

Three measurement layers work together. First, referrer-based tracking: when ChatGPT, Perplexity, or Claude send users to your site, the referrer often contains identifiable strings (chat.openai.com, perplexity.ai, claude.ai). Configure analytics to surface these as distinct source channels. Second, UTM-tagged links in places you control: when AI systems can find your branded content with UTM parameters, those parameters flow through to analytics. Third, branded search lift: track the correlation between AI mention exposure and increases in branded search queries on Google. AI mentions often drive users to search for your brand later rather than clicking through immediately, so branded search is the leading indicator of AI exposure that pure referrer tracking misses.

Question 4

What is a realistic AEO measurement cadence for most teams?

Accepted Answer

A monthly cadence works for most teams. The structure is: a defined prompt set of 30 to 100 high-value queries, sampled across three to five major AI surfaces, with three to five samples per prompt to handle stochasticity, producing a share of model number per surface and a weighted overall number. The same cadence captures competitor share, citation quality, and trend lines. Higher-frequency sampling is typically not worth the operational cost for marketing teams; the underlying changes in AI behavior and content rank are slow enough that monthly captures meaningful movement. Companies in very fast-moving categories or those running active campaigns can move to bi-weekly. Annual sampling is too sparse to be useful.

Question 5

Should AEO be a separate team or integrated with existing growth functions?

Accepted Answer

Integrated, not separate. AEO measurement and execution share too much with existing organic growth, content marketing, brand, and analytics functions to justify a standalone team in most companies. The right operating model is an AI search workstream inside organic growth, with named contributors from content, SEO, PR, brand, and analytics. The workstream owns the prompt set, the measurement framework, the monthly review, and the prioritization of AEO-specific projects. The functions execute. This avoids duplicate process, conflicting ownership, and the political cost of standing up a parallel growth function. The few companies where a dedicated AEO team makes sense are usually those with very large content operations, very specific AI-search-dependent revenue, or strategic AI partnerships that require dedicated coordination.