Share of Model.

Share of Model, properly understood

Share of Model measures how often AI assistants (ChatGPT, Perplexity, Google AI Overviews, and similar) mention, cite, or recommend a brand when asked category-relevant questions, calculated by running a defined panel of representative prompts across multiple phrasings and tallying mention rate — a brand mentioned in 140 of 200 prompt runs has a 70% Share of Model for that prompt set. Unlike classic SEO rank tracking, there's no single authoritative API for this yet, so measurement typically means running structured, repeated prompt panels manually or via emerging third-party GEO-tracking tools, and results can vary between runs of the same prompt because these models don't return fully deterministic answers — a single-run test is noisy, and a credible measurement needs repeated sampling over time. The mechanic behind why a brand gets cited traces back largely to the AI system's underlying retrieval from the organic web index and structured data — assistants surface brands that show up prominently, are well-structured, and are corroborated across the sources the model draws from, not brands that simply exist or claim things about themselves.

Arab-world consumers are increasingly asking AI assistants in Arabic for recommendations — "أفضل تطبيق توصيل في جدة" style queries — often before or instead of a traditional Google search, which means Share of Model for Arabic-phrased prompts is a genuinely different (and currently much less contested) surface than Share of Model for the equivalent English prompt, since fewer regional brands have invested in Arabic-language content the assistants can draw from. Building genuine Share of Model in Arabic realistically starts the same way organic SEO does — publishing clear, well-structured, factual Arabic content that answers the exact questions category buyers ask, since these assistants are citing what exists in the organic index and structured web data, not applying some separate ranking system; there's no shortcut that bypasses having citable content in the language being queried. Track Share of Model separately by dialect-adjacent phrasing too, since a formal MSA prompt and a colloquial Gulf-Arabic-phrased prompt can surface different answers from the same assistant.

The biggest risk in this space is overclaiming mechanism — no assistant vendor has published a reliable, real-time way to 'buy' or directly manipulate citation the way paid search auctions work, and any claim of guaranteed AI-citation placement should be treated skeptically; the actual lever available is producing genuinely well-structured, accurate, frequently-corroborated content and letting normal indexing and retrieval do the rest. A second pitfall is treating a single prompt run as a stable measurement — because these systems' outputs vary between runs and are frequently updated by the vendor without notice, Share of Model needs to be tracked as a trend from repeated sampling over time, not a one-off snapshot. And a high mention rate doesn't necessarily mean favorable mention — the tracking panel should also code sentiment/framing of each mention, since being cited negatively or as an also-ran competitor counts very differently from being recommended as the answer.

Pair Share of Model with organic traffic and SERP visibility (the traditional surfaces it's meant to complement, not replace, since a meaningful share of searches still land on a classic results page) and with brand mention volume across the open web generally, since AI systems draw heavily on the same corroborating-source pattern that underlies both classic SEO authority and broader digital PR — strengthening one tends to strengthen the others over time rather than requiring an entirely separate strategy.