People also ask
In practice
Models disagree — about which sources to cite, which brands to name, how to describe a product — because they differ in training data, retrieval partners and system design; cross-platform citation overlap measured in the low teens means the same buyer question assembles a different answer per engine. Share of model is the honest read: the prompt set run per engine, presence scored per model, the trends compared — revealing where the brand is secured, where it’s fragile, and where it’s absent entirely.
The strategic uses: budget allocation (place into the sources that move the engines the buyer actually uses — an enterprise buyer asking Perplexity and a developer asking ChatGPT need different placement maps); gap diagnosis (present in one model, absent in another usually traces to the source layer each draws on — the community surfaces versus the comparison surfaces versus the authority references); and the memory read (the search-disabled test per model, showing which engines “know” the brand from training versus from live retrieval — a durable-but-stale asset worth monitoring, not manipulating).
The reporting format: a small matrix — models by intent clusters, cells showing presence percentage and trend — which makes cross-model strategy discussable in one page. The honesty layer holds: models update on their own schedules, shares shift without warning, and the programme claims movement measured, never permanence promised. The competitive read is the quiet payoff: rivals’ share per model is visible in the same runs, and a competitor strong everywhere except the engine your buyers prefer is an opening the placement map should exploit.
See also: Share of voice in AI answers, Platform-specific citation sources, AI search visibility monitoring.
Checklist: Link Building for GEO.
Related service: Brand mentions.
