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In practice
The workflow formalises what correlation research suggests and field measurement confirms: mentions accumulate the footprint that engines draw from, and citations follow when the named brand is also describable and its category presence is structured.
The steps: baseline — run the prompt set, record per answer who is named and what is cited (the mention-citation quadrant); target — the pages that get cited for the buyer’s questions become the placement list, prioritised where the brand is mentioned but not cited (the conversion-primed cells); place — accurate mentions on those pages, or upgraded entries where the brand is already named thinly (a bare name becomes a described entry: what it does, who for, one true differentiator); corroborate — the brand’s own pages carry the extractable answers (definitions, comparisons) so the engine has a home-domain source to cite alongside the third-party one; re-measure — the same prompts re-run on cadence, the quadrant re-read, the movement attributed honestly (correlation, again, not proof).
The workflow’s discipline is the iteration loop: cells that don’t convert get diagnosed — the mention is too thin, the page’s description of the category is off, the engine’s source set changed — and the placement or content adjusts. What the workflow refuses to promise: conversion rates or dates — engines re-index and re-compose on their own schedule. What it delivers: a repeatable process that spends placement budget where the answer layer is already looking, and a measurement trail that shows which conversions happened after which placements.
See also: AI citations, AI brand mentions, Entity gap analysis in AI answers.
Checklist: Brand Mentions Campaign.
Related service: Brand mentions.
