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Measurement · Foliora field guide

How to measure AI search visibility without an opaque score

Build a reproducible prompt panel, preserve cited sources, account for variance, and connect observations to published initiatives.

Define the unit of observation

An AI visibility observation is more than a brand mention. Store the engine, model or product surface, prompt, market, persona, date, full answer, cited URLs, competitors, and whether the brand was named, recommended, or directly sourced.

Separate coverage from sentiment and citation

Coverage asks whether the question panel was sampled. Mention rate asks whether the brand appeared. Citation rate asks whether the brand’s domain was linked or named as a source. Position or recommendation language is another dimension. Combining these into one unexplained score hides useful differences.

Design for variance

Generated answers are non-deterministic and can change with retrieval conditions. Sample at a consistent cadence, preserve raw evidence, use enough questions to reduce single-prompt noise, and label directional changes rather than implying census-level precision.

Tie observations to initiatives carefully

A publishing date followed by a new citation is not proof of causation. It is a useful association when the cited URL, buyer question, and published evidence align. Report the timeline and uncertainty so a customer can judge the relationship.

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