A Multi-Factor Brand-Recognition Audit for AI Answer Engines: An Open Instrument and Protocol for Measuring Generative Visibility

Companion measurement paper to The Mention Density Model (DOI: 10.5281/zenodo.20379032). Where the prior paper explains why brands are recognized inside large language model answers (mention density across training and retrieval corpora), this paper supplies the reproducible instrument and protocol to measure that recognition: the Multi-Factor Brand-Recognition Audit (MFBRA), a five-factor design (Brand x Test Query x Category x Entity Type x AI Model) scored on Share of Voice, five Visibility Vitals (Presence, Clarity, Coverage, Authority, Preference), sentiment, and citation rank, plus a mapping of the aided/unaided brand-awareness funnel onto answer engines. Applied to 50+ mid-market e-commerce brands across 11 countries (n ~ 200 audits), it surfaces four measurement findings: a single-model citation lottery on broad queries, citation collapse with query breadth (cited-count 3.1 to 0.4), a 10-15 point sentiment drop on trust queries at 100% SOV, and ~50% factual mis-description. Replicable at ~$0.02 per audit.