AI Buyer Simulation

AI Buyer Simulation is the practice of running real buyer-intent queries through multiple AI search engines simultaneously and measuring how often a brand is cited, where it ranks, and how it is described — to quantify AI visibility for a domain. It is the AI-search-era equivalent of keyword rank tracking, but with simulation prompts instead of search queries and citation outcomes instead of position numbers.

A typical buyer simulation set covers four query types. Category queries ("what are the best CRM tools?") test whether the brand surfaces unprompted. Comparison queries ("X vs Y") test head-to-head citation against named competitors. Direct-brand queries ("is X any good?") test whether the model has accurate context about the brand. Problem-solution queries ("how do I fix Y?") test whether the brand surfaces when the underlying need is mentioned.

For each query, the simulation runs identical prompts through ChatGPT, Perplexity, Gemini, Claude, and Grok, then parses the responses for brand mentions, citation URLs, citation position, and sentiment. The aggregate metric — typically Share of Voice or Citation Rate — is what marketing teams report monthly.

AI Buyer Simulation is distinct from server-side AI crawler tracking (which measures whether bots fetch the page) and from structural audits (which measure whether the page has the signals models weight). All three layers are necessary; simulation alone tells you the result without explaining the cause.

AEOlens runs 25 buyer-intent queries across 5 AI models per simulation, mapped to the four query types above, and surfaces citation rate, Share of Voice, sentiment per engine, and the specific URLs each model cited.