CRO, AEO, and GEO terms, defined.

Answer engine optimization is the practice of structuring content so AI answer engines (ChatGPT, Perplexity, Claude, Google AI Overviews) extract and cite it directly in their generated answers. It emphasizes a self-contained definition in the first paragraph, passage-level question-and-answer structure, statistical density, schema markup, and crawler-accessible HTML.

Generative engine optimization is the practice of optimizing content to be cited as a source by AI search engines that generate summarized answers. It overlaps roughly 80 percent with AEO; the authority signal that decides citation has shifted from backlinks to third-party brand mentions, which correlate with citation more strongly than links.

An answer engine is an AI system that responds to a query with a direct, synthesized answer and citations instead of a list of links. ChatGPT, Perplexity, Claude, and Google AI Overviews are answer engines.

AI search visibility is whether, and how often, AI engines cite your domain when buyers ask them questions. It is the AI-era equivalent of search ranking, measured per engine across the buyer-intent prompts your audience actually asks.

AI citability is the structural and reputational state that makes AI search engines quote a page in their answers. It is predicted by crawler accessibility, statistical density, schema completeness, named-author attribution, first-paragraph clarity, and brand-mention authority.

Retrieval-augmented generation, which Google calls grounding, is the technique AI search engines use to answer a question by retrieving relevant, up-to-date web pages from a search index and reading them before generating the answer, rather than answering from training data alone. Because the answer is built from sources retrieved at query time, crawlable, fresh, retrievable pages are the prerequisite to being cited.

Query fan-out is the technique an AI search engine uses to answer a question by generating several related sub-queries from the one a user typed, retrieving sources for each, and synthesizing a single answer. Because of fan-out, a page can be cited for a question its author never literally wrote, so topical depth and coverage matter more than exact-match keywords.

The accessibility tree is the structured representation of a web page that browsers expose to assistive technologies and automated agents, listing each element's role, accessible name, state, and value. Google's guidance describes AI browser agents reading websites from the screenshot, the DOM, and the accessibility tree, which makes a clean accessibility tree a measure of how legible a page is to an AI agent, not only to a screen reader. WhyIQ captures it through the Chrome DevTools Protocol, the same interface an agent reads it through.

Pre-traffic CRO is conversion rate optimization performed before a landing page receives any visitors. Instead of waiting for an A/B test to accumulate traffic, it simulates how distinct visitor archetypes process the page and surfaces the specific reasons each would leave, so the fixes happen before you spend on traffic.

Conversion rate optimization is the practice of increasing the percentage of visitors who take a desired action by diagnosing and removing the friction that makes them leave. It spans the page's clarity, trust signals, value proposition, and the cognitive load it places on a visitor.

Behavioral simulation models how distinct visitor types would process a page using calibrated psychological frameworks (cognitive load, dual-process cognition, regulatory focus, prospect theory), rather than asking one generic AI for an opinion. WhyIQ runs 50 such simulations per scan.

A visitor archetype is a calibrated profile of a visitor type, such as a skeptical evaluator or a price-sensitive browser, with distinct goals, trust thresholds, and cognitive state. WhyIQ draws its 50 simulated visitors from a pool of 21 archetypes to model how different people experience the same page.

The WhyIQ Score is a 0-100 conversion-readiness score for a page, derived from how 50 simulated visitors experience it. A higher score means more of the simulated visitors understood the page and were willing to act.

A heatmap is a visualization of where visitors click, move, and scroll on a page, aggregated across real traffic. It shows what visitors do but not why they leave, and it needs existing traffic before the patterns become statistically readable.

A/B testing compares two versions of a page against live traffic to measure which converts better. It requires significant existing traffic and weeks of time to reach statistical significance, which is why low-traffic pages turn to pre-traffic CRO instead.