Utah AI Search Visibility Study

The Utah AI Search Visibility Study is designed to measure those patterns across a fixed set of commercial prompts, markets and industries.

What the Study Measures

Recommendation frequency

How often a business appears as an option across the sampled prompts.

Citation frequency

How often the answer cites a source connected to the business or topic.

Source type

Whether citations come from business websites, review platforms, directories, publishers, associations or other sources.

Owned-source share

How often a recommended business is supported by its own website versus a third party.

Market variation

Whether patterns change between Salt Lake City, St. George, Lehi, Orem and other Utah markets.

Industry variation

Whether home services, healthcare, law, ecommerce and other categories rely on different source environments.

How the Study Is Structured

The study uses a fixed city x industry x prompt matrix. Recommendation, comparison and need-based prompts are kept consistent so results can be compared without rewriting the question until a preferred brand appears.

What Gets Recorded

• Platform and visible search/model mode when available

• Exact prompt

• City and industry

• Collection date

• Businesses mentioned or recommended

• Citation/source URLs

• Whether the cited source belongs to the recommended business

• Source category

• Uncertainty, refusal or no-answer behavior

Why Recommendation and Citation Are Separated

A company can be recommended without its own site being cited. A company can also publish a source that is cited without being recommended. Those are different signals and should not be combined into one artificial score.

How to Read the Results

Every published percentage should show the number of observations behind it. AI answers can vary with prompt wording, platform, model, location, user context and time, so the study represents a dated sample of market behavior rather than a permanent ranking table.

How This Connects to AI Search Optimization

The findings help us identify which types of sources repeatedly support recommendations and where businesses may have an entity, evidence or corroboration gap. See our AI Search Measurement Guide and AI Search Optimization methodology.

Does this study rank the best Utah businesses?

No. It measures how selected AI systems respond to a defined commercial prompt sample. It is not a quality rating of the businesses themselves.

Can the same prompt produce a different answer later?

Yes. That variability is one reason the collection date and repeatable prompt method are important.