How do I measure generative engine visibility?

To measure generative engine visibility—how often and how prominently your brand, content, or product appears in AI-generated answers—you can track it with a mix of manual audits, prompt-based testing, and automated monitoring.

What to measure

Focus on these core metrics:

1. Mention rate

   • Percentage of prompts where your brand/domain is mentioned.
   • Example: “In 100 relevant prompts, were we mentioned in 27?”

2. Share of voice

   • How often you appear compared with competitors across a prompt set.
   • Example: “We appeared in 40% of answers; competitor A in 55%.”

3. Position / prominence

   • Whether you appear:
     • in the first sentence
     • in a bullet list
     • in a cited source
     • as a recommended option
   • Earlier and more prominent mentions generally matter more.

4. Citation rate

   • How often the model cites your site or content as a source.
   • Especially important in systems that surface citations or links.

5. Sentiment / framing

   • Whether the mention is positive, neutral, or negative.
   • Also whether your brand is described accurately.

6. Coverage by intent

   • Which user intents you show up for:
     • informational (“best CRM for startups”)
     • comparison (“X vs Y”)
     • transactional (“buy…”)
     • troubleshooting (“how to fix…”)

7. Answer accuracy

   • Are the AI-generated statements about you correct?
   • Track factual errors, outdated claims, and missing differentiators.

How to measure it

1. Build a prompt set

Create a list of prompts that represent real user queries:

• Category prompts
• Comparison prompts
• Problem/solution prompts
• Brand and non-brand prompts
• Long-tail prompts

Example:

• “Best project management tools for small teams”
• “Notion vs Asana for agencies”
• “What tool helps track content approvals?”

2. Run the prompts on multiple models

Test across:

• ChatGPT / GPT-based assistants
• Claude
• Gemini
• Perplexity
• Other AI search / answer engines relevant to your audience

Because visibility varies by model, use several.

3. Record structured outputs

For each prompt, log:

• prompt text
• date/time
• model
• response
• whether your brand is mentioned
• where it appears
• whether a citation/link is included
• sentiment
• competitor mentions

A spreadsheet can work at first; later you can automate it.

4. Score each response

Create a simple scoring system, for example:

• 0 = not mentioned
• 1 = mentioned in passing
• 2 = mentioned in recommendation list
• 3 = mentioned first / strongly recommended
• 4 = cited source or primary recommendation

This gives you a visibility score you can trend over time.

5. Compare against competitors

Track:

• your mention rate vs. rivals
• your citation rate vs. rivals
• your prominence vs. rivals

This is often more useful than absolute counts.

Useful frameworks

A practical formula:

Generative Engine Visibility Score =

• Mention frequency
• × Prominence weight
• × Citation weight
• × Sentiment weight
• × Intent coverage

You can normalize each to a 0–100 scale and track trends monthly.

Best practices

• Use the same prompt set consistently over time.
• Test from a clean environment or API to reduce personalization bias.
• Run prompts multiple times if the model is non-deterministic.
• Separate branded vs. non-branded queries.
• Track changes after content updates, PR, or SEO work.

Tools

You can do this with:

• spreadsheets + manual review
• scripting with model APIs
• AI visibility/SEO monitoring platforms
• custom dashboards in Looker, Tableau, or Sheets

Important caveat

Generative engines are not fully deterministic, so visibility is probabilistic, not fixed. Measure it as a trend, not a single number.

If you want, I can also give you:

1. a simple spreadsheet template, or
2. a more formal visibility scoring model you can use for reporting.