Measuring share of answer across ChatGPT, Perplexity and Gemini

When you first start looking at Answer Engine Optimisation (AEO), your instinct is to ask a simple question:

“Do we show up at all?”

And that’s a useful starting point. If your brand is completely absent when your ICP asks key questions in ChatGPT, Perplexity or Gemini, you have a visibility problem.

But visibility is only step one.

In most categories, several vendors are mentioned. Your buyers aren’t just asking whether you exist – they’re asking which option looks safest, strongest and most relevant to their situation.

To compete in that world, you don’t just need visibility.
You need share of answer.

What is “share of answer”?

Think of share of answer as the AEO equivalent of share of voice.

Instead of asking “what proportion of mentions in the press or social media do we get?”, you ask:

“Across the answers my buyers receive from AI assistants, how much of the ‘mental space’ do we occupy vs competitors?”

More concretely, share of answer tries to capture:

• How often you are named when relevant questions are asked
• How you are positioned relative to other options
• How much of the explanation and detail is about you vs the rest of the field.

Why visibility alone is a weak success metric

Imagine three scenarios:

From a raw “are we mentioned?” standpoint, scenarios 2 and 3 look identical. From a buyer standpoint, they’re worlds apart.

If you’re serious about AEO, you need metrics that actually reflect how answers feel to a human buyer, not just whether your name appears.

That’s where share of answer comes in.

The building blocks of share of answer

Measuring share of answer across ChatGPT, Perplexity and Gemini (or any other assistant) starts with breaking each answer down into components you can score and compare.

Here are the core dimensions we use inside Odyssiant.

1. Mention frequency

Basic but essential: for a given set of prompts:

• Brand mention rate: % of answers where your brand is named at all
• Competitor mention rate: % of answers where each key competitor is named

This gives you a visibility baseline: Are you broadly present? Are there certain themes / journey stages where you disappear?

But again, it’s only the starting point.

2. Position in the answer

Most AI answers list vendors in some order. The first 1–3 names get disproportionate attention.

We can capture this with simple scores such as:

• 2 = mentioned first / as a lead recommendation
• 1 = mentioned but not first
• 0 = not mentioned

Average that across prompts and engines, and you start to see patterns like: “We appear in 40% of relevant answers, but we’re first in only 10%.” vs “Competitor A appears in 60% and is first in 45%.” This is an early proxy for answer dominance.

3. Recommendation strength

Some mentions are neutral; others are clearly endorsements.

You can use a small scale such as:

• 2 = explicitly recommended / strong positive framing (e.g. “X is often the best choice for…”)
• 1 = neutral mention (e.g. “Other options include X, Y, Z…”)
• 0 = negative / warning (e.g. “X may not be suitable if…”)

Across prompts, high average scores indicate strong perceived suitability, while mixed scores might flag inconsistent positioning or polarised reputation.

4. Coverage depth

Depth is about how much of the answer is “about you”.

Indicators include number of sentences dedicated to your product vs others, specific capabilities described, and presence of implementation detail.

You can translate this into a simple index:

• 2 = detailed coverage (specific features, use cases, examples)
• 1 = brief coverage (one sentence descriptor)
• 0 = name only

Depth matters because it shapes the buyer’s mental shortlist and shows whether the assistant has enough evidence about you to work with.

5. Evidence and citation

Finally, check whether the answer actually leans on your material:

• Does it link to your site?
• Does it cite your blog, docs, case studies, or research?
• Does it reference third-party reviews or analyst reports about you?

Scoring might look like:

• 2 = your own site/assets are cited or linked
• 1 = third-party sources talk about you (reviews, articles)
• 0 = no evident sources connected to your brand

This dimension matters because cited assets are a clue to what to strengthen or replicate, while lack of citations suggests your content is either weak, buried, or misunderstood.

Pulling it together: a simple share-of-answer score

Once you’ve broken answers into components, you can combine them into a single share-of-answer metric per brand, per theme, per engine.

For example, for each answer where your brand appears:

• Mention presence (0–1)
• Position score (0–2)
• Recommendation strength (0–2)
• Coverage depth (0–2)
• Evidence use (0–2)

Normalise these into a 0–100 scale if you like, then average across all prompts for a given Theme × Journey Step. Compare your score to your top 3 competitors and repeat for each engine.

You’ll end up with insights like:

• “On ChatGPT, our share-of-answer for ‘Risk analytics’ in the shortlisting phase is 62, vs 78 for Competitor A and 45 for Competitor B.”
• “On Perplexity, we lag badly for ‘Implementation guidance’ questions – our score is 27 vs 70+ for Competitor A.”
• “Gemini almost never cites our site directly, even when it mentions us.”

These numbers are imperfect, but they’re far more informative than a binary ‘mentioned vs not mentioned’ view.

Measuring across multiple AI assistants

Different assistants behave differently:

• ChatGPT (via OpenAI): Strong at explanatory answers. May not always cite sources in the same way as web-first engines.
• Perplexity: More search-like behaviour, with heavy citation and linking. Interesting for tracking AI-driven traffic to your site.
• Gemini / Copilot: Tighter integration with existing search ecosystems (Google/Bing). Sometimes more conservative in vendor recommendations.

Because of this, your share-of-answer can vary significantly by engine. You might lead in Perplexity (because your content is well-structured and easily cited) but lag in ChatGPT (because your brand isn’t strongly differentiated in training data).

That’s why any decent AEO measurement needs to run the same question set across multiple assistants, score each answer consistently, and provide both per-engine views and an aggregate picture.

Metrics that actually matter for AEO

Once you have share-of-answer scores, you can start building a proper KPI stack for AEO.

Turning measurement into action

Measurement is only useful if it drives decisions. Once you can see share-of-answer clearly, it should inform:

• Content priorities: Double down on themes where you’re strong. Launch targeted projects where you’re weak but strategically important.
• Positioning work: Are AI answers reflecting the positioning you want? If not, is it because of weak content, inconsistent messaging, or dated third-party material?
• Budget allocation: Move spend away from content categories that don’t shift AI answers. Channel more into high-leverage formats (evidence-rich guides, case studies, explainer docs).
• C-suite storytelling: Share before vs after snapshots. “Here’s how often we used to appear in AI answers. Here’s our share now.”

Where Odyssiant fits in

You can do all of this in spreadsheets and screenshots for a small number of prompts. But it quickly becomes painful when you’re dealing with hundreds of questions per ICP, multiple AI engines, quarterly re-runs, and the need to tie everything back to Themes, Needs and content.

Odyssiant is built to:

• Generate and manage the prompt universe for each ICP
• Run those prompts across ChatGPT, Perplexity, Gemini and others
• Score the answers automatically along the dimensions above
• Present AI Visibility Dashboards and share-of-answer scorecards
• Connect that to your content inventory and GA4 metrics.

Whether you use Odyssiant or your own tooling, the core principle remains:

Don’t stop at “are we visible?”.
Aim for “do we dominate the answer where it matters?”

That’s what share-of-answer tells you. And in the era of AI assistants, it may end up being one of your most important marketing metrics.