When a Google AI summary appears, users click a traditional result link in only 8% of visits, according to Pew Research Center. Visibility in AI answers now decides who gets considered. The new AI Mindshare Suite claims to measure that, and this review looks at what it does, where it is useful for marketing teams, and which questions to ask before you sign.
What the suite actually launched
The vendor announced general availability of an AEO Metrics Suite built around three proprietary scores: Brand Mindshare, Brand Consideration and Brand Engagement. The company says they measure how generative AI platforms position a brand against its competitors. The suite sits on top of its existing conversation-mapping infrastructure for answer engine optimization (AEO) and generative engine optimization (GEO).
The pitch is simple. Most teams count mentions and citations. The vendor argues those counts can’t separate a brand named as the recommended choice from one listed as an afterthought, and that they treat a single answer as if it represented a whole multi-turn conversation.
That critique is fair. A mention is not a recommendation.
The three scores, in plain language
- Brand Mindshare: tracks share of voice and share of citation across AI answers. Think of it as your presence relative to competitors.
- Brand Consideration: measures positioning against named competitors, meaning whether the AI recommends you over rivals.
- Brand Engagement: tracks how deeply the AI engages with your brand across multi-turn conversations, so a brand named once and dropped scores differently from one reinforced across several turns.
The vendor’s own guidance on intent clusters frames the work as tracking citations by cluster rather than by static keyword. It also sets a minimum volume of search journeys for the Engagement and Consideration read. That number matters for budgeting, and we’ll come back to it.
A brand cited as one option and a brand cited as the recommended choice look identical in a simple mention count. Consideration scoring exists to separate them.
Why marketing teams should care now
Pew Research Center tracked the browsing of 900 U.S. adults in March 2025. In its analysis of AI summaries, users clicked a traditional result in 8% of visits with a summary, against 15% without one. Links inside the summary itself were clicked in just 1% of visits, per Pew.
Read that as a budget problem. If the answer is the destination, then being named inside it is the placement you’re competing for. Rank tracking alone no longer tells you whether you’re winning.
Pew also found that users ended their browsing session on 26% of pages with an AI summary, versus 16% on pages with only traditional results. Fewer second chances. Fewer retargeting opportunities.
Where it fits for creator and influencer programs
Influencers Time readers run creator programs, so here is the practical angle. Creator content, reviews and discussion threads feed the sources AI engines draw from. Pew’s data shows Wikipedia, YouTube and Reddit are the most commonly linked sources in both AI summaries and standard results. If your seeding strategy touches YouTube creators or community threads, a Consideration score could show whether that work shifts how engines frame you.
That is a hypothesis, not a proven result. The vendor’s materials don’t present creator-specific data, so treat any attribution of creator spend to score movement as something you must test yourself.
For the measurement mechanics behind this, see our breakdown of prompt citation tracking. If you’re comparing vendors in the category, the review of Pyras Prime covers a different set of claims worth holding next to this one.
What the evidence does and doesn’t show
Credit where due: the vendor published a methodology angle. The announcement describes perturbation testing across 4,445 ChatGPT responses and 135 matched query pairs in the Australian insurance category, aimed at showing how sensitive answers are to small query changes. That is the right instinct, because AI answers are unstable and any score built on a thin sample is noise.
But a launch announcement is not an independent audit. The one named customer example, Rentomojo, the Indian furniture and appliance rental platform, is described qualitatively. No lift figures, no before and after, no cost data appear in the material I reviewed.
So here is my read. The scoring concept is sound. The proof of commercial impact is not yet public.
Treat the three scores as diagnostic instruments, not outcome guarantees. Until a score is tied to pipeline or revenue in your own data, it is a leading indicator at best.
A vetting checklist before you buy
- Ask for score stability. Run the same prompt set twice, a week apart. If scores swing widely, the measurement is too noisy to steer budget.
- The vendor’s guidance on journey volume implies a heavy sample requirement. Ask who pays for that volume and whether your category is covered at that depth.
- Check engine coverage. The launch names ChatGPT, Google AI Overviews, Claude, Gemini and Perplexity. Confirm which ones feed each score and how they’re weighted.
- Demand definitions. What counts as a “recommendation” versus a mention? Who labels sentiment, and how often is it audited?
- Test the exit. Can you export raw conversation data? If not, you are renting insight you can’t keep. Our guide to AI vendor vetting lays out the exit terms to request.
- Tie it to outcomes. Agree up front how you’ll connect score movement to leads or sales, using something like the approach in our pipeline attribution review.
Suite or specialist?
Many teams already pay for SEO platforms that bolt on AI visibility modules. Is a dedicated scoring layer worth another line item? It depends on whether your leadership asks “are we chosen?” or only “are we visible?” If it’s the first question, a Consideration-style metric is hard to fake with mention counts. If it’s the second, your existing stack may be enough. Our piece on suite versus specialist fit gives a framework for that call.
One more caution. Proprietary scores are hard to benchmark across vendors. If you switch tools, your history may not translate. Keep a simple internal baseline (a fixed prompt list and a manual review of the top answers) so you can compare any vendor’s numbers to something you own.
FAQs
What is the AI Mindshare Suite?
It is the AEO Metrics Suite, three proprietary scores (Brand Mindshare, Brand Consideration and Brand Engagement) that the vendor says measure how generative AI platforms position a brand against competitors. It is aimed at marketing teams that want to know whether a brand is chosen, not just visible.
How is Brand Mindshare different from a citation count?
Per the vendor, Mindshare covers share of voice and share of citation, while Consideration adds positioning against named rivals. A raw citation count can’t tell a recommended brand from an alternative that was merely listed.
Does it cover Google AI Overviews and ChatGPT?
The launch announcement names ChatGPT, Google AI Overviews, Claude, Gemini and Perplexity. Ask the vendor how each engine contributes to the scores for your category.
Is there proof it improves ROI?
Not publicly that I could verify. A customer example exists (Rentomojo), but it is described without lift or cost figures. Run a pilot and tie score changes to your own pipeline or sales data.
Who should consider it?
Brands in categories where buyers ask AI assistants for recommendations, and teams whose creator or content programs aim to shape those answers. If your category sees little AI-driven research, a simpler tracker may be enough.
Why do AI answers make measurement hard?
Answers shift with small wording changes and across multi-turn conversations. That is why sample size and repeat testing matter more than any single score.
Next step: build a 50-prompt baseline for your category this week, run it through any vendor’s trial, and refuse to buy until their scores match your manual read twice in a row.
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