How to Find the Buying Criteria AI Actually Uses to Recommend Vendors

How to Find the Buying Criteria AI Actually Uses to Recommend Vendors

When an AI engine recommends a vendor, it surfaces specific attributes—price, integration breadth, ease of use, compliance certifications, support tier—that justify the choice. Your share of voice tells you how often you're mentioned. Your share of criteria tells you why you're mentioned, and more importantly, why you're not.

Key takeaways

• AI engines surface 5–15 distinct buying criteria per query, and your brand may only appear against 2–3 of them, even if you rank high overall.
• The criteria an AI mentions are not random; they reflect what the model learned from training data, recent web content, and the structure of the query itself.
• You can extract these criteria by analyzing the language AI uses in its recommendations, then audit your own content to see which ones you own and which ones competitors dominate.
• Closing a criteria gap is faster than rebuilding share of voice: one well-placed, answer-first piece on a neglected attribute can shift how AI frames your brand.

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Why share of voice is not enough

Share of voice measures frequency: how many times ChatGPT, Perplexity, or Claude mentions your brand in answers to a given query. It's a useful baseline. But it misses the real question: on what grounds is the AI recommending you?

Consider a query like "best CRM for mid-market sales teams." An AI might mention five vendors. Each vendor appears in, say, 60% of answers—so share of voice is even. But the AI might justify each recommendation differently:

• Vendor A: "strongest reporting and forecasting."
• Vendor B: "easiest onboarding and training."
• Vendor C: "best mobile app."
• Vendor D: "most affordable."
• Vendor E: "tightest Salesforce integration."

If you're Vendor A, your share of voice looks good. But if your actual strength is mobile-first workflows and the AI never mentions that, you're invisible against the criteria that matter most to your target buyer. You're winning on the wrong dimension.

Share of criteria is the inverse: it measures which attributes the AI associates with your brand, and how often those attributes appear across all mentions of you.

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How AI engines decide which criteria to mention

AI models don't choose criteria at random. They surface attributes based on three overlapping signals:

1. Query structure and intent

When someone asks "best CRM for mid-market sales teams," the query itself hints at relevant criteria: team size, role, and use case. The AI will prioritize attributes that address those signals. A query about "CRM for healthcare compliance" will surface different criteria—security, audit trails, HIPAA certification—than one about "easiest CRM to implement."

2. Training data and web content

The AI learned from millions of product reviews, comparison articles, and vendor websites. If those sources repeatedly describe Vendor A as "best for reporting" and Vendor B as "easiest to use," the model internalizes those associations. When it generates a recommendation, it draws on those learned patterns.

This is why recency matters: if your competitors publish new content that reframes the category—"CRM selection now hinges on AI-native workflows"—the model will pick up that shift within weeks to months, depending on its training cutoff and how it handles live web search.

3. Grounding and retrieval

Modern AI systems like Claude and ChatGPT with web search don't rely solely on training data. They retrieve recent content to ground their answers. If your competitor published a well-written piece titled "Why Mobile-First CRM Architecture Matters," and your brand has no comparable content, the AI will mention their mobile strength and stay silent on yours—not because you lack it, but because the retrieval step didn't surface evidence of it.

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How to extract the criteria AI is using

The process is manual but straightforward. You're looking for the adjectives, nouns, and phrases the AI uses to justify recommendations.

Step 1: Collect 10–20 AI answers to your core queries

Run your top vendor-comparison queries through ChatGPT, Perplexity, Claude, and Google AI Overviews. Save the full responses. If you're a CRM, queries might include:

• "Best CRM for [your industry]"
• "CRM comparison: [Competitor A] vs. [Competitor B]"
• "How to choose a CRM in 2025"
• "CRM with best [specific feature] integration"

Step 2: Highlight every buying criterion mentioned

Read through each response and mark every attribute the AI uses to describe vendors. Use a simple spreadsheet:

Don't overthink this. You're looking for the specific reasons the AI gives for each recommendation, not your internal feature list.

Step 3: Calculate your share of criteria

For each criterion, count how many times your brand is mentioned against it, divided by the total mentions of that criterion across all vendors.

If "ease of onboarding" appears 5 times across all vendor mentions, and your brand appears once, your share of that criterion is 20%. If "reporting depth" appears 8 times and you appear 4 times, your share is 50%.

Rank criteria by:

1. Total frequency (how often it appears across all answers)
2. Your share (what % of mentions you own)
3. Relevance to your ICP (does this matter to your target buyer?)

You'll likely find a pattern: 2–3 criteria where you dominate (70%+ share), 3–5 where you're competitive (30–60%), and 5–10 where you're invisible (0–20%).

Step 4: Map criteria to your content

For each criterion, audit whether you have content that explicitly addresses it. Not implied—explicit. If "ease of onboarding" is a criterion you're losing, do you have a page or article titled something like "CRM Onboarding in [X] Days" or "Why Onboarding Speed Matters"?

If not, that's a gap. If yes, check whether it's answer-first (does it lead with a concrete claim?) and whether it's discoverable by AI (is it well-structured, does it use schema markup, is it cited by other sources?).

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Closing the criteria gap: a faster path than rebuilding share of voice

Rebuild share of voice takes months. Closing a criteria gap can take weeks.

Here's why: if an AI engine has never associated your brand with a particular criterion, it's not because you lack the capability—it's because your content doesn't make it visible. A single, well-executed piece of answer-first content on that criterion can shift how the model frames you.

Example: You're a CRM with strong mobile-first architecture, but AI answers never mention it. You publish an article:

Title: "Mobile-First CRM Architecture: Why Field Teams Need Native Apps, Not Responsive Web"

Opening: "A mobile-first CRM is built natively for phones and tablets from the ground up, not adapted from a desktop interface. This distinction matters because field teams need offline access, touch-optimized workflows, and instant sync—capabilities that responsive web design cannot deliver."

The article then covers:

• Why responsive design fails for field CRMs
• The technical differences (native vs. web)
• Real use cases (sales reps in remote areas, outdoor service teams)
• How your CRM delivers this

Within 4–8 weeks, as this content gets indexed and retrieved by AI systems, you'll see "mobile-first architecture" or "native mobile app" start appearing in AI recommendations for your brand. Your share of that criterion will climb from 0% to 20–40%, sometimes faster if competitors lack comparable content.

This works because you're not fighting for share of an existing criterion—you're making the criterion visible and owning it first.

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Tools and workflow

You can do this manually with a spreadsheet, but it scales better with structure. When you're tracking criteria across dozens of queries and multiple AI engines, a system helps:

• Collect answers: Save full AI responses (copy-paste or screenshot) in a shared folder.
• Tag criteria: Use a simple taxonomy (pricing, ease of use, integrations, compliance, support, performance, customization, AI capabilities, etc.) and tag each mention.
• Measure share: Build a pivot table that shows, for each criterion, which vendors appear and how often.
• Audit content: Cross-reference each criterion against your website and content library. Mark which ones you own, which are gaps.
• Prioritize: Focus on criteria that are high-frequency, high-relevance to your ICP, and where you have a real strength but zero visibility.

If you want to measure this systematically and track how your criteria share evolves over time, GEO Studio includes criteria extraction and tracking as part of its AI visibility audit. You can also start with a free AI Visibility Snapshot to see which criteria are surfacing for your brand today.

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Frequently asked questions

Q: Does share of criteria matter more than share of voice?

A: They measure different things. Share of voice tells you how often you're mentioned; share of criteria tells you why. A brand with 40% share of voice but only 2 criteria will lose to a brand with 25% share of voice across 8 criteria, because the second brand is visible for more buying reasons. Ideally, you want both: high share of voice and broad criteria coverage.

Q: Can I influence which criteria AI mentions?

A: Partially. You can't force an AI to invent a criterion that doesn't exist in its training data or the query context. But you can make an existing criterion visible by publishing content that explicitly addresses it. If "compliance" is a criterion the AI mentions for competitors but not for you, a well-written, answer-first article on your compliance posture will shift that within weeks.

Q: How often should I re-audit my criteria share?

A: Every 4–8 weeks if you're actively publishing new content or your competitors are. Every quarter if you're in a stable market. Major AI model updates (like a new Claude or GPT release) can shift criteria overnight, so re-audit after those events.

Q: What if a criterion I'm strong in never appears in AI answers?

A: It's not a criterion the AI considers relevant to that query, or your content hasn't made it visible yet. Test by publishing answer-first content on that strength and monitoring whether it appears in subsequent AI answers. If it doesn't appear after 6–8 weeks, it may not be a buying criterion for that query—focus on criteria that do appear.

Q: Should I create content for criteria where I'm weak?

A: Only if you can genuinely own that criterion. If competitors have 5 years of content and customer base proving they're the best at "enterprise-grade security," don't try to outrank them on that criterion. Instead, own adjacent criteria where you have real strength and less competition. Build on what you're already known for.

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Next steps

Start with one core query—the one your ICP uses most often when evaluating vendors like you. Run it through three AI engines, extract the criteria, and map them to your content. You'll likely find 1–2 criteria where you're invisible but strong. That's your quick win.

If you want to scale this across your full query set and track criteria share over time, explore GEO Studio to see how it automates criteria extraction and visibility tracking. Or grab a free snapshot to see which criteria are already surfacing for your brand in AI answers today.