AI recommendation benchmark for B2B companies
When a buyer asks ChatGPT, Perplexity or Google AI Overviews who to shortlist, the answer is either your brand, a competitor, or no one clear. An AI recommendation benchmark is a fixed way to test that: the same buyer questions, the same engines, scored on visibility, recommendation, citation and competitor displacement. This page explains the method SupaDesign uses so B2B services, SaaS and professional firms can see what is actually happening before they spend on fixes.
There are no free scores and no free reviews. The paid entry point is the €750 AI Blind Spot Audit: 30 prompts, written findings in five business days.
What an AI recommendation benchmark is
An AI recommendation benchmark is a dated baseline of how answer engines respond to the commercial questions your buyers already ask. It is not a classic SEO rank tracker. It records, for each prompt and each engine:
- Whether your brand appears at all (visibility)
- Whether you are named as a fit, shortlist or recommended option (recommendation)
- Whether a source that supports your brand is cited or linked (citation)
- Who is named instead of you, and how often (competitor displacement)
The point is comparison over time on a locked prompt set — not a one-off vanity screenshot.
More on the wider discipline: AI search optimization (AEO / GEO) for B2B. Why competitors often win the prompt: Why ChatGPT recommends your competitor.
How the benchmark works
SupaDesign runs the same sequence for every engagement. The method is repeatable so month-to-month comparisons mean something.
1. Lock the buyer questions
Agree a commercially relevant prompt set before any testing. Typical sets mix:
- Category shortlists (“best [category] for [use case]”)
- Problem-led asks (“who helps [outcome] for mid-market B2B”)
- Comparison and alternative prompts (“[competitor] alternatives”)
- Pricing and fit questions buyers actually type into assistants
- “Who should we shortlist” style procurement prompts
The audit uses 30 of these questions. Monthly programmes keep the same core set so re-tests stay honest.
2. Test the same engines
Each prompt is run across the surfaces that matter for B2B research:
| Engine | What we record |
|---|---|
| ChatGPT | Brand named, position in the answer, framing (recommended / mentioned / ignored) |
| Perplexity | Brand named, citations and linked sources |
| Google AI Overviews | Brand included in the overview, supporting links where shown |
Surfaces change. The benchmark notes the date, product or mode used, and keeps the prompt wording identical between runs.
3. Score four dimensions
Every prompt × engine pair is scored on four qualitative dimensions. Scores are recorded as clear states, not invented percentages.
| Dimension | What “strong” looks like | What “weak” looks like |
|---|---|---|
| Visibility | Brand name appears in the answer | Brand absent; category answered without you |
| Recommendation | Named as a fit, shortlist pick or recommended option | Mentioned vaguely, or only as a negative / outdated example |
| Citation | A page or third-party source that supports your brand is cited or linked | Competitors’ pages or directories are cited; yours are not |
| Competitor displacement | You appear ahead of or alongside named rivals | Rivals are named and you are not |
4. Map who wins each prompt
For prompts where you are missing or weak, the report lists who was named instead and, where visible, which sources the engine leaned on (directories, listicles, vendor pages, review sites). That map drives the fix list — answer pages, entity clarity, and third-party mentions — rather than generic “create more content” advice.
5. Re-test on a schedule
A single run is a baseline. Programmes re-test the same 30 questions on the same engines (often in 90-day blocks) and report change: still absent, newly visible, newly recommended, newly cited, or still displaced. AI placement is never guaranteed; the benchmark measures what happened on the agreed set.
What the benchmark measures (scoring sheet)
Use this sheet as the structure of a report. Cells stay empty until a real run is logged for a named company. Do not fill them with illustrative numbers on a public page.
Prompt × engine matrix (template)
| # | Buyer question (locked wording) | ChatGPT: Visibility | ChatGPT: Recommendation | ChatGPT: Citation | ChatGPT: Who named instead | Perplexity: Visibility | Perplexity: Recommendation | Perplexity: Citation | Perplexity: Who named instead | Google AI Overviews: Visibility | Google AI Overviews: Recommendation | Google AI Overviews: Citation | Google AI Overviews: Who named instead | Notes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | [prompt] | — | — | — | — | — | — | — | — | — | — | — | — | |
| 2 | [prompt] | — | — | — | — | — | — | — | — | — | — | — | — | |
| … | ||||||||||||||
| 30 | [prompt] | — | — | — | — | — | — | — | — | — | — | — | — |
Scoring key (qualitative): Present / Absent · Recommended / Mentioned / Ignored · Cited / Not cited · Competitor names listed verbatim where shown.
Example row shape only (illustrative — not a real client)
The table below shows how a completed row is written. It is not a real audit result and does not refer to any customer.
| # | Buyer question | Engine | Visibility | Recommendation | Citation | Who named instead (example labels) |
|---|---|---|---|---|---|---|
| — | “Who should we shortlist for [category]?” | ChatGPT | Absent | Ignored | Not cited | Competitor A, Competitor B |
| — | Same prompt | Perplexity | Present | Mentioned | Not cited | Competitor A (cited directory) |
| — | Same prompt | Google AI Overviews | Absent | Ignored | Not cited | Competitor C |
Replace example competitor labels with real names only inside a paid client report.
Checklist: inputs before any run
- Company legal and trading names agreed (one primary brand entity)
- Website URL and key offer pages listed
- 3–5 named competitors the sales team already loses deals to
- 30 buyer questions locked (wording frozen for re-tests)
- Engines and modes noted (ChatGPT / Perplexity / Google AI Overviews)
- Test date and timezone recorded (Europe/Dublin or as agreed)
- No free “score”; work starts only after the €750 audit is paid
Checklist: outputs a complete benchmark should include
- Dated baseline for all 30 prompts × three engines
- Four-dimension scores per prompt (visibility, recommendation, citation, displacement)
- Competitor name map for weak or missing prompts
- Source hints where the engine shows citations or links
- Prioritised fix list (answer pages, entity/schema, directories and third-party mentions, technical accessibility)
- Clear statement that placement is not guaranteed
Who this benchmark is for
Established B2B companies roughly €2m–€30m revenue whose buyers research suppliers in ChatGPT, Perplexity or Google AI before they talk to sales — and who already lose shortlists when those systems name someone else.
Typical fits:
- B2B services and professional firms
- SaaS and product-led companies with a human sales motion
- Specialists who win on expertise but look thin or inconsistent on the open web
It is a poor fit if you want a free dashboard score, guaranteed placement, or Ireland/UK-only positioning. SupaDesign works globally. Studio and build work lives at Design & Build.
How this ties to the €750 AI Blind Spot Audit
The benchmark method above is what the audit runs in practice.
In five business days after a completed intake, SupaDesign:
- Tests 30 real buyer questions across ChatGPT, Perplexity and Google AI
- Shows who gets named instead of you
- Scores the four dimensions and delivers a prioritised written fix list
The full €750 is credited against month one if you continue on a monthly programme within 30 days. No meetings. Questions by email: darren@getsupadesign.com. AI placement is not guaranteed.
Monthly programmes (three-month minimum), already public on site:
| Plan | Price |
|---|---|
| Core | €2,950/mo |
| Plus | €4,950/mo |
| Build | €7,950/mo |
Full plan detail: AI visibility pricing. Discipline definitions: AEO vs SEO vs GEO.
Get My AI Blind Spot Audit (€750)Why a fixed benchmark beats ad-hoc screenshots
Ad-hoc prompts produce noise. A fixed benchmark produces a decision:
- Same questions — sales and marketing argue from one list, not cherry-picked wins.
- Same engines — ChatGPT visibility without Perplexity citation is a different problem from the reverse.
- Four dimensions — “we showed up once” is not the same as “we were recommended and cited.”
- Competitor map — fixes target who is winning the prompt and which sources models already trust.
- Re-testable — monthly or 90-day programmes can prove change (or no change) without inventing metrics.
Fifteen-plus years of design and web work sit behind how SupaDesign turns those findings into pages, entity clarity and shipped site changes — measured on the same prompt set, not promised as rankings.