Message test · Example

Only 8 of 15 buyers would take a meeting to learn more.

https://example.com/15 AI-simulated buyers

Your message needs work: they know what it is, who it's for, and why to pick you, but not why it's worth their time.

Simulated responsesNo humans answered these questions. Every quote below was written by an AI model role-playing a buyer profile.
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01

Your verdict

  • Clarity

    Do they understand what you do?

    Mixed10 of 15

    10 could name what kind of product this is, unprompted.

  • Relevance

    Can they tell what it solves, and who it's for?

    Mixed10 of 15

    10 could quickly tell what problem it solves and who it is for.

  • Value

    Fix first

    Do they actually want it?

    Weak8 of 15

    8 would take a meeting to learn more.

  • Differentiation

    Is there a reason to pick you over the alternatives?

    Mixed10 of 15

    10 could name a reason to pick you over a similar option.

Four separate measures, not stages: all 15 personas answered all four questions. Each square is one persona.

Additional signalBrand alignment6 of 15WeakShow finding ▸

6 of 15 recognized the kind of company behind the page, in a tone written for them. Not one of the four layers, and it does not affect the scores above or the order to fix them in.

These are 15 simulated buyers. Want 15 real ones?

Test with humans
02

Fix these first

Fix these first

Three edits, in the order that matters.

The first is on your weakest layer, the second on the next, the third on the layer the most buyers had a problem with. Each says what to change on the page and why, with one simulated answer behind it.

  1. Say the product category in the first five words of the H1.

    Why: Respondents could not name it unprompted.

    1 of 15 raised this

    “Stub answer (unsure).”
    CMO, FinTech · 501-1000 employeessimulated
    Moves Clarity
    From what buyers said
03

All recommendations

Value

Weak8 of 15

Suggestions for this layer are not available in this report. The score stands on its own.

Relevance

Mixed10 of 15

No specific edits needed here — this layer held up.

Differentiation

Mixed10 of 15

No specific edits needed here — this layer held up.

04

Buyer evidence

Biggest risks

A deliberately adversarial read of the same answers. Each claim was checked back against what the personas said and dropped if nothing supported it.

  • high

    The page fails its primary job of naming the category

    Multiple respondents could not identify the product.

  • low

    An unsupported claim that cites nothing

    Should be dropped by verification.

Clarity

  • The category is never named

    1 of 15

    “Stub answer (unsure).”
    CMO, FinTech · 501-1000 employeessimulated
    See all 2 comments
    “Stub answer (qualified_no).”
    CMO, FinTech · 501-1000 employeessimulated
05

How this works

Who we simulated (15 personas)

15 AI-simulated personas matched to your target market. Each answered independently, without seeing your goal, the scoring criteria, or each other’s answers. Attribution is role, industry and company size only.

CMOFinTech · 501-1000 employeesEU
VP MarketingeCommerce · 51-200 employeesUS
Head of GrowthIT Services · 201-500 employeesUK
Director of ProductSaaS · 501-1000 employeesEU
CMOFinTech · 51-200 employeesUS
VP MarketingeCommerce · 201-500 employeesUK
Head of GrowthIT Services · 501-1000 employeesEU
Director of ProductSaaS · 51-200 employeesUS
CMOFinTech · 201-500 employeesUK
VP MarketingeCommerce · 501-1000 employeesEU
Head of GrowthIT Services · 51-200 employeesUS
Director of ProductSaaS · 201-500 employeesUK
CMOFinTech · 501-1000 employeesEU
VP MarketingeCommerce · 51-200 employeesUS
Head of GrowthIT Services · 201-500 employeesUK
Methodology

Every answer on this page was written by an AI model role-playing a buyer profile, scored on Wynter’s B2B Message Layers framework. The personas were sampled in code across role, industry, company size and behavioral traits; the model wrote only the answers. Scores arrive through fixed verdict categories and the counts are computed in our own code, so no number here was written by a model.

Score details: the count and the strength

The count is how many personas cleared the bar on each question. A yes can be unhesitating or come with reservations; the scorecard counts both as a yes, and this is the only place the difference is shown. Per layer:

  • Clarity: 10 of 15, 4 without hesitation, 2 with reservations
  • Relevance: 10 of 15, 4 without hesitation, 6 with reservations
  • Value: 8 of 15, 2 without hesitation, 6 with reservations
  • Differentiation: 10 of 15, 3 without hesitation, 7 with reservations

These answers are AI-simulated and directional. Validate anything you’re betting on with real buyers, your ICPs.

Your next 3 moves

  1. 1.Say the product category in the first five words of the H1.

See what real buyers say.

A detailed, section-by-section message test report from verified B2B professionals who are actually in-market for what you sell.

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