Clarity
Do they understand what you do?
11 could name what kind of product this is, unprompted.
https://acme-analytics.example/15 AI-simulated buyers · US/UK/EU · 11-50, 51-200, 201-500, 501-1000, 1001-5000 employees
Your message needs work: they know what it is, but not who it's for, why it's worth their time, or why to pick you.
Do they understand what you do?
11 could name what kind of product this is, unprompted.
Can they tell what it solves, and who it's for?
7 could quickly tell what problem it solves and who it is for.
Do they actually want it?
5 would take a meeting to learn more.
Is there a reason to pick you over the alternatives?
2 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.
These are 15 simulated buyers. Want 15 real ones?
Test with humansThe 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.
Why: Every competitor now claims AI insights, so it reads as table stakes. Only 2 of 15 named anything unique — the weakest of the four measures.
3 of 15 raised this
“I get it immediately and it's clearly for someone like me. What I can't tell is why I'd pick this over Amplitude.”
Why: Name the metric that moves and by roughly how much. Abstract benefit language was the most common reason readers understood the product but did not want it.
3 of 15 raised this
“We already have this in three places. Nothing here tells me why we'd add a fourth.”
Why: Something like "for product teams at 50–500 person SaaS companies". Four readers self-excluded because they had to guess, and guessed wrong.
6 of 15 raised this
“Reads like it's built for a 200-person growth team. We're eleven people.”
Why: Readers were mentally comparing you to Amplitude and Mixpanel regardless. Doing it for them controls the comparison instead of losing it.
3 of 15 raised this
“I get it immediately and it's clearly for someone like me. What I can't tell is why I'd pick this over Amplitude.”
Why: "Hundreds of teams" was not cited by a single reader as a reason to keep going.
3 of 15 raised this
“I get it immediately and it's clearly for someone like me. What I can't tell is why I'd pick this over Amplitude.”
Why: Two readers stopped for this specific reason — they would not book a call to discover cost.
3 of 15 raised this
“We already have this in three places. Nothing here tells me why we'd add a fourth.”
Why: SOC 2, data residency and HIPAA were the first questions for healthcare and finance readers, and their absence ended the evaluation before value was assessed.
3 of 15 raised this
“We already have this in three places. Nothing here tells me why we'd add a fourth.”
Why: An explicit exclusion converts a vague mismatch into clean qualification, and readers trust pages that turn someone away.
6 of 15 raised this
“Reads like it's built for a 200-person growth team. We're eleven people.”
Why: Readers used the absence of a real interface as evidence the tool was not for practitioners.
6 of 15 raised this
“Reads like it's built for a 200-person growth team. We're eleven people.”
Why: "The intelligence layer for modern product teams" names a metaphor, not a category. Four readers could not say what you sell after reading the whole page.
5 of 15 raised this
“'Intelligence layer' is doing a lot of work to avoid saying 'dashboards'.”
Why: All three were read as filler. Readers who guessed correctly did so from the word "product data", not from the headline.
5 of 15 raised this
“'Intelligence layer' is doing a lot of work to avoid saying 'dashboards'.”
Why: Gives a second chance at comprehension without rewriting the hero, which is the cheapest possible fix for a Clarity failure.
5 of 15 raised this
“'Intelligence layer' is doing a lot of work to avoid saying 'dashboards'.”
A deliberately adversarial read of the same answers. Each claim was checked back against what the personas said and dropped if nothing supported it.
The homepage fails its primary job: a third of readers cannot name the category after reading it.
Four of fifteen could not name the product category at all. Their answers to the other three questions are flagged accordingly.
The value proposition is generic enough to belong to any competitor.
"Better decisions, faster" and "AI-powered insights" were both read as table stakes rather than differentiators.
Targeting is implicitly narrow while the copy claims breadth.
Eight of fifteen did not see themselves as the intended buyer, despite no stated audience limit on the page.
Social proof is too vague to carry weight.
"Hundreds of teams" was not cited by any persona as a reason to continue.
No differentiator survives contact
3 of 15
“I get it immediately and it's clearly for someone like me. What I can't tell is why I'd pick this over Amplitude.”
“Genuinely can't name one thing they do that the others don't. That's usually a bad sign.”
“Every competitor claims AI-powered insights now. This page could be any of them with the logo swapped.”
Missing pricing and compliance blocks evaluation
3 of 15
“We already have this in three places. Nothing here tells me why we'd add a fourth.”
“The outcome they promise is vague. 'Better decisions' isn't a result I can take to my VP.”
“Fine, but nothing made me want to act today.”
Reads as built for mid-market SaaS only
6 of 15
“Reads like it's built for a 200-person growth team. We're eleven people.”
“We're a services business. Everything on this page assumes I ship software.”
“Not for me. I run stores, not a product.”
“No pricing. I'm not booking a call to find out if it's £500 or £50,000.”
The category is never named
5 of 15
“'Intelligence layer' is doing a lot of work to avoid saying 'dashboards'.”
“Read it twice and still couldn't tell you what they sell.”
“Four sentences in and I still don't know the category. I'd have closed the tab.”
“No SOC 2, no HIPAA, no mention of data residency. Non-starter before I even learn what it does.”
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.
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.
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:
These answers are AI-simulated and directional. Validate anything you’re betting on with real buyers, your ICPs.
A detailed, section-by-section message test report from verified B2B professionals who are actually in-market for what you sell.







