The buyer-psychology heatmap, explained without the jargon
A red cell isn't an opinion. Here's how we turn 50 synthetic shoppers into a score you can defend in front of a client.
| Buyer signal | You | Rival A | Rival B |
|---|---|---|---|
| Perceived Quality | 45 | 92 | 60 |
| Size / Fit Clarity | 30 | 85 | 40 |
| Value for Money | 88 | 65 | 70 |
| Brand Trust | 50 | 90 | 55 |
The heatmap is the most-screenshotted part of the product, and also the most misunderstood. So let me take the mystery out of it.
| Buyer signal | You | Rival A | Rival B |
|---|---|---|---|
| Perceived Quality | 45 | 92 | 60 |
| Size / Fit Clarity | 30 | 85 | 40 |
| Value for Money | 88 | 65 | 70 |
| Brand Trust | 50 | 90 | 55 |
Where the numbers come from
Every cell is an average of how a category-true panel of AI shoppers reacted to your assets on that one dimension. We don't ask them to rate abstractly — they compare your listing against the rivals in front of them.
- Each shopper is briefed with the fears specific to your category.
- They score you and both rivals on the same signal, side by side.
- We average the panel, so no single opinion swings the result.
One model reply is a guess. A panel gives you a distribution — and the disagreement between shoppers is itself a signal about how polarizing your listing is.
Reading it in ten seconds
Scan for the reddest row where a rival is green. That's your highest-leverage fix — the place a competitor is quietly taking the click.
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