It isn't about how many personas you can generate. A billion plausible shoppers is a party trick.
The only question that pays your team back is whether the model reproduces your real sales — and ours is built, and tested, to do exactly that.
Grounded in what shoppers actually bought — not what a survey said they might do.
Fit to your sales until the model's numbers match reality — not just look plausible.
How well it predicts real market shares on sales it never saw. A blind test, published.
Most synthetic-shopper tools stop at the first layer and hope it's right. The second layer is where a plausible simulation becomes a decision-grade one.
Simulated buyers weigh brand, price and features against the alternatives on the shelf and pick — the way real people trade off a name they trust against a dollar saved. This is where behaviour becomes believable: substitution, loyalty, the pull of a promo. But believable is only the starting point.
We estimate a structural demand model (BLP-style) against your real transactions, tuning the model until its predicted shares and price responses line up with what the market truly does. This is the moat: not a prettier simulation, but one whose outputs are anchored to measured reality — and checked on data it never saw.
Purchases and market structure — the record of what happened, and the shelf it happened on. No scraped reviews, no borrowed opinions.
Products, prices and units sold in your own category. Even a few weeks anchors the model to your real market.
Scanner-style purchase panels — like Nevo's cereal and dunnhumby's frozen pizza — to extend and stress-test the model beyond your own history.
Attributes, prices and pack sizes that define how products actually compete — the axes shoppers trade off.
What rivals charge and where they show up — so the model reflects the shelf your buyers really face.
Raw agent simulation lands far from the truth. The calibration layer pulls it onto real market shares — measured on sales the model never saw.
Bar length = how close each lands to the true market share. Watch calibration snap onto reality.
Two public supermarket categories back this up — Nevo's ready-to-eat cereal and dunnhumby's frozen pizza. The full method, the honest error ranges, and where it stops working are in the research →
You can generate as many synthetic shoppers as you like and they'll all look convincing. But believable is not the same as accurate. Scale a plausible guess and you get a very large plausible guess — with no way to know how far it sits from your real sales.
Your real price elasticity, your real substitution, your real promo lift — those come only from fitting the model to transactions and checking it against data it never saw. That held-out proof is exactly the step synthetic-shopper tools skip. It's the difference between a demo and a decision.
Decision-grade means we tell you where it's strong, where it's uncertain, and where it stops holding.
We'll run the live simulator on your own category, calibrated to your real sales — and send you the full reproducible benchmark report. Believable isn't enough; we'll prove it's accurate.
Request a demo →