What the twins
got right.

We took real consumers who had completed two surveys, built a digital twin of each one from purchase history alone, and scored every prediction against the human who actually answered. The twin was never told what was being tested.

• 88.4%agreement across 22 factual car care behavior questions
• 86–93%within one step on a five-point concept test the consumer had never seen
• —Where it stopped working: package design preference. Purchase history cannot tell you which shelf design catches the eye.

How digital twins are built, what makes them accurate, and why the data foundation matters more than the model. The argument in one line: the accuracy of a twin is bounded by the accuracy of its training data, and there is no algorithmic shortcut around that.

• 01The three data foundations in use today — surveys, demographics, and verified purchase records — and what each is actually good for
• 02Why 75% of self-claimed category buyers had never purchased in that category (84.51°)
• 03What transaction-grounded twins require: consent, cross-retailer visibility, SKU-level granularity

A walkthrough of the simulation sandbox. Meet a twin built from 1,200+ verified purchases, run a category study in minutes, and see the exact purchase records behind every answer.

• 01A twin profile with actual SKUs, prices, brands, and purchase timing
• 02“Why This Answer” — every response links back to the records that informed it
• 03Segment comparison defined by purchase behavior, not self-reported demographics

The long-form reference on behavioral digital twins for consumer research: how the technology works, how the market got here, and how to tell the approaches apart when you are evaluating vendors.

• 01Survey-based, pure synthetic, and purchase-data-grounded twins compared side by side
• 02What the published accuracy research actually measured
• 03An evaluation checklist for platforms in this category