Datapods
Use Case

Test products with people who actually buy the category.

Concept, packaging, price — tested with verified buyers of your category instead of professional panelists. Census-weighted, results in days. And after launch, the same panel shows whether people buy.

With people who verifiably buy the category — not just claim to.

Concept test · MaxDiff

Which concept wins with heavy buyers?

Concept B — “No additives”42%
Concept A — “Protein+”31%
Concept C — “Barista”27%

n = 412 verified category buyers · census-weighted · 6h fieldwork

How it works

From behavior to answers — and back.

[01]

Find category buyers

Heavy buyers, occasional buyers, private-label buyers: we build the sample from real purchases, not screener answers.

[02]

Test the concept

MaxDiff, monadic, pricing — combined with AI interviews that probe why a concept wins. Fieldwork in hours, not weeks.

[03]

Track the launch

After launch, orders and receipts show whether intent turns into purchases — in the same panel.

A question you could ask tomorrow

Which of the three packs wins with heavy oat-drink buyers — and why?

What you get

Samples from real purchases

Category buyers defined by orders and receipts — not self-reporting.

Quant + qual in one run

MaxDiff and monadic tests, plus AI interviews on the reasons behind them.

Census weighting

Results weighted to the German population — board-ready.

Launch tracking

Purchase intent is a start. We measure actual purchases afterwards.

Why our data wins here
REWE

German grocery product catalog — tests at SKU level, not just category.

E-receipts

Inbox receipts show what actually ends up in the basket.

Census

Population weights for representative claims instead of convenience samples.

Powered by the Datapods app
25,000+people in the panel, verified through connected accounts
11B+behavioral data points across 12 platforms
100%opt-in — every share is a conscious, paid decision
GDPRnative & EU-hosted. Never used to train open models.
Use Cases

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