Questions, answered.

Why Not Buy is an AI-moderated qualitative research platform. We interview recent, receipt-verified buyers of your category in depth, then synthesize the findings into a report on why people choose — and don't choose — the brands in it, and what to do about it. Most clients center the report on their own brand; investors, analysts, and teams weighing a market entry can commission the same study across the whole category instead.

Only people a national behavioral-data panel has confirmed actually bought in your category, verified through omni-channel purchase receipts. Each respondent re-confirms a recent purchase before the interview begins, so you hear from buyers whose decision is still fresh — never claimed-behavior panels or hypothetical shoppers.

Surveys give you scale but shallow, pre-worded answers. Why Not Buy runs genuine open-ended qualitative interviews — laddering into motivations, probing contradictions, following the story — with far more people than you could ever put in a focus-group room. You get the depth of qualitative and the reach of quantitative.

Calibration is the core of the product. The moderator is built to avoid the failure modes that corrupt qualitative data — leading questions, sycophancy, over-probing, and putting words in respondents' mouths. Every interview is then audited by a separate AI pass that checks the narrative against the behavioral facts and flags anything that doesn't hold together.

Yes. Before any transcript is shared, it is run through a de-identification pipeline that strips names, contact details, locations, and other personal identifiers. The de-identified transcripts and coded interviews are the only versions that reach you — the raw records never leave our system.

Because interviews are AI-moderated, a wave fields and completes in days, not the weeks a traditional qualitative study takes. Exact timing depends on your sample size and criteria, and we confirm it in your quote before anything starts.

Pricing is simple and up-front. You design the study in a short chat, we come back with a clear quote covering price and timing, and nothing is fielded until you approve it — no surprises.

It comes down to a few things: how common your category is — what share of the national behavioral-data panel has recently bought it — plus how fast you need results, how much confidence you want in the findings, and your budget. Most studies land somewhere between 50 and 150 completed interviews. You don't have to work this out on your own: when we send your quote, we recommend a sample size based on all of these and give you a few options to choose from.

A decision-ready report — the real reasons behind choice, plus what to do about them — along with the de-identified interview transcripts and structured data from every interview. You can also ask questions of your own data in plain language — the platform reads across every transcript and coded interview to answer follow-ups on demand.

The protocol adapts to how a category is actually decided — from fast-moving consumer packaged goods to services with lock-in and switching costs. If you're not sure whether your category fits, get in touch and we'll tell you.

You can connect your own — the portal is never required. Why Not Buy exposes your study data over MCP — the open standard for connecting AI agents and applications to data sources. You generate an API key in your settings and point any MCP-compatible tool at your endpoint; your agent can then list your studies, read the report, browse de-identified transcripts, pull structured signals, and ask questions of your data — no portal login and no export step. Access is scoped to your account with a single key, everything stays de-identified, and it follows the same access window as the portal. It's included with every study at no extra cost.

The interview itself is fixed by design — and that's what makes the findings trustworthy. It's built on the consumer-research methods with the strongest evidence behind them, and the structure does real work: we capture what buyers actually did before asking why, so a stated reason can always be checked against real behavior. Inserting custom or hypothesis-testing questions would reintroduce exactly the leading and bias the protocol is built to strip out. What you shape is the study — your category, the brands it covers, and who we recruit — and once the interviews are in, you can ask your own questions of the data in plain language and get answers drawn from every interview.