What is synthetic research?

Synthetic research uses AI-powered personas that simulate how real buyers, customers, or market segments would respond to your research questions. It produces directional signal — insights to sharpen your hypotheses — before you invest in real primary fieldwork. Gather Synthetic runs AI-moderated interviews across any audience archetype in minutes, not weeks.

How is synthetic research different from real research?

Synthetic research generates directional signal using AI personas, while real primary research collects data from actual people. Synthetic pre-research is faster (minutes vs. weeks), cheaper, and ideal for hypothesis generation and stakeholder alignment. It is not statistically valid and should be used to focus and prioritize real fieldwork, not replace it. Gather offers both synthetic pre-research and real AI-moderated interviews with actual respondents.

What types of research can Gather Synthetic run?

Gather Synthetic supports 14 study types: Brand Perception, Product Feedback, Concept Testing, Competitive Intelligence, CX/NPS Deep Dive, Messaging & Claims Testing, Win/Loss Analysis, Market Segmentation, Pricing Research, Ad & Creative Testing, Customer Journey Mapping, Feature Prioritization, Churn & Retention Analysis, and Audience Discovery.

How much does synthetic research cost?

The first 5 synthetic interviews are free. After that, B2C audience interviews cost $0.50 each and B2B audience interviews cost $5.00 each. There is no subscription required.

How accurate is synthetic research?

Synthetic research is directional, not statistically accurate. Confidence scores reflect internal AI response consistency, not statistical significance. Quantitative projections carry a ±15–20% margin of error. Treat findings as educated hypotheses to validate with real primary research, not as definitive measurements.

← All brands

Creative hobbies & individual-parts shopping · PUBLIC RESEARCH BRIEF

LEGOCan builders find the right pieces for their next idea?

LEGO's US Pick a Brick page offers individual pieces with search and filters, a search-within-set option and piece-list upload. These different starting points create a useful research question: which path helps a builder assemble an accurate order, rather than just browse more pieces?

Sources checked 2026-09-20 · Simulation results not yet generated

Test a question in Gather ↗Explore fictional demo →

CHANGE ONE THING. LEARN WHAT MATTERS.

Three questions for the GTM team.

For an adult using LEGO Pick a Brick to extend an existing set, would starting with the set number or browsing by piece type lead to a more accurate parts list? Give both groups the same build requirement and compare chosen piece identifiers and quantities, not just confidence.

Set up this study →

For a LEGO builder with a prepared parts list, what would make uploading that list feel preferable to selecting each piece manually? Observe how people identify and resolve unavailable or mismatched pieces. Do not assume the system substitutes a compatible part.

Set up this study →

Would a proposed review step that groups a LEGO Pick a Brick basket by the builder's project help them catch missing quantities before ordering? Compare a prototype with the current basket. Treat project grouping as a proposed feature, not an existing LEGO capability.

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

US adults buying LEGO pieces for their own hobby projects, including builders extending an existing set and builders starting an original design. Include first-time and experienced Pick a Brick users. Recruit adults only and do not infer children's preferences from adult responses. Proposed audience; no segment size is claimed.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, observe adult builders completing a small, predefined parts-shopping task using the current US experience. Measure correct identifiers, colors and quantities, unresolved pieces and order readiness. Distinguish enjoying the search from having the pieces needed to complete the intended build.

Longer term · 3–12 months

Over 3–12 months, follow consenting buyers through receipt and project completion, then a later project if one occurs. Ask whether the ordering method is reused and whether missing or incorrect parts require another purchase. More pieces bought is not automatically a better building experience.

What would make the result actionable?

Use a checked reference parts list, observed task errors and opt-in order and delivery records. Account for stock changes, total charges and the user's prior parts knowledge. Compare finished projects and avoidable repeat orders, not just basket size; any revenue forecast needs observed incremental transactions and fulfillment costs.

A Gather simulation returns hypothetical customer reactions. Quantifying revenue, traffic or retention needs actual business inputs and validation against observed behavior.

Public sources

LEGO US Pick a Brick: piece search, filters and list upload ↗Current product page; checked 2026-09-20