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.

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Lodging discovery & trust signals · PUBLIC RESEARCH BRIEF

AirbnbWhich trust signal helps a traveler decide without over-reading the badge?

Airbnb's help center says Guest favorites identify well-loved homes using ratings, reviews and reliability factors, and that guests can filter for them. It also explains that eligible listings may show top-home highlights and percentile labels. This brief studies how travelers interpret those signals alongside the listing itself; it does not treat a badge as a guarantee.

Sources checked 2026-09-22 · Simulation results not yet generated

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CHANGE ONE THING. LEARN WHAT MATTERS.

Three questions for the GTM team.

When comparing similar Airbnb listings, would a Guest favorites badge alone or a short explanation of the ratings, reviews and reliability factors behind it help travelers choose with fewer false assumptions? Keep price, location and amenities constant and test what participants believe the badge guarantees.

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Would a top-home highlight or a concise set of recent review themes better help an Airbnb guest build a shortlist for a specific trip? Use real, authorized listing information and score whether the traveler notices trade-offs that matter to the stated trip.

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If a Guest favorite conflicts with one important listing detail or recent review, what explanation helps the traveler weigh the badge against that evidence? Test a realistic mismatch without inventing an incident and allow the appropriate outcome to be not booking.

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PROPOSED AUDIENCE

Who should weigh in?

US and Canadian adults planning a leisure trip who are comparing at least three whole-home or room listings on Airbnb. Include first-time and repeat users, travelers with different budget constraints and people who read reviews deeply or mainly use filters. Recruit adults only. Proposed audience; no booking share is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run controlled listing-comparison tasks using current public listing details with permission where required. Measure accurate badge comprehension, useful shortlist quality, review engagement, qualified booking intent and later booking completion. Faster selection is not success if important constraints are missed.

Longer term · 3–12 months

Over 3–12 months, follow consented travelers from search through completed stays and subsequent booking behavior. Examine expectation gaps, cancellations, support contacts, review outcomes and whether travelers continue using the signal appropriately. Do not attribute experience quality to the badge without controlling for listing and trip differences.

What would make the result actionable?

Use current eligibility guidance, authorized listing snapshots and consented behavioral events. Independent reviewers should assess whether choices match each traveler's stated constraints. Any conversion or repeat-booking claim requires observed bookings, stays, cancellations and contribution after refunds and support costs; simulated trust is not evidence of a safe or satisfactory stay.

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

Public sources

Airbnb Guest favorites, highlights and eligibility factors ↗Current product page; checked 2026-09-22