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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Customer data activation & orchestration · PUBLIC RESEARCH BRIEF

TwilioWhich customer-data signal is trustworthy enough to activate?

Twilio's current Segment CDP page describes collecting real-time data into unified profiles, activating profiles across tools and channels, using flexible APIs, and applying data validation, observability and consent management. This brief studies the trust boundary between collecting a signal and allowing it to change a customer experience.

Sources checked 2026-09-26 · 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.

When Twilio Segment proposes a unified customer profile, would an identity-confidence summary or the underlying matched identifiers help a reviewer catch more incorrect merges before activation? Use synthetic identities and include ambiguous matches.

Set up this study →

For a real-time audience, would immediate activation with an exception log or a short review window for novel and sensitive conditions create the safer first operating model? Compare delayed experiences, false inclusion, duplicate actions and reviewer workload.

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Before a profile signal changes a message or support experience, would a visible consent-and-source panel or a simple eligible/ineligible label lead operators to make more appropriate decisions? Include revoked, conflicting and stale consent states.

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PROPOSED AUDIENCE

Who should weigh in?

North American digital product, growth, lifecycle-marketing and data teams evaluating or using Twilio Segment. Include CDP owners, data engineers, marketers, product managers, analysts, customer-support operators and privacy or governance reviewers. Recruit authorized adult employees. Proposed audience; no personalization or revenue outcome is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run controlled identity, audience and consent tasks with synthetic or approved de-identified events, sandbox destinations and fixed governance rules. Measure false merges, inappropriate inclusion, source inspection, consent errors, duplicate actions and correct suppression. Do not activate production destinations.

Longer term · 3–12 months

Over 3–12 months, follow approved pipelines as schemas, sources, destinations and consent change. Examine identity drift, broken tracking, audience instability, destination failures, observability use, deletion propagation and governance workload. Personalization or revenue claims require observed customer and business outcomes.

What would make the result actionable?

Use a versioned event taxonomy, synthetic identifiers, documented identity and consent rules, current destination permissions and independently adjudicated expected outcomes. Apply data minimization, least privilege and reversible sandbox tests. Simulated operator confidence is not evidence of identity accuracy, consent or business value.

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

Public sources

Twilio Segment CDP for unified profiles, activation, data quality and consent ↗Current product page; checked 2026-09-26