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. Predictive accuracy and a statistical interval are only shown when established for the specific result. Treat findings as educated hypotheses to validate with real primary research, not as definitive measurements.

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Web delivery & performance analytics · PUBLIC RESEARCH BRIEF

VercelWhich performance cue tells a team what to fix before the next release?

Vercel currently documents Speed Insights for real-user Core Web Vitals and Web Analytics for pages, referrers, visitor attributes, custom events and feature-flag usage. The dashboards separate performance and traffic views. This brief studies prioritization across those signals; it does not claim that a faster metric causes conversion or that a dashboard recommendation is sufficient evidence to ship.

Updated 2026-10-08 · 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 Vercel Speed Insights shows a weak route, would an element-level priority list or a plain-language explanation of the metric better help a cross-functional team choose the next performance fix?

Set up this study →

Before a production release, would a preview-versus-production comparison or a change-centered performance diff better help engineers avoid acting on traffic and environment differences?

Set up this study →

When Web Analytics and Speed Insights move together, would a linked diagnostic view or a required competing-explanations checklist better prevent teams from treating correlation as causal impact?

Set up this study →

PROPOSED AUDIENCE

Who should weigh in?

North American web product, growth, engineering and marketing teams using or evaluating Vercel, including frontend developers, performance owners, product managers and growth analysts. Recruit teams with preview and production workflows and different traffic volumes. Proposed audience; no private deployment, log, analytics event or visitor record is included.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, test fictional Vercel projects with seeded route, device, country and environment patterns. Measure correct prioritization, interpretation of percentile metrics, separation of preview and production, and whether teams propose an observable verification step rather than claiming conversion impact.

Longer term · 3–12 months

Over 3–12 months, follow consenting teams in test or de-identified projects across releases, traffic shifts and device-mix changes. Examine whether selected fixes improve the intended field metric without degrading other routes and whether analytics interpretations stay calibrated. Business impact requires separate experiment or causal evidence.

What would make the result actionable?

Use versioned Vercel documentation, synthetic Web Analytics and Speed Insights fixtures with a hidden cause map, controlled performance regressions and release logs. Compare decisions with lab and field measurements, preserve environment and device context, and review privacy, accessibility and statistical assumptions.

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

About Vercel

Visit the company website ↗