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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Customer feedback & workflow automation · PUBLIC RESEARCH BRIEF

AlchemerWhich feedback deserves an automated response?

Alchemer describes a feedback platform spanning surveys, digital feedback, analytics and workflows. Its help documentation describes a no-code workflow engine that connects feedback to teams and supports building, testing, publishing, monitoring and reporting. Routing feedback into action raises a separate governance question: which signals are sufficiently clear, current and consequential to trigger a workflow?

Updated 2026-10-20 · Simulation results not yet generated

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CHANGE ONE THING. LEARN WHAT MATTERS.

Three questions for the GTM team.

For Alchemer, which feedback conditions should trigger an automated case, a human review queue or no action: severity, recurrence, identity confidence, consent, account context or response quality?

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When survey feedback, a public review and operational data disagree, what evidence should a workflow expose before assigning an owner or priority?

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How should teams test workflow changes so that a new survey question, integration or classification rule does not silently alter who receives follow-up?

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PROPOSED AUDIENCE

Who should weigh in?

North American customer-experience, product insights, research operations, support operations, marketing operations, analytics and governance teams evaluating Alchemer. Include workflow builders, response owners and people responsible for survey quality. Proposed audience only; no Alchemer response, contact record or customer system is included.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run synthetic feedback through versioned workflows containing duplicates, ambiguous sentiment, missing consent and conflicting context. Compare correct routing, false escalation, missed cases and explanation quality without sending messages.

Longer term · 3–12 months

Over 3–12 months, follow approved rules across channel changes, staffing transitions and survey revisions. Study alert fatigue, ownership gaps, rule drift and whether closed-loop actions are actually recorded.

What would make the result actionable?

Use synthetic feedback with known labels, provenance, timestamps, consent states, duplicate markers, workflow versions and delivery logs. Any customer or financial impact claim requires observed outcomes, appropriate comparison groups and the full cost of follow-up.

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

About Alchemer

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