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 service case orchestration · PUBLIC RESEARCH BRIEF

ServiceNowWhich customer case gets resolved without creating another handoff?

ServiceNow's Customer Service Management page describes self-service, an integrated Agent Workspace, AI-generated case summaries and recommended actions, cross-department case management and automated workflows. This brief studies how service teams route and resolve complex cases with accountable human review; it does not assume that AI output is accurate or that deflection is a customer benefit.

Sources checked 2026-09-23 · 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 a complex ServiceNow CSM case, would an AI-generated summary or a structured timeline of verified customer and fulfillment events better help the receiving agent continue without repeating discovery? Compare factual omissions, corrections and customer questions repeated.

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Would routing a ServiceNow case from a recommended next action or from an explicit ownership-and-dependency map produce fewer avoidable handoffs? Use authorized historical patterns or synthetic cases and score correct ownership and recovery from an incorrect suggestion.

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For an eligible service request, would beginning with conversational self-service or offering a visible path to an Agent Workspace first create a more appropriate resolution? Measure successful completion, abandonment, escalation quality and customer effort rather than deflection alone.

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PROPOSED AUDIENCE

Who should weigh in?

North American customer service organizations evaluating or operating ServiceNow CSM for multi-channel, cross-department cases. Include frontline agents, supervisors, knowledge owners, back-office fulfillers and service-operations leaders across different case complexity and automation maturity. Recruit authorized employees and use adult customer scenarios. Proposed audience; no productivity or satisfaction change is implied.

TWO TIME HORIZONS

Trial today. A habit tomorrow?

Near term · 0–90 days

Over 0–90 days, run controlled case simulations with synthetic or approved de-identified records and the current configured workflows. Measure summary accuracy, correct ownership, handoffs, safe escalation, resolution completeness and customer effort. Include adversarial and ambiguous cases and prohibit autonomous external action during research.

Longer term · 3–12 months

Over 3–12 months, follow approved workflow variants across comparable case types. Examine reopened cases, repeat contacts, knowledge corrections, cross-department delay, automation overrides, agent workload and verified customer outcomes. Any cost, resolution-time or satisfaction claim requires observed operational data and should account for shifted work.

What would make the result actionable?

Use current configuration, approved case taxonomy, de-identified or synthetic records, knowledge-owner review and independent adjudication of the correct route. Protect customer and employee data, log model and workflow versions and require a human owner for consequential actions. Simulated completion is not evidence of real resolution or customer satisfaction.

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

Public sources

ServiceNow Customer Service Management product overview ↗Current product page; checked 2026-09-23