Research sample quality · PUBLIC RESEARCH BRIEF
CintWhen is fraud protection enough to trust a sample?
Cint describes a research marketplace and a data-quality program that combines an AI-powered Trust Score, proprietary and third-party fraud controls, deduplication and human oversight. Screening can reduce known risks, but a passed session does not by itself establish representativeness, attentive responding or fitness for a particular research decision.
Updated 2026-10-29 · Simulation results not yet generatedCHANGE ONE THING. LEARN WHAT MATTERS.
Three questions for the GTM team.
How should a research buyer distinguish fraud reduction from representativeness, engagement and measurement quality when deciding whether a sample is fit for use?
Set up this study →When rejection or reconciliation rates change by supplier or subgroup, what evidence should determine whether the cause is traffic quality, survey design or an overly aggressive control?
Set up this study →PROPOSED AUDIENCE
Who should weigh in?
North American market-research, consumer-insights, sample-operations and data-quality leaders buying or managing online research sample through Cint. Include buyers, survey designers, supplier managers, analysts and fraud reviewers. Proposed audience only; no Cint account, respondent score, supplier record, survey or private marketplace data is included.
TWO TIME HORIZONS
Trial today. A habit tomorrow?
Near term · 0–90 days
Over 0–90 days, generate synthetic fieldwork with known bots, duplicate identities, inattentive humans, biased supplier mixes and legitimate rare behaviors. Compare automated thresholds, manual review rules and the downstream estimates each policy produces.
Longer term · 3–12 months
Over 3–12 months, vary fraud tactics, survey length, incentives, incidence and supplier composition. Study false positives, false negatives, subgroup exclusion and whether quality decisions remain stable as detection rules change.
What would make the result actionable?
Retain respondent- and supplier-level audit signals without exposing identities, and label known-good and known-bad evaluation cases independently of the control being tested. Report exclusion and error rates by relevant subgroup, then validate substantive findings in a separately sourced holdout.
A Gather simulation returns hypothetical customer reactions. Quantifying revenue, traffic or retention needs actual business inputs and validation against observed behavior.