30–50%
Survey Fraud
Of unvalidated panel responses contain fraud, bot-generated content, or low-quality data that would corrupt findings if shipped as-is.
Industry research · ESOMAR benchmarks
Bad data doesn't look bad — that's what makes it dangerous. The Validation Intelligence Loop is a six-stage quality architecture that catches what surface-level quality checks leave behind.
Most datasets look fine on delivery. Weeks later, when the strategy built on them starts missing, the cost is already locked in. Three industry benchmarks for why validation is not optional.
Of unvalidated panel responses contain fraud, bot-generated content, or low-quality data that would corrupt findings if shipped as-is.
The cost of a misinformed strategic decision based on compromised research — category entry, launch pricing, positioning call gone wrong — is rarely small, and rarely recoverable.
Reduction in the predictive accuracy of research findings when fraud goes undetected — enough to flip the direction of any close-call recommendation.
Every Robin & Berry dataset passes through a sequential quality architecture - each stage designed to catch what the previous one cannot. By the end, only decision-grade data survives. Full six-stage Loop applies to Robin & Berry full-service engagements. Sample-only, API, and self-serve projects use a tailored subset - confirmed in the scope of work.
Digital fingerprinting, duplicate IP detection, device type validation, geo-verification, incognito mode and VPN detection at the point of entry. No unverified respondent enters the survey.
Entry GateStraight-liner detection, speeder flags, internal consistency traps, attention check questions, and behavioural signals like right-click, inspect element, alt-tab, and print-screen detection.
Flags: ~12%Precision alignment between the respondent’s profile and the study’s targeting objectives. AI-powered sampling ensures each respondent is genuinely qualified — not just screener-compliant.
AI VerifiedBehavioural patterns audited end-to-end — inconsistency traps, context mismatch, engagement depth, and response coherence scored before release.
97.4% CleanGibberish detection, copy-paste flagging, AI-generated response identification, pattern matching, minimal effort scoring, duplicate text removal, one-word answer filtering.
Removes: ~3%Wave-over-wave drift detection, sentiment shifts surfaced from OE responses, cohort-level pattern emergence, and anomaly flags raised before they distort the read.
Signal LockedQuality control has three windows — and most firms only use one. We run checks at all three, because each window catches a different class of problem. Post-hoc cleaning alone is the category default, and it's not enough.
Our fraud detection architecture operates across four distinct layers — each catching a different class of bad actor. Together, they eliminate the noise that corrupts research findings.
Free-text answers reveal patterns structured data can't — one of several layers in our quality stack. Our OE Fraud Evaluator runs every open-ended response through eight detection algorithms before it reaches your dataset.
Beyond standard duplicate IP checks, our environment monitoring layer captures the behavioural signals that bots, click farms, and motivated bad actors leave behind — even when they try not to.
Real-time detection of out-of-country responses, GPS spoofing, and VPN routing.
Flags respondents who open browser dev tools — a key indicator of manipulation attempts.
Detects when respondents switch tabs during the survey — copying answers from other sources.
Restricts survey completion to specified device types — preventing emulator and scripted access.
Unique device signatures prevent the same user completing via multiple browser sessions.
Identifies print screen attempts — protecting proprietary research instruments from leakage.
We don't ask you to trust us. Every Robin & Berry dataset is delivered with a Quality Certificate — a full audit trail of each validation stage's performance metrics, scoped to the stages applied.
Average performance across recent Robin & Berry datasets — pulled from the Validation Intelligence Loop’s real-time scoring. Every value below is what we ship a dataset against, not what we aspire to.
See exactly what our Validation Intelligence Loop would catch in your current data pipeline — before bad data costs you a bad decision.