DATA QUALITY CRISIS
One in three survey responses is fake. Your current defenses don't catch them.
For every $1M in panel spend at a 38% discard rate, $380,000 buys garbage data. CAPTCHAs cost $0.01 to defeat. Trap questions are pattern-matched by LLMs. Device fingerprinting catches less than half of organized fraud. The truth gap is invisible — until your product launch fails.
The fraud crisis in detail
These are not estimates. They're measured rates from industry audits, panel analysis, and published research.
Four types of fraud. Your current tools catch at most one.
AI Bot Respondents (Critical)
GPT-4 and Claude can complete surveys indistinguishably from humans. They pass attention checks, give coherent open-ends, and provide internally consistent responses. Traditional detection is powerless.
Survey Farms (High)
Organized operations with rows of devices, each completing surveys for incentive payments. Workers are real humans, so behavioral checks pass. But the responses are mechanical and incentive-driven.
Duplicate Participants (High)
The same person completing surveys across multiple panels under different identities. Cross-panel deduplication barely exists. One person can appear in your sample 5-10 times.
Demographic Fraud (Medium)
Respondents lying about their demographics to qualify for higher-paying surveys. A 22-year-old student becomes a 45-year-old household decision maker. Your targeting is meaningless.
DETECTION COMPARISON
Every traditional defense has a bypass. VerifyHuman doesn't.
Partial means 'catches some, misses most.' Check marks mean 'reliably detects.'
| Capability | AI bots | Survey farms | Duplicates | Demo fraud | LLM synthetic |
|---|---|---|---|---|---|
| CAPTCHAs | |||||
| Trap Questions | partial | ||||
| IP Blocking | partial | partial | |||
| Device Fingerprint | partial | partial | |||
| Speeder Detection | partial | ||||
| VerifyHuman |
Bad data doesn't announce itself. It just leads to wrong decisions.
Wrong Product Launches
Contaminated concept testing data leads to launching products that fail. 70-90% of CPG products fail despite market research — fraud is a contributing factor.
Wasted Ad Spend
Pre-test data contaminated by bots and speeders greenlights ineffective creative. Millions in media spend on ads that tested well but perform poorly.
Eroded Trust in Research
When research fails to predict outcomes, stakeholders lose confidence in the function. Budget cuts follow. The fraud problem is invisible but the consequences aren't.
Panel Degradation
Fraud incentivizes more fraud. As word spreads that panels are easy targets, organized fraud operations scale up. The problem compounds over time.
Three layers of data quality protection
01 — VerifyHuman Behavioral Verification
Passive analysis of involuntary behavioral signals confirms a real human is present. Not CAPTCHAs. Not IP filtering. Behavioral liveness detection in 3-5 seconds.
02 — Geometric Fingerprinting
128-dimensional facial geometry vector identifies duplicate participants across studies without storing PII. One person, one response — across your entire research program.
03 — RDIT Random Sampling
When you source respondents through RDIT, you get fresh, unconditioned participants. No professional survey-takers. No panel loyalty gaming.
Find out what percentage of your data is actually real.
At a 38% discard rate, you're paying $380,000 per million for garbage data. Run VerifyHuman alongside your next study. Compare verified vs. unverified responses. The truth gap will tell you everything.