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      Rep Data Launches Market Research Industry's First-Ever "No-Fly" Suppression List to Eradicate Bad Actors

      Rep Data

      News provided by

      Rep Data

      Jun 25, 2025, 08:53 ET

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      New suppression list blocks repeat fraudsters across sources, bringing lasting accountability to sample quality

      NEW ORLEANS, June 25, 2025 /PRNewswire/ -- Rep Data, the industry's leading provider of high-quality research data and fraud prevention solutions, today announced the market research industry's first-ever "No-Fly" list.

      This list ensures the permanent exclusion of all known bad actors from entering or re-entering any survey, when run through Rep Data's Research Defender.

      "No one outside of our team has a clue how many times these same bad actors are popping up across the same sample sources over and over again, regardless of which source you're leveraging. The No-Fly list is a win for all researchers," said Patrick Stokes, Founder and CEO of Rep Data.

      Rep Data is uniquely able to create and maintain this list thanks to its ability to see and record 3 billion survey scans in the last 12 months across nearly 200 sample sources, with approximately 15 million more scans coming in each day.

      Vignesh Krishnan, CTO at Rep Data added: "Even if a respondent is taking your survey through a first-party source, you might not know that it's the 267th survey of their day, because it's a bad actor equipped to game the system to reap incentives at high volumes. You also might not know that they've already answered that same survey in three other sources that happen to share samples behind the scenes due to supply shifts. The industry is messy, and the No-Fly list addition to Rep Data's Research Defender fraud prevention suite helps researchers clean it all up for their projects, permanently."

      To showcase the necessity for such an industry-wide suppression list, Steven Snell, PhD, Head of Research at Rep Data, conducted a study using 1,928 respondents from a variety of prevalent sample sources. Rather than blocking low-quality respondents with Research Defender as is standard practice, he allowed them in, mirroring the typical researcher experience when purchasing sample from these sources.

      The findings were alarming: 31% of responses were fraudulent, and worse yet, 26% were classified as "good-looking fraud" meaning there would have been no way to detect the poor quality through either human-supervised or AI-focused data cleaning.

      "At Rep Data, we set the bar for quality with continuous development of Research Defender, the leading product in survey fraud detection," added Stokes. "Just a few weeks ago, our team spotted a rise in a new fraud trend accounting for about 0.3% of traffic across the entire survey ecosystem, mixing and matching IP addresses and device identifiers to be able to batch enter the same survey, and we mitigated the risk immediately. No one else is doing this. Now, we're taking it a step further by blacklisting bad actors permanently with our No-Fly list."

      About Rep Data
      Rep Data is a technology-driven data and insights partner, helping insights professionals streamline insights initiatives with a focus on data quality and consistent execution. Partnering with market research agencies, consultancies, Fortune 500 corporations, advertising and brand strategy firms, universities, and public relations agencies, Rep Data delivers real, reliable, and fraud-free research data at scale. Its solutions include: Research Desk, a centralized DIY sampling platform that enhances efficiency and data quality; and Research Defender, an advanced fraud prevention system that conducts 2.9 billion security scans annually to protect data integrity. Through tech-enabled services, Rep Data provides expert support to help organizations scale with confidence. Committed to high-quality data and innovative tech-enabled solutions, Rep Data is redefining market research to drive faster, more reliable insights. For more information, visit www.repdata.com.

      SOURCE Rep Data

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