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Even with written screeners, compliance checks, and vetting interviews, standard Expert Network verification still boils down to one fundamental flaw: it relies entirely on self-reported data. The Verification Gap The standard vetting process at most expert networks involves three things: Reviewing a LinkedIn profile Running a short screener And asking the individual to self-certify their experience. This approach has two fundamental weaknesses. First, it confirms what someo

When people hear that we work with LinkedIn data, they sometimes assume we must be scraping it. It’s a fair assumption, LinkedIn scraping has existed for years, and many companies still rely on it to build large “verified” databases. But that’s not what we do at SuperMarketer. Scraping means using automated bots to collect publicly visible information from profile pages. These tools gather names, job titles, companies, and education details without the user’s knowledge or con

The Data Quality Dilemma In market research, most data still begins with self-reporting. Respondents describe who they are, what they buy, and how they behave. That approach has worked for decades, but it also brings familiar problems: incomplete profiles, inconsistent answers, and long hours spent cleaning data before it can be used. Every study starts with the same friction. Participants re-enter the same information. Panels struggle to keep data fresh. Analysts spend weeks

How GDPR Data Makes Fraud Prevention Smarter For years, market research has relied on profiling surveys to understand who a respondent...