Beyond Self-Reported Profiling

How GDPR Data Makes Fraud Prevention Smarter

For years, market research has relied on profiling surveys to understand who a respondent really is. The idea is simple: ask people about their job, their household, their spending, their travel, and then store those answers for targeting future surveys.

The problem is almost all profiling is purely self-reported. Respondents know that the right answer gets them into more surveys and more rewards. It’s no wonder overclaiming is rampant. “Everyone is a senior manager.” “Everyone travels internationally twice a year.”

These exaggerations distort panels, inflate budgets, and compromise insights.

Most platforms have tried to address this with regular profiling question updates and smart fraud detection. But even with AI-driven trust scoring and server-to-server security, the question still remains one blind spot: what if the respondent is a real human, but lying? That’s where GDPR data exports change the game.

Profiling That’s Based on Proof, Not Claims

At SuperMarketer, we don’t ask people to tell us who they are. Instead, we use GDPR portability rights to request data directly from the platforms where people already spend their digital lives. Respondents authorise the export, and we transform that raw data into validated profiling attributes.

Here’s what that looks like in practice:

• LinkedIn: Confirms job title, company, industry, and seniority by cross-checking against a live professional profile.

• YouTube: Shows which brand videos, tutorials, or product demos a respondent actually watched which is a stronger signal of intent than a claimed interest.

• Booking.com: Confirms travel frequency, accommodation types, destinations.

• Amazon and Google (coming soon): Soon we will be able to study purchase history, search interest and browsing categories.

Together, these streams of data build dynamic, fraud-resistant profiles that evolve with the respondent’s actual activity.

The Missing Layer in Trust Scores

Most survey platforms utilise AI and machine learning to flag fraud signals, duplicate entries, bot-like behavior and VPN manipulation.

But even the best technical defenses can’t detect all bots off of technical fraud signals alone or whether a real person misrepresents themselves. That’s where our tools come in:

Bot Score: Derived from Google/LinkedIn account signals like account age and activity patterns.

Identity Verification: Ensures the details provided during the survey match the respondent’s online record

Behavioural Match Score: Compares survey responses with actual online behaviors to highlight inconsistencies that suggest overclaiming or deception (searches, videos watched, bookings made).

Most fraud prevention is after the fact, after you’ve reconciled your buyers, not proactive. GDPR-powered profiling lets us flip the script: start with proof of who the respondent is, and build everything else from that foundation. By layering validated digital profiles into existing fraud prevention systems the industry can close the gap between “real vs fake” and “truth vs exaggeration.”

At SuperMarketer, we’re building the tools to make that possible. If you’re leading the charge on data quality and want to explore how this approach fits with your fraud framework, let’s connect.