When data lies: The hidden crisis in market research

Hey there! 👋

Sharekh here! Welcome back to The Research Mag—your monthly dose of sharp insights, evolving trends, and the tough questions shaping the future of market research.

Before we get into this month’s issue, let’s quickly rewind to last time.

🔍 Quick recap

Last time on The Research Mag, we launched something new—our podcast, Research Decoded with Sharekh—with a hard-hitting first episode on AI in market research.

Here’s what we explored:

• AI-moderated interviews are fast, but still lack the emotional nuance and flexibility of human moderators.

• Market research fraud is rising—with people using AI to fake expertise and generate believable (but false) responses.

• The real opportunity? Using AI as an assistant, not a replacement—especially for automation, fraud detection, and processing at scale.

• If we don’t build smarter verification systems now, research teams risk losing trust in their own data.

We unpacked it all with Stacy Thomas and Angie Stahl from GRRR. If you missed it, you can catch the full episode here.

🚀 What’s New?

Last time, we tackled the rise of AI and what it means for the future of insights. But this time, we’re turning the spotlight on something just as critical—and maybe even more urgent.

What if the data itself can’t be trusted?

This issue, we dig into the hidden crisis in market research: bad data, fake respondents, and eroding confidence in research outcomes. Because when your insights aren’t real, your decisions won’t be either.

When data gets harder to reach

Let’s be real—data might be everywhere, but getting it the right way? That’s becoming tougher by the day.

Between stricter privacy laws, the death of third-party cookies, and rising consumer skepticism, researchers are no longer just data collectors. They’re becoming stewards of trust.

In this issue of The Research Mag, we’re digging into how the research world is shifting gears—not just to comply with the rules, but to completely rethink how we collect, manage, and protect information.

Here’s what we’re unpacking:

• Why traditional data pipelines are slowing down—and why that might actually be a good thing

• How consent, transparency, and responsible usage are becoming non-negotiable

• What privacy-first innovation looks like: think contextual targeting, zero-party data, and synthetic alternatives

• What this means for the future of decision-making in research-driven teams

Let’s dive in.

The death of “easy” data—and why researchers need to care

There was a time when tracking users through third-party cookies and massive data panels was the default. It made research fast—but not always ethical.

Now? The game’s changed.

Here’s how:

• Third-party cookies are disappearing -

  Google Chrome is now following Safari and Firefox in removing support for third-party cookies, marking a major shift in how user data is tracked across the web.

  Read more about it → Google Privacy Sandbox

• Global privacy laws are tightening -
  From the GDPR in Europe to CCPA in California, regulations are reshaping how data can be collected, stored, and used—with more regions following suit.

• Browsers are getting smarter about privacy -
  Apple’s Intelligent Tracking Prevention and Firefox’s Enhanced Tracking Protection are blocking many standard tracking methods.

According to a 2024 survey by Statista, 43% of marketers said new data privacy regulations are already having a significant impact on how they approach digital advertising and data collection.

So what now?

It’s not just about compliance—it’s about consent and trust. Researchers must shift from passive tracking to approaches where people know why their data is being collected, and what’s in it for them.

What does “value exchange” actually mean?

Say you’re running a study for a product launch. Instead of burying a data consent clause in legalese, tell participants what their input will shape—a new feature, a better user experience, or even early access perks. Be clear. Be fair. That’s value.

In short?

Data access is becoming harder. Trust is becoming more valuable. Researchers who adapt to that reality will thrive.

What happens when respondents stop being real?

Let’s be honest—these days, getting a “response” doesn’t always mean it came from a real person.

And that’s not an exaggeration.

📊 According to Research Defender, up to 35% of responses in online research are either low-quality or outright fraudulent.
Add AI-generated open-ends, click farms, and identity spoofing into the mix, and what you end up with is a dashboard full of fiction.

Here’s how fraud is showing up today:

• Identity spoofing is rampant → Fraudsters pose as qualified B2B participants, sometimes using AI-generated LinkedIn profiles or scraped credentials to pass screeners undetected.

• Survey farms are thriving → Entire online communities now exist to help people “hack” research platforms for incentives. (You’ll find Reddit threads and Discord servers with step-by-step guides.)

• AI-generated responses are harder to catch → Many read like thoughtful, articulate answers—until you realize they’re stitched together from LLM prompts, not real experience.

The most dangerous part? These responses look great in your dashboard—until your product fails because the insight behind it was fake.

This kind of fraud doesn’t just waste research budgets—it erodes trust in the entire process. When your insight team starts second-guessing every dataset, strategic confidence nosedives.

That’s why fixing things after the data comes in isn’t enough. Research teams need smarter upfront safeguards:

• Identity verification (yes, even in B2B)

• Behavioral quality checks

• In some cases, live validation for high-stakes studies

  

  When the dashboard looks perfect, but your insights are built on fake data

The slow death of third-party data (and what’s replacing it)

Third-party data used to be the go-to for targeting, segmentation, and audience insights. But that era? It’s fading—fast.

Thanks to privacy regulations like GDPR and CPRA, browser changes (looking at you, Google), and increasing consumer distrust, marketers and researchers are losing access to the cookie crumbs they’ve depended on for years.

And this isn’t just a marketing problem.

It’s a research problem, too.

When third-party data dries up, so do a lot of easy assumptions. Behavioral benchmarks, targeting criteria, even recruitment pipelines for studies—all get messier without passive tracking.

So what’s taking its place? Researchers are moving toward:

• Zero-party data → Voluntarily shared by the participant (preferences, intent, motivations)

• First-party data → Collected directly from user behavior (with consent)

• Contextual insights → Gained from smart, in-the-moment research rather than passive tracking

The upside? You’re getting real signals from real people, not inferred guesses from shady data brokers.
The downside? It’s slower, more intentional, and requires better design.

But here’s the big opportunity:
When companies take the time to ask the right people the right questions—instead of just scraping behaviors—they don’t just comply with privacy laws. They build trust.
And trust scales.

🛠️ 3 ways to strengthen data quality today

So, how do you fight the flood of bad data? Here are threestrategies your research team can start using right now:

1. Flip the screener -
Use reverse-screening logic—design questions that intentionally catch contradictions or fake experience. Think of it as a truth test for your respondents.

2. Verify, don’t assume -
Add identity verification layers—tools that match LinkedIn profiles, check for consistent digital footprints (IP/location), or analyze behavioral signals in real time. Yes, even for B2B.

3. Spot the weird stuff early -
Look for patterns: survey speeders, generic or gibberish open-ends, copy-paste answers. Flag them before they skew your insights.

💡 It’s not about perfect data. It’s about setting a higher bar for what makes it into your dashboard.

What trustworthy research looks like now

Let’s be honest—“trust” in research doesn’t come from the cleanest dashboard or the prettiest pie charts.

It comes from knowing who your participants are, how the data was collected, and why the insights actually reflect reality.

In 2025, trustworthy research means more than just asking good questions—it’s about asking the right questions to the right people, and being sure they’re real.

So what does good look like today?

✅ Participant validation built in—not bolted on
Tools like Research Defender and Lucid Impact Measurement are leading the way in real-time respondent verification—flagging duplicates, bots, and suspicious behavior before it hits your dataset.

✅ AI-assisted analysis, not AI-generated data
- Using LLMs to sort through transcripts? Smart.
- Using LLMs to replace transcripts? Risky.
- The best teams use AI as an amplifier, not a stand-in.

✅ Smaller samples, deeper conversations
The industry is moving away from “more = better.”
Hyper-targeted sampling, paired with real qualitative depth, is leading to richer, more reliable insights.

✅ Transparency in methods and incentives
More teams are now sharing how their studies were run, who they included, and what incentives were offered. That’s not just good ethics—it’s good business.

So, is trust in research broken—or just evolving?

Let’s face it—between privacy pushback, respondent fraud, and AI chaos, it’s tempting to say market research is losing its edge.

But here’s the truth:

It’s not broken. It’s just evolving. Fast.

And that evolution is pushing researchers to get sharper, think deeper, and build smarter systems of trust.

We can’t go back to the days when panel-based quant ruled all, or when identity verification was “just a nice-to-have.” The new era demands better. Not more research—but better, cleaner, and more credible research.

So, what can we do?

→ Rethink what data quality really means
→ Stop over-indexing on quantity
→ Invest in tools that prioritize validation and integrity
→ Ask better questions, and build smarter blends of qual + quant

The researchers who rise now won’t be the ones who know the most tools.
They’ll be the ones who know who to trust—and how to verify it. Let’s make sure we earn that edge.

💸 What’s the ROI of better data?

Bad data isn’t just a research problem—it’s a business risk. A few hours saved during data collection can cost companies millions in bad product decisions, misaligned strategy, or flawed GTM efforts.

On the flip side, investing in fraud detection and identity checks upfront helps research teams avoid costly blind spots—and build credibility inside the org.

Wrap-Up: What’s next for researchers?

If this issue made you feel like research is getting harder—good.
Because it is.

But here’s the upside: it’s also getting smarter.

The challenges we’re facing—privacy walls, fraud, AI noise—aren’t signs of decline. They’re signals that research is becoming more central, more scrutinized, and more strategic than ever before.

It’s not just about gathering data anymore. It’s about proving it’s real, proving it’s reliable, and proving it’s worth acting on.

The future of research isn’t just about scale. It’s about trust.

So here’s a question for you:

When was the last time you really trusted the data you collected?
If the answer isn’t “last week,” it might be time to look deeper.

Let’s keep building, testing, verifying—and yes, trusting—better.

That’s a wrap for this issue of The Research Mag!

💭 What’s your take?

Have you noticed growing gaps in data trust, or faced any challenges validating your sample quality lately? Hit reply—I’d love to hear what you liked about this newsletter. Do reach out to me here.

Got an idea for a future topic? Let me know! Let’s keep the conversation going.

Sharekh,
The Research Mag
Founder @CleverX
Connect with me on X and LinkedIn