The hidden cost of poor data quality in B2B market research.

Poor data quality costs far more than the line item you saved on sample. By the time the data is clean enough to defend, those initial savings are rarely worth it. The real bill arrives as extended fieldwork, resampled studies, decisions made on unreliable data and a research team whose credibility takes a hit.

Explore what bad data actually looks like, why B2B research is hit hardest and the four places the hidden costs of poor quality data show up once fieldwork is complete.

What does "bad data" mean in market research?

Bad data is any response that enters your dataset without a real, qualified human giving a considered answer. It appears in six common forms.

• Bot-driven and AI submissions. Automated scripts or AI models complete surveys instantly, mimicking human patterns to flood datasets with artificial feedback.
• Demographic and firmographic misrepresentation (screener gaming). Participants misrepresent their job title, location or background during pre-qualification screeners to match eligibility criteria and claim incentives. Misrepresented knowledge and experience can also happen when a respondent’s profile is outdated, listing an old job as a current one, or when they have experience in the desired industry but not at the desired seniority.
• Professional survey takers and farms. Organized individuals or click farms use multiple profiles, devices and VPNs or proxies to bypass geo-restrictions and harvest bulk rewards.
• Speeding and straight-lining. Respondents rush through questions or select the exact same rating column down a page to finish rapidly without reading.
• Duplicate submissions. The same human or source enters a survey multiple times using distinct throwaway emails or cleared browser cookies.
• Nonsensical or fabricated text. Participants type gibberish or unrelated text into open-ended boxes just to pass validation steps.

Fraud is often framed as a survey problem, but qualitative methodologies experience fraud, too. Qualitative participants in B2B studies can breeze through warm-up questions, but then struggle to answer experience-specific questions once the interview gets into the substance of the topic. This issue is cited in many studies, including interviews with “emergency nurses” who were unable to answer basic clinical questions to “investors” who weren’t able to define the S&P 500.

Though a qualified live moderator will usually catch this type of behavior, catching it still burns the moderator’s time and leaves a gap in the sample that has to be refilled.

Why is fraud such a problem in B2B market research?

Fraud is more prevalent in B2B market research than its consumer counterpart because the incentives are bigger and the respondents are harder to find.

B2B sample costs more because sourcing people with specific professional backgrounds and decision-making authority is genuinely difficult. Not many people purchase software for enterprise companies. Everyone uses toothpaste (at least we hope). Do you know any professional earning a decent salary who would take a 15-minute survey for $5? Would you? Larger incentives attract more fraud. The same $75 that makes a real IT decision maker willing to give you 20 minutes makes a fraudster willing to claim they are one.

Not all B2B sample is created equal, and you get what you pay for. Quality sample costs more than low quality sample. Marketing, product, strategy and research teams are all being asked to do more with less, and imperfect B2B respondents are cheaper and fill surveys faster than premium providers. Everyone’s bottom line is getting squeezed and paying a premium for quality is not always an easy decision to defend.

But do those savings stay savings? Costs creep up once you start digging into the true cost of low quality sample.

1. Collecting enough quality data can increase your timeline.

Less time in field means faster insights, which is an advantage for any business. Panels can fill studies quickly, but once cleaning starts, that advantage evaporates.

According to the second wave of the Insights Association 2026 Data Quality Benchmarking report, the post-survey cleanout rate in general B2B studies is nearly 60 percent. With average cleanout rates that high, the time required to reach a defensible sample climbs. You may have presented a two- or three-week timeline to your internal or client team that becomes a four- or five-week wait for clean data. That not only costs time, but it also costs reputation. Though increased timelines are frustrating for all involved, they do prevent a worse alternative: not catching quality issues before fieldwork ends.

Longitudinal work raises the stakes further. When a brand tracker starts returning suspicious results, you are not just delaying one deliverable. Poor quality puts historical data in jeopardy, potentially undermining years of benchmarks and every decision built on them.

2. Catch bad data quality after fielding, and the cost becomes resampling the survey.

If you catch poor data quality after fieldwork has closed, you are between a rock and a hard place. There are only three options and none of them is good.

1. Use the data anyway. It’s free, but a bad decision using that data will cost you more in the long run.
2. Scrap the study. You absorb the full cost of the research and still have no answer.
3. Pay to resample. You pay twice, and the timeline slips by weeks while stakeholders wait.

3. The cost of making the wrong decision is always a concern with poor data quality.

The most expensive outcome of poor data quality is making the wrong call. When a decision rests on faulty data, everything downstream, from marketing campaigns to product roadmaps, is in jeopardy of underperforming. Leaders rarely share these publicly, but it happens more often than we realize. According to the Greenbook 2022 GRIT report, 48 percent of buyers made at least one poor business decision due to sample quality or availability.

A study comparing verified and unverified sample shows exactly how the wrong business decisions can be made using bad data. Emporia and GfK ran the same research on IT decision makers through two routes: a validated expert network of 97 respondents and niche B2B panels of 300. The difference between the two produced a 45-point gap on brand familiarity, which would have led to developing an ineffective marketing campaign based on an incorrect market share assumption.

4. Lost social capital is the most painful cost of bad data.

The most painful cost of poor quality data will never show up in a company’s bottom line, but it will create a lasting impact. From missing a delivery deadline or disseminating faulty data, dealing with poor data quality chips away at your (and your team’s) credibility.

Once data cleaning starts during collection, you often learn that a survey that should’ve been in field for two weeks will now take three or four in order to meet your quotas with quality data. With every missed deadline, a little bit of trust is lost.

A dataset that does not pass the product or sales team’s sniff test does more damage still. It diminishes their trust in every future dataset the research function delivers, including the good ones.

How Emporia ensures the highest-quality B2B sample.

Quality sample is not a cleaning problem to be solved at the end of a study. It’s a sourcing decision made before you start fieldwork.

• 100% verified B2B respondents. Every participant is verified as a real professional in the role they claim, before they enter a study. Every healthcare provider is NPI-verified.
• Multiple layers of AI-powered anti-fraud technology. Bots, duplicates, farms, VPN and proxy use, screener gaming and fabricated open ends are screened across nine independent checks.
• Thousands of targetable intent signals. Recruit based on verified behavioral signal data, not just self-reported screener responses.

Reach out to learn more about how Emporia can help stop fraud from ever entering your dataset.

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Frequently asked questions.

What is poor data quality in market research?

Poor data quality is survey or interview data that does not come from a real, qualified respondent giving a considered answer. It includes bot and AI submissions, screener gaming, professional survey takers, speeding, straight-lining, duplicate entries and fabricated open-ended text.

How much survey data is typically removed for quality reasons?

Published benchmarks range widely. The Insights Association reported a global 59.6 percent post-survey cleanout rate among agencies in general B2B studies in H1 2026.

Why is B2B market research more vulnerable to survey fraud?

B2B respondents are harder to source and are paid substantially more than consumer respondents. Higher incentives attract more fraud, and screener gaming is the cheapest way for a fraudster to reach them.

Is cheap B2B sample actually cheaper?

Rarely. The savings on the invoice are offset by extended fieldwork, replacement waves, resampling costs and the risk of a wrong strategic decision that costs far more than the sample ever did.