Key points
- B2B incidence runs 6 to 35 percent against 50 to 80 percent for consumer, which is why a B2B complete costs five to ten times more.
- Consumer fraud is identity fraud that device and bot checks catch. B2B fraud is a real person claiming a role they do not hold, which only an employment check catches.
- Senior B2B respondents stop finishing near 15 minutes, so a long questionnaire quietly changes who is in your sample rather than just costing more.
- No single B2B panel is deep enough for senior audiences, so B2B sample is nearly always blended whether or not the provider says so.
- Consumer cost per complete is quoted and held. B2B cost per complete is often quoted optimistically and revised in field.
What kinds of survey fraud are there?
Identity fraud is one person or bot pretending to be many, or someone outside the target country. Claim fraud is a real, unique person selecting a job title, seniority or company size they do not have. The first is well understood and mostly caught at entry. The second is what breaks B2B studies, and it passes every consumer grade check because there is nothing wrong with the device, the network or the account. Only the answer is false.
What are the red flags in a single response?
- Seniority inflation. A "VP" at a company they report as having 30 employees. Possible, but rare enough to check.
- Everything qualifies. A respondent who ticks every decision area in a select all that apply question, has authority over every budget, and uses every product on a list. Genuine decision makers have narrow remits.
- Company size given as a round number that fits the screen. "500" when the qualifying threshold is 500. We ask for a number and check it against the named employer.
- Category knowledge that is generic. Asked which ERP they use, a real controller names one. A fake one writes "Microsoft" or "the cloud."
- Open ends that could be about anything. Fluent, on topic, and empty. Increasingly generated by tools rather than typed.
- Speed. Finishing a 14 minute survey in 5. Standard check, still worth keeping.
- Straightlining and pattern answers on grids, especially combined with any of the above.
- Inconsistency across the survey. The stated title at Q3 and the responsibilities described at Q20 do not describe the same job.
What are the red flags across the whole dataset?
- Incidence in field far above what the audience supports. If 35% of starts qualify as finance leaders, a third of them are not.
- Suspiciously even distributions across seniority, size or industry, when the population is not even.
- Brand awareness of 90%+ on obscure vendors. Real decision makers do not know every company on your list.
- Completes arriving in bursts at odd hours for the target country.
- Very high satisfaction and very high intent to purchase across every brand. Professional respondents are agreeable.
What actually prevents survey fraud?
Verification before delivery, in layers. Identity checks catch duplicates and bots. Employment verification through corporate domain or a professional record catches title inflation. A category knowledge question, reviewed by a person, catches the rest. Then a rejection log so the buyer can see what was removed. This is how we field every study.
What should you ask your sample vendor?
- How do you verify job title and company size, specifically?
- What share of raw completes did you reject on the last three studies for this audience?
- Will I get a log of rejections and replacements with the data?
- Do you review open ends before delivery, and who does it?
- What is your policy if I find respondents after delivery who fail your own criteria?
The second question is the useful one. A vendor with real verification has a rejection rate and knows it. A vendor without one will describe their process instead of giving a number.
Survey fraud: frequently asked questions
What is the most common type of fraud in B2B surveys?
Claim fraud: a genuine, unique respondent selecting a job title, seniority or company size they do not have. It passes identity checks because nothing about the person or device is fake. Only the claim is, and only checking the claim catches it.
Do attention checks catch B2B fraud?
They catch inattention and some bots. They do not catch a real, attentive person who is not a CFO. Attention checks are necessary and nowhere near sufficient for decision maker audiences.
What rejection rate should I expect from a vendor who verifies?
Eight to fifteen percent of raw completes for most B2B audiences, higher for C-suite. A vendor reporting near zero rejections on decision maker sample is not looking.
Can open ends written by AI be detected?
Partly. Generic, fluent, on topic but empty answers are the tell, and a category knowledge question with a specific correct answer defeats most of it. Human review of open ends before delivery is the reliable check.
Send the spec. Get real numbers back.
Audience, market, target n, expected interview length. Feasibility the same business day for standard audiences, with reachable counts, an estimated qualification rate we believe and a quoted cost per complete that stays fixed for the agreed brief.