Let’s talk about data quality.
With bots getting smarter and AI agents gaming surveys better than ever, the difference between good data and garbage has never been harder to spot.
Here’s what actually works:
Make your screener smart, not obvious: Stop advertising who you’re looking for. Your screener should disguise your target, not hand fraudsters a roadmap to qualification.
Respect people’s time: Get your critical insights in 15 minutes or less. Your completion rates will improve and people will actually pay attention.
Build fraud detection everywhere: Block bots before they hit your survey. Build quality checks inside the survey. Auto-terminate speeders, straightliners, and attention check failures before they pollute your data.
Use multiple sample sources: Multiple sources mean less bias, faster fielding, and flexibility to cut bad suppliers the second you spot problems. Your best bet is to use a sample aggregator who manages multiple vetted sources for you and can easily add or substitute sources to protect data integrity.
Actually review soft launch data: Check survey logic, brand awareness patterns, revenue figures. Anomalies you catch in the first 50 completes are way easier to fix than ones you find after 500.
Get real about incentives: CEOs at Fortune 500 companies for $20? After everyone takes their cut, that respondent gets maybe $5-10. Does that make sense for someone making half a million? If completions roll in at that rate, something’s very wrong.
Work with experienced people: When red flags pop up Friday at 4pm, there’s no substitute for someone who’s seen it before.
Bottom line: When you cut corners on data quality, you’re risking every decision that gets made based on that data.
Want to talk about doing this right? Hit reply or send us a bid. We can help.