[Webinar Recap] Ditch the Bad Data with Greenbook’s Lenny Murphy as Your Guide

The Challenge with Sample Quality

“There is a sample quality crisis in the industry. We’ve had it for years, but it could be ignored because there’s plentiful supply and cheap prices. But that has changed. Quality issues have become so extensive that it’s not unusual to throw away…30% of your sample on average, with 50-60% happening occasionally.” —Lenny Murphy

While sample quality issues have been a longstanding challenge, they have become more pressing due to current supply constraints. This issue has been exacerbated by an overemphasis on speed and cost efficiencies at the expense of data integrity. In a live poll, the majority of respondents said they experienced the following sample-related problems more than once in the past 6 months:

• Fell short of planned sample size
• Had to address doubts about quality
• Had serious doubts about research quality
• Missed important deadlines

Strategies for Improving B2B Sample Quality

 “It’s not just about the explosion of tools… Now we have this whole other component that is AI-driven, making the options even more complex. But there are still questions that are fundamental, like if you’re buying a car—you need to look at quality ratings, reliability, cost, upkeep, etc.” —Lenny Murphy

To combat sample issues, research leaders are using different strategies across the industry:

Alternative Sample Sources: Moving away from traditional panels, researchers are exploring new sample sources to improve quality. This includes partnering with companies that custom recruit respondents and speciality panels that are able to verify their professionals.

Increase Pressure on Sample Providers: Research leaders are starting to hold sample providers accountable for the quality of their deliverables. Leaders can ask providers questions like: Where do you source experts from? Are experts able to self-declare information? How do you verify their expertise?

Building Proprietary Panels: Some organizations are developing their own panels, which allows for greater control over the sample quality and ensures the data meets specific standards .

Automation and Fraud Detection Tools: Implementing advanced tools for automation and continuous quality assessment helps in detecting and eliminating poor-quality data early in the process. The use of sophisticated fraud detection services has seen a significant increase, providing an additional layer of security against data contamination.

Ditch the Bad Data

Don’t get stuck with research that’s not delivering results. Recognize the triggers for change and foster direct relationships with partners to drive better research outcomes for your organization.