Qualtrics announces new AI capabilities to improve the quality of research insights

Qualtrics, the leader in experience management, announced the launch of ExpertReviewTM – Response Quality. Powered by artificial intelligence (AI), this new solution empowers market researchers and insights professionals to weed out poor quality feedback, allowing organisations to make critical business decisions with more accurate data.

“Acquiring high quality feedback is one of the biggest challenges in market research. Low quality responses can make up as much as 20 percent of any given study,” says Kelly Waldher, Vice President, Qualtrics Research Core and Brand Experience. “Qualtrics ExpertReview uses AI to automatically detect and remove poor quality responses with the click of a button, increasing researchers’ trust and confidence in their data.”

Built on the Qualtrics XM Platform™, ExpertReview – Response Quality automatically detects bad actors, bots and disengaged respondents, and provides recommendations to address the problem, including deleting all poor-quality responses with a single click. Top brands are already using ExpertReview to run more accurate research studies, leading to smarter insights and better results. In addition, sample providers like Lucid, the world’s largest platform for survey respondents, partner with Qualtrics to provide a cohesive and secure survey experience.

“Establishing data quality standards is one of our primary initiatives at Lucid,” says Courtney Williams, Executive Director, Quality at Lucid. “ExpertReview is elevating quality, not only within our industry but also in the many areas of business that Qualtrics reaches. We’re proud to partner with Qualtrics and see the impact that ExpertReview delivers to the world of data.”

ExpertReview Response Quality allows researchers to:

• Automatically detect invalid or low-quality responses coming from problematic sources such as bots, duplicates, cheaters, speeders, straightliners and gibberish.
• Improve the quality of insights by removing low quality responses from the final analysis.
• Avoid future quality gaps with intelligent recommendations to improve research design and sampling strategy.
• Increase...