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Data quality isn't just a checkbox for us – it's our top priority. Our commitment shines through in our articles and podcasts, empowering researchers with the knowledge to maintain high-quality data.
What sets us apart from other sample options? Humans. Technology is absolutely necessary in the data quality process, but it isn't enough on its own. We see clear differences in the quality of our data compared with companies that are equally committed to data quality but rely entirely on technology.
The reality is that scaling quality is really hard. We've made our choice clear: Quality always comes before quantity.
Our multi-layered defense to improve data quality
By thinking about the problem in terms of layers of protection implemented throughout the research process, we have the best chance of preventing fraud.
Our typical process includes three distinct layers, each designed to address a different threat. We embed multiple advanced quality control tools directly into your surveys to identify and remove low-quality responses in real time. Human review adds another layer of scrutiny, helping us catch issues that technology alone might miss. By combining both, we deliver the best results.
Our 5 quality principles:
Robust Screening
Robust Screening
Combat fraud with our best-in-class anti-fraud software. This includes respondent activity checks, cookie de-duplication, digital fingerprinting, and more.
Expert Sourcing
Expert Sourcing
Leverage our expertise to source high-quality respondents from the most reputable providers. Secure 100% verified samples whenever possible.
Engaging Experience
Engaging Experience
Boost engagement with surveys designed to tell stories. Pack them with visuals and a friendly tone to keep participants engaged throughout.
Real-Time Cleaning
Real-Time
Embed advanced quality control directly into your surveys to identify and remove low-quality responses in real-time.
Evidence-Based Approach
Evidence-Based
Harness tried-and-true quality control measures backed by ongoing research and experimentation.
Want to have an honest conversation about data quality with practical tips?
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