Implementing transparency, consistency and universal standards are the top three actions that marketers would like from data providers  to improve data quality, according to a recent study commissioned by data management platform Lotame.

Of the 300 brand marketers polled, 90% said they would buy more data if companies could guarantee its accuracy.  

Nearly all marketers view audience data as valuable, but only 20% are “very confident” of its accuracy, and 68% are “somewhat confident.” Only 12% are either “slightly confident” or “not confident at all” in the accuracy of the data they purchase.

Marketers are concerned with the frequency in which data gets updated, but the biggest challenge remains with the 25% of marketers who are not sure how to measure the success of their audience data purchases, and 53% who don’t measure demographic data quality at all.

When marketers do measure success, 67% say the click-through rate is the top metric. Awareness came in at 45%, followed by cost per acquisition at 43%, sales lift at 42%, video views at 33%, viewability at 33%, brand lift at 22%, brand recall at 17%, and other at 2%.

And 55% of marketers don’t measure demographic data for accuracy. Those that do, measure mostly against Nielsen DAR at 36%, followed by comScore vCE at 12%.

Marketers  are also concerned with bot fraud and non-human traffic because ad fraud not only impacts data accuracy, but campaign performance. The study also mention the misattribution of values and motivations that lead to broad or inaccurate categorization.

When marketers do buy data, 42% look for age and gender, which is the most popular data type, followed by geographic data at 34%. 

Advanced demographics such as household income, education, children, along with interest come in at 28% each. Third-party data and behavioral data, each take 25%, and social influencer and second-party data each take 24%.  

When the study asked for marketers to rank their use of demographic audiences, 76% said they “always” or “usually” target by age, 61% said by gender, 50% said by household income, 40% said by education level, and 32% say by the number of children in the household.