90% would use more data if they had greater confidence in its accuracy.
AB released rules that make it easier for advertisers to understand the quality of the data they’re buying. But how much does data quality matter?
Lotame, an independent DMP, just wrapped a study polling 300 advertisers that buy or use audience data about data quality. The big takeaway? Data quality, not scale or price, matter the most.
- 84% say accuracy was key when buying data vs. price (55%) and scale (37%)
- If data was more accurate, 90% of marketers polled said they’d buy more data
- The most popular data types are: age/gender (42%), then geographic (34%).
For the study, 300 brand marketers that purchase or use audience data (1st, 2nd or 3rd-party) were polled regarding the state of data quality, data quality challenges, quality-control practices, and more. 60% of marketers are concerned with the quality of data available from data providers/sellers. The top 3 reasons marketers are concerned with data quality are refresh rate, bots/fraud, and inaccurate categorization.
“In this more established data market, scale is a given,” said Jason Downie, Chief Strategy Officer, Lotame. “As audience data’s importance continues to grow, sophisticated marketers are becoming more interested in the quality of data to ensure they are making the right business decisions while reducing wasted ad spend.”
Nearly 100% of Marketers View Audience Data as Valuable
Per the study’s findings, 90% of marketers view audience data as either “very valuable” or “somewhat valuable” to their marketing efforts, while only 10% say it is only “slightly valuable” or “not valuable at all.” But what data are they buying?
Respondents said that they purchase a range of data types, with the most popular being:
- Demographic (age, gender) (42%)
- Geographic (34%)
- Advanced demographic (household income, education, children) (28%) and interest (28%)
- 3rd-party (25%) and behavioral (25%)
- Social influencer (24%) and 2nd-party data (24%)
“With so many different needs and goals, there isn’t a one-size-fits-all dataset that marketers can simply rely on,” said Downie. “It’s not surprising to see marketers investing in multiple data types for insights and activation.”
Age (76%) Most Popular Demographic Audience
Lotame asked marketers to rank their use of demographic audiences, such as age, gender, household income, education, and the number of children in a household.
- Age — 76% of marketers said they “always” or “usually” target by age.
- Gender — 61% said they target by gender “always” or “usually.”
- HHI — 50% said they “always” or “usually” target by household income.
- Education — 40% said they “always” or “usually” target this way.
- Number of Children in Household — 32% say they “always” or “usually” target by the number of children in the household.
“Age and gender are unsurprisingly the top demographic datasets that marketers leverage,” added Downie. “In addition to age and gender, for marketers seeking to deliver digital campaigns in an even more targeted way, it would benefit them to layer in other forms of audience data, like geolocation and behavioral.”
84% of Marketers Prioritize Accuracy
Per the study’s findings, only 20% of marketers who purchase or use demographic data are “very confident” of its accuracy, while 68% are “somewhat confident.” Meanwhile, 12% are either “slightly confident” or “not confident at all” in the accuracy of the data they purchase. That said, through this research, Lotame found that over 90% of marketers that purchase demographic data would use more data if they had greater confidence in its accuracy.
“Marketers now expect scale and are turning their attention towards the quality of data,” said Downie. “They want to be confident that the data they purchase will ensure that they reach their customers and help them make better business decisions.
In addition, accuracy was identified as the most important factor for those who purchase demographic data with 84% saying it was “very important” to their purchasing decisions, followed by price (55%) and scale (37%).
“Data sellers need to put in place rigorous quality control measures to ensure data’s quality and accuracy,” added Downie. “This means curated audience segments, leveraging a more robust verification process and using data science.”