Is third party data bad?
The research below raises the question of the value of third party data, and it’s worth for marketing campaigns. I’ve always been a fan of the data – even when it’s not perfect. This just means one should 1) really evaluate if third party data is the right approach for your business case and need and recognize the inaccuracies before jumping in. 2) test it on a small scale with certain data providers to see if it’s money worth spending.
Third party data is so often questioned for it’s value, and often time the statistic of 35% inaccuracy between determining if the subject is male or female is thrown around. But where does that number come from and how much bearing does that have on the accuracy of third party data? In 2012 one anonymous ad tech exec told Digiday “the gender is wrong 30-35 percent of the time,” and that statistic has been plaguing the analytics market like wildfire.
Marketers are then commonly forced to ask: But how much does that number really stand up; how inaccurate is third party data really; is the price I am paying for third party data worth it?
During the Digiday Programmatic Summit in November of 2016, Matt Rosenberg, ChoiceStream, went over the importance of scale that third-party data sellers are pressured under, and often time in order to meet that need for scale, accuracy is thrown to the wind. “Advertisers need scale, and as a data vendor, if you can’t provide that, no one will buy your segment,” he said.
Rosenberg put it like this: “If you can get 300,000 people in a group with 95 percent confidence that they belong there, or 30 million people in a group with 60 percent confidence, well, it might not be such a hard decision to relax your model a bit, especially when no one is set up to audit you.”
In a study done by ChoiceStream, the company Rosenberg was once CMO of, it was found that a particular data vendor had identified 84 percent of users as both male and female, much higher than the traditional “35%” that is usually thrown around. While this could easily be seen as an outlier, ChoiceStream took the time to examine two vendors that were least likely to identify people as both male and female. By getting the third-party data internally from the vendors and syncing across data-sets, it was still found that about a third of the time the two vendors disagreed on what gender an individual was.