How Truthset is Bringing Transparency and Accountability to Ad-Tech

In this interview with Jason Damata, Founder and CEO at Truthset, Scott McKinley, explains why he started the company, and how it uses a Data Collective to provide ad buyers, sellers and users of large scale consumer data, with visibility into how accurate their data is to produce better business outcomes.

Scott McKinley: I started Truthset because, after nearly 20 years in the mar-tech and ad-tech landscape, I got tired of how much obfuscation and opacity and general lack of transparency and accountability there was, in what was growing to be a quarter trillion dollar industry, now half a trillion dollar industry. And I frankly would have either left advertising entirely, if I couldn't figure out a way to do some good in that industry.

And so I created Truthset to, for the first time ever, provide buyers and sellers and users of large scale consumer data, audience data, targeting data, to have visibility into how accurate this data was to produce better outcomes.

Jason Damata: How does it work?

Scott McKinley: It works on a foundation of our Data Collective, which is a group of data providers, over 20 of them now, who contribute data to our system. We analyze that data and run machine learning over it to ascertain the accuracy of all the files that we're receiving. We use that then to create a derivative product, which is essentially an accuracy score that can be applied to any record, any consumer identity that's used for any purpose.

And therefore what happens is, when someone's working with large files, let's say to create an audience of new moms for CTV as an example, a lot of that data is not as accurate as it could be. Some records are more likely to be true, other records are less likely to be true. 

So we give both the buyer and the seller visibility into the sort of grades of data across that audience asset, so they can decide which records they should target, which ones they shouldn't.

This ultimately is good for everybody. It's good for the data providers who can premium price their data assets instead of just selling it by the pound. It's better for publishers who can build more accurate audiences to compete with walled gardens and walled garden level performances, and basically improve CPMs, potentially even lower ad load.

It's better for the consumers who get a more relevant advertisement, which is more like a service rather than an irritation. And then it's better for the brands who benefit from consumers who respond to the ads because they mean something to them. So it's really a system that's meant to really lift all boats. This is not a zero sum game, where there's winners and losers - there's too much of that in ad-tech. So we're really out there to make the whole data ecosystem better for placements.