The Cross Device Question: Krux

AdExchanger features an interview with Krux’s co-founders, CEO Tom Chavez and CTO Vivek Vaidya, to discuss what the company offers in terms of linking consumers across devices.  The interview gives a very clear view on Krux’s approach to Cross-Device User Identification.

How does Krux identify users across devices?

CHAVEZ: There are two modes: deterministic case and the statistical approach. [Ed: Other DMP companies used the adjective “probabilistic.” This is a tomato-tomahto type of distinction.] The Telegraph is one of the publishers that’s early to the punch. They had a batch of named users and wanted to find them across the properties they control. In the deterministic case, they provide us with a key, usually email identification, that allows us to match users across, for example, mobile vs. desktop.

That’s deterministic because there’s no uncertainty whether we found a user uniquely identified by that key. It’s certainly a pattern a lot of our publishers have as they have subscription registration already. They’re looking to reach those users in a controlled way across all those screens. Marketers don’t want to carpet bomb the wrong users more than is effective or useful and it’s strategic for them to sell cross-device campaigns.

And statistical?

CHAVEZ: You don’t have a unique identifier that deterministically identifies a single user, but you have a data signature. That has a lot of characteristics or attributes of the user of interest. Some of that could be tied to IP address, it could be tied to a data signature around configurations in a particular browser. All of these bits of information are useful grist for the mill, allowing us to build a statistical profile of the user.

[. . .]

How do you determine or improve the accuracy of the statistical approach?

CHAVEZ: The game of course is to boost that signal using the deterministic information you have. There’s a machine-learning aspect where you’re basically taking a seed and using that to infer more interesting things about a broader set of users who look, sound and act like the users you have in that seed.

What’s interesting for us and our customers is because we’ve amassed an interesting level of scale at Krux. We see over 1.5 billion users across the globe, that provides an interesting training set to power the statistical CDUI we provide.

VAIDYA: To add to what Tom said, in terms of data signatures we track, we use the IP address, the user’s device signature, their browsing patterns, where they browsed from, all of these different elements go into the [statistical] model. The model ends up computing a score that looks through cookies and says the similarity score between these two cookies is X. If X is past a certain threshold, we determine that those two cookies represent the same user across multiple devices.

The deterministic approach and the truth set are used as the training and test set for verifying the accuracy of the similarity score we computed. It’s a continuous feedback loop. As we get more data from our first-party registered data from our clients, it feeds the deterministic piece, which feeds the machine-learning piece and the cycle goes from there.

To read the interview in full visit