ADOTAS — Having spent many years solving endemic problems in the mobile ad network space — first at Quattro, then at iAd — and as my co-founders and I were just starting to consider AdMobius, the recurring thought was how to address all the disorganized mobile publisher data which exists on any given user. In totality, mobile data creates the ultimate repository for understanding the audience. In theory, this dataset knows when you wake and sleep, where you live and work, where else you go and what you purchase, even who you know and like. The data is massive and possibilities seemingly endless. There was much to dream about here until the rubber hit the road on mobile data: Hashing, Proprietary IDs, and lack of standardization meant that each of those that wanted to share their data (or even just understand all of their own data) were faced with a series of highly difficult technical challenges in device identification and user identity resolution. With all the brashness and scrappiness of a startup, our team at AdMobius set out to solve these problems from a mobile-centric perspective seeking to end the siloed nature of mobile data. We didn’t know it then, but we were solving a much larger problem than was apparent.
Solving the problem of matching profiles and data across disparate mobile namespaces coupled with the difference from a data rich online cookie environment relative to mobile data dearth led us to the idea of utilizing our mobile identification algorithm (which is dependent only on some sort of a persistent identifier, but not of any particular type) to pull data from online cookies and make this available to marketers looking for targeting data on mobile. While this is useful, it points to a much larger opportunity. If we have a device graph which identifies links between cookies and mobile IDs within and across publishers’ and marketers’ ID spaces, and we also house their proprietary data that exists on each of these platforms, we now have an opportunity to do something huge. We enable a wide variety of mixed modality targeting and analytics including cross channel retargeting and sequential messaging and measurement of effectiveness of one channel using another. Consider a publisher trying to quantify effectiveness of desktop website changes where a user is typically engaging in consideration on that channel against measurement in mobile where a user may be typically engaging in a conversion behavior. This simply cannot be done effectively without the device graph. This huge opportunity starts with matching cookies to mobile ids, but carries over to almost any digital device imaginable, from home automation devices to set top boxes. Having a device graph is at the center of making all this behavior available to the data owner as a single profile and enabling that owner to act on it.
I’ve been quite sure for some time that a DMP with a device graph is going to make a huge impact in the marketplace. It has been our belief at AdMobius that this capability ought to exist outside of any media buying entity and should exists as a service to all of the data owners in the ad tech ecosystem. Why should a marketer only be able to retarget mobile users against fixed web cookies if done in the context of a specific media buy with a particular DSP? This state of affairs makes no sense to us and we don’t think it’ll make much sense to the data owners either. It’s for this reason that when Lotame came to us suggesting that we incorporate our mobile and cross device capabilities into one of the largest DMPs in the space, with tons of customer adoption, we knew this was a great fit. There are so many talking about this problem and none offering a solution. To be able to do so at scale with a team that shares our vision of media agnostic ad tech services, a commitment to startup scrappiness, a pick yourself up by the bootstraps work ethic, and unparalleled organizational sensitivity and reaction to opportunity, is a dream and we at AdMobius knew we were home.
This article was originally published on the Lotame blog.