DMP 1-2-3

Blank Whiteboard IsolatedAlmost every marketer is starting to lean into data management technology. Whether they are trying to build an in-house programmatic practice, use data for site personalization, or trying to obtain the fabled “360 degree user view,” the goal is to get a handle on their data and weaponize it to beat their competition.

In the right hands, a data management platform (DMP) can do some truly wonderful things. With so many use cases, different ways to leverage data technology, and fast moving buzzwords, it’s easy for early conversations to get way too “deep in the weeds” and devolve into discussions of “match rates” and how cross-device identity management works. The truth is that data management technology can be much simpler than you think.

At its most basic level, DMP comes down to “data in” and “data out.” While there are many nuances around the collection, normalization, and activation of the data itself, let’s look at the “data in” story, the “data out” story, and an example of how those two things come together to create an amazing use case for marketers.

 

DataIn_DataOut

The DMP Tie Fighter: The left wing shows data coming into the DMP, and rhe right wing shows the data actvated on various channels. 

 

 

The “Data In” Story

To most marketers, the voodoo that happens inside the machine isn’t the interesting part of the DMP, but it’s really where the action happens. Understanding the “truth” of user identity (who are all these anonymous people I see on my site and apps?) is what makes the DMP useful in the first place, making one-to-one marketing and understanding customer journeys something that goes beyond AdExchanger article concepts, and starts to really make a difference!

  • Not Just Cookies: Early DMPs focused on mapping cookie IDs to a defined taxonomy and matching those cookies with execution platforms. Most DMPs—from lightweight “media DMPs” inside of DSPs to full-blown “first-party” platforms—handle this type of data collection with ease. Most first-generation DMPs were architected as cookie collection and distribution platforms, meant to associate a cookie with an audience segment, and pass it along to a DSP for targeting. The problem is that people are spending more time in cookie-less environments, and more time on mobile (and other devices). That means today’s DMPs have to have the ability to do more than organize cookies, but also be able to capture a large variety of disparate identity data, which can also include hashed CRM data, data from a point-of-sale (POS) system, and maybe even data from a beacon signal.
  • Ability to Capture Device Data: To a marketer, I look like eight different “Chris OHara’s:” three Apple IDFAs, several Safari unique browser signatures, a Roku device ID, and a hashed e-mail identity or two. These “child identities” must be reconciled to a “Universal ID” that is persistent and collects attributes over time. Most DMPs were architected to store and manage cookies for display advertising, not cross-device applications, so platforms’ ability to ingest highly differentiated structured and unstructured data are all over the map. Yet, with more and more time dedicated to devices instead of desktop, cookies only cover 40% of today’s pie.
  • Embedded Device Graph: Cross-device identification is notoriously difficult, requiring both the ability to identify people through deterministic data (users authenticate across mobile and desktop devices), or the skill to apply smart algorithms across massive datasets to make probabilistic guesses that match users and their devices. Over the next several years, the word “device graph” will figure prominently in our industry, as more companies try and innovate a path to cross-device user identity—without data from “walled garden” platforms like Google and Facebook. Since most algorithms operate in the same manner, look for scale of data; the bigger the user set, the more “truth” the algorithms can identify and model to make accurate guesses of user identity.

The “data in” story is the fundamental part of DMP—without being able to ingest all kinds of identifiers and understand the truth of user identity, one-to-one marketing, sequential messaging, and true attribution is impossible

Data Out

While the “data in” story gets pretty technical, the “data out” story starts to really resonate with marketers because it ties three key aspects of data-driven marketing together. Here’s what a DMP should be able to do:

  • Reconcile Platform Identity: Just like I look like eight different “Chris O’Haras” based on my device, I also look like 8 different people across media channels. I am a cookie in DataXu, another cookie in Google’s DoubleClick, and yet another cookie on a site like the New York Times. The role of the DMP is to user match with all of these platforms, so that the DMP’s universal identifier (UID) maps to lots of different platform IDs (child identities). That means the DMP must have the ability to connect directly with each platform (a server-to-server integration being preferable), and also the chops to trade data quickly, and frequently.
  • Unify the Data Across Channels: To a marketer, every click, open, like, tweet, download, and view is another speck of gold to mine from a river of data. When aggregated at scale, these data turn into highly valuable nuggets of information we call “insights.” The problem for most marketers that operate across channels (display, video, mobile, site-direct, social, and search, just to name a few) is that the fantastic data points they receive all live separately. You can log into a DSP and get plenty of campaign information, but how do you relate a click in a DSP with a video view, an e-mail “open,” or someone who has watched a YouTube on an owned and operated channel? The answer is that even the most talented Excel jockey running twelve macros can’t aggregate enough ad reports to get decent insights. You need a “people layer” of data that spans across channels. To a certain extent, who cares what channel performed best, unless you can reconcile the data at the segment level? Maybe Minivan Moms convert at a higher percentage after seeing multiple video ads, but Suburban Dads are more easily converted on display? Without unifying the data across all addressable channels, you are shooting in the dark.
  • Global Delivery Management: The other thing that becomes possible when you tie both cross-device user identity and channel IDs together with a central platform is the ability to manage delivery globally. More on this below!

gdmGlobal Delivery Management

If I am a different user on each channel—and each channel’s platform or site enables me to provide a frequency cap—it is likely that I am being over-served ads. If I run ads in five channels and frequency cap each one at 10 impressions a month per user, I am virtually guaranteed to receive 50 impressions over the course of a month—and probably more depending on my device graph. But what if the ideal frequency to drive conversion is only 10 impressions? I just spent 5 times too much to make an impact. Controlling frequency at the global level means being able to allocate ineffective long-tail impressions to the sweet spot of frequency where users are most likely to convert, and plug that money back into the short tail, where marketers get deduplicated reach.

In the above example, 40% of a marketer’s budget was being spent delivering between 1-3 impressions per user every month. Another 20% was spent delivering between 4-7 impressions, which conversion data revealed to be where the majority of conversions were occurring. The rest of the budget (40%) was spent on impressions with little to very little conversion impact.

In this scenario, there are two basic plays to run: Firstly, the marketer wants to completely eliminate the long tail of impressions and reinvest it into more reach. Secondly, the marketer wants to push more people from the short tail down into the “sweet spot” where conversions happen. Cutting off long tail impressions is relatively easy, through sending suppression sets of users to execution platforms.

“Sweet spot targeting” involves understanding when a user has seen her third impression, and knowing the 4th, 5th, and 6th impressions have a higher likelihood of producing an action. That means sending signals to biddable platforms (search and display) to bid higher to win a potentially more valuable user.

It’s Rocket Science, But It’s Not

If you really want to get deep, the nuts and bolts of data management are very complicated, involving real big data science and velocity at Internet speed. That said, applying DMP science to the common problems within addressable marketing is not only accessible—it’s making DMPs the must-have technology for the next ten years, and global delivery management is only one use case out of many. Marketers are starting to understand the importance of capturing the right data (data in), and applying it to addressable channels (data out), and using the insights they collect to optimize their approach to people (not devices).

It’s a great time to be a data-driven marketer!

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