Brett House, VP of Global Product Marketing at Nielsen Marketing Cloud, recently penned an article in ExchangeWire that breaks down the extreme hype around marketing AI. He states that many AI solutions only provide “batch” or offline learning (instead of real-time), and a lack of true transparency is clouding the potential of AI in marketing and advertising.
I can say the same about DMPs.
AI Decisioning is the Future of Ad Buying
I work for a company that’s all about using AI to maximize ad performance. Every minute, we collect millions of signals from all over the web (mostly search and browsing behavior data) and update our user profiles in real-time. Our AI works amazingly on our in-house DSP to deliver CPA’s at a fraction of what even the most skilled AdOps person can do manually. After all, there are just too many variables for a human being to beat a computer at that game.
With all our DSP has to offer, many agency clients prefer to use their own DSP. Learning a new tool can be a laborious task for some, or they may have deep integrations between their DSP of choice and their backend systems.
Regardless of why, last year we set out to launch Magnetic Live Audiences (“MLA”) for that reason. Our AI creates custom, real-time audiences that MLA packages as third-party segments to be activated on the DSP the advertiser is using.
That’s where a DMP often comes into play. DMPs have been used traditionally to aggregate, manage and deliver audiences from data producer to data consumer. For example, from an advertiser’s CRM system to demand-side platform (DSP).
DMPs Are Stuck in the Past
You would think that, in 2018, using a Data Management Platform (DMP) to deliver a multitude of custom audiences in real-time to any buying platform is table stakes. Haven’t DMPs touted “just in time” audience management for ages?
Not so fast.
In recent months, I’ve been feuding with leading DMPs to distribute our audience quickly and efficiently. Most established DMPs have difficulties handling the volume of data needed to sync audiences that are custom-built for each campaign and updated continuously.
At the same time, consumer behavior is notoriously fickle. Think about somebody adding an expensive phone to a shopping cart on one of our many eCommerce partners. Suddenly the value of this user skyrockets. A shopping cart action is one of the most valuable signals that a user is about to convert. Unfortunately, this “value boost” will last a few minutes, hours at most.
Now, picture us struggling to communicate to DMPs that our shopper’s cookie or device ID is suddenly worth 100x more for a Samsung campaign than it was just one minute before. DMPs will take hours to ingest our updated segment, more hours to process and approve the change, and a few more hours to match that user and re-export to our target DSP.
To make matters worse, the DSP has its own ingestion and processing lag. Some established DMPs even require manual operation on their UI to complete the update. When things go wrong, the DMP and DSP point fingers at each other while we are left scratching our heads. In most cases, by the time our phone shopper ID is ready for activation on the DSP where our Samsung campaign is running, the phone has been ordered, delivered, and activated.
A Superfluous Layer Between Data Seller and Data Buyers
Most of DMPs’ business is still to distribute “standard” catalogs of pre-packaged segments that are static for weeks (or even months). These are the same segments that nobody on the buying side trusts. Because of this legacy, DMPs tend to sit between the data seller (us in this case) and the data buyer (the DSP). This additional “hop” creates a refresh latency and decreases the all-important match rate (i.e. how many of our user cookies can be mapped to the cookie seen by the DSP).
However, in the new AI-driven world of custom segments and real-time online learning, this simply won’t do. It should come as no surprise that we are seeing much better performance with direct integration with DSPs than when distributing our audiences through a DMP.
But don’t DMPs offer more bells and whistles? It’s true that DMPs come with a better UI, easier data onboarding, and fancy analytics and attribution models. But performance is what actually matters. That’s why AI-driven data sellers like Magnetic will gravitate toward direct integrations with DSPs.
Leading DSPs Will Build Powerful Capabilities to Ingest Real-time Audiences
For all these reasons – in the end, independent DMPs will either be absorbed by established DSPs or major marketing clouds, or they will die. So where does that leave us?
- Refreshing audience segments (in minutes or less) will soon become the norm, and DSPs will get us there. AI-driven custom audiences are indeed the future of data selling. Most of the leading DSPs understand that. DMPs gave us quick reach, but top-notch performance will require close integration with the buying platform. DSPs and audience sellers alike will continue to innovate and quickly bring new capabilities to the forefront to maximize performance from AI-driven custom audiences. Stalled, static behavioral segments will soon be a thing of the past.
- Audience sellers will gain necessary control over the bid price. This can be tricky as media buyers need the flexibility to adjust price according to overall campaign objectives and performance to date. The Trade Desk “base bid multiplier” feature offers a smart trade off here. While the trader can set a base bid, the audience seller can set a multiplier for each user to adjust the bid price according to the value of the user. Some platforms also offer multiple pricing options. For example, going beyond fixed CPM fees and offering percent-of-media or CPA.