Once upon a time, the data management platform (DMP) was an essential component in one’s martech stack, as it was considered a critical cog in brands’ marketing machines.
Many companies’ have built their programmatic advertising strategies around the DMP in recent years, as they rely on the tech to provide anonymized, cookie-based audience data to run, and improve the targeting for, their advertising campaigns.
Some businesses, like media and publishing organizations, have used data management platforms to create audience segments they can sell to advertisers and agency buyers.
But data management platforms have oversold, overpromised, and underdelivered to marketing professionals and their organizations for much of the past decade.
Marketers at well-known brands told AdExchanger they’re “in the process of sunsetting their DMPs,” while agencies said “clients are moving away from DMPs en masse.”
With the swift death of third-party cookies — thanks in large part to privacy changes implemented in Safari, Chrome, and other browsers — the usefulness and effectiveness of data management platforms for marketing teams has diminished substantially.
One programmatic advertising consultant told Digiday roughly 80% of the ad experts he spoke with around the time GDPR went into effect in mid-2018 said “they were not getting the most from their DMP.”
Publishers have heard the data management platform’s death knell for some time too.
AdExchanger also reports many marketers are “abandoning first-generation DMPs and trying out a new breed of segmenting engines that don’t require cookies.”
It’s now crystal clear: The data management platform’s days as an integral, must-have component of companies’ marketing strategies are coming to an end — and there’s ample proof to support its downfall.
Why data management platforms aren’t worth the investment for marketers
I already broke down how a customer data platform (CDP) differs from a DMP (and offers considerably more benefits for modern marketers), so I won’t do so again here.
Instead, I’ll lay out an increasingly obvious state of affairs: that the data management platform has failed — and will continue to fail — many marketers who rely on the tech.
Downside #1: Losing data after a matter of days is stupid.
Picture it: You’ve spent all this time figuring out what customer and campaign data you want to collect and you’re running great data analysis to understand how to make the most of it.
It’s awesome — until you start losing all of that data because the cookies expire. Suddenly:
- That historical data analysis you’ve been so carefully conducting? Far less potent.
- That lifecycle marketing strategy you’re trying to construct? Way too fragmented.
- That single customer view you so desperately want to achieve? Just not happening.
The exact cookie expiration date now varies from one browser to the next. The point is this audience data quality and reliability is as low as it’s ever been for marketers, publishers, and advertisers.
Simply put, none of your customer data should have an artificial shelf life.
Rather, all of your customers’ data should be stored, dynamically updated, and always accessible in persistent profiles where you can activate it in a matter of seconds (or days, months, or years, your choice) — not available for a limited time only and, thus, limiting your marketing capabilities.
But that’s the data management platform’s bread and butter: Cookie-based data that simply doesn’t last. (In other words, not nearly as effective as its near-polar opposite: first-party data.)
Downside #2: Black boxes of customer data suck.
There’s a really annoying TV commercial in which people whine, “But it’s my money, and I need it now!” Marketing professionals should feel the same way about their customer data:
“It’s my customer data, and I should have full and total access to it when I need it.”
That way, if you have questions about reports, for example, you can go in and understand the data analysis, rather than just cross your fingers and trust that what your DMP is spitting out is right.
If you can’t trust your own data or know what’s going on with it at all times (and in real time), then what’s the point of gathering it and storing it in a data management platform in the first place?
Black boxes lack transparency and visibility into the quality, accuracy, and reliability of your customer data sets and are major hindrances to marketing success — and your data management platform could be enabling this mindset.
Downside #3: It’s crazy to wait weeks for segments.
Speaking of real-time data, some marketers are willing to wait more than 10 minutes — let alone several days or even weeks — to get a segment that can be used in activation channels.
Real talk: If it takes your data management platform two-plus weeks to build customer segments, there’s virtually zero chance those segments re still accurate by the time they’re finished.
So much will have happened in the decision journeys for the consumers in those segments during those two weeks that attributes describing them change after two hours, never mind two weeks.
A customer segment definition that isn’t updated in real time isn’t old — it’s ancient. What’s more, it’s nearly impossible to take advantage of dynamic data (e.g., behavioral and contextual data points) when creating unique segments.
Other marketing technologies, like the customer data platform, offer real-time data collection and leverage machine learning to build customer segments based on a variety of granular profile traits and behaviors.
With other options for customer segmentation at your disposal — and one, in particular, that can take the considerable amounts of data stored across your stack and allow you to quickly and efficiently bucket and group contacts as desired — the dependency on your DMP makes even less sense.
Downside #4: There’s better martech out there.
Martech isn’t static. The 7,000+ marketing technology vendors in the marketplace today continue to enhance their SaaS solutions to make marketers’ lives and jobs easier and output more effective.
Thus, it shouldn’t come as a surprise there’s a fair amount of feature overlap from one type of martech to another – and that the landscape is always evolving. And such is the case with data management platforms and related systems.
For example, identity resolution solutions have adopted data collection functionality DMPs have traditionally been known for. Many can now match existing profile IDs with those offered by anonymous, third-party data providers and send paired IDs directly to demand-side platforms (DSPs).
As you can see, this circumvention of data management platforms renders one of its core features seemingly useless for marketers with the former system in their martech stacks.
In the same vein of other marketing technologies developing similar features, many marketing pros are going directly to Facebook and Google for their audience targeting needs for ads and, in turn, are questioning the cost of using DMPs to simply load data into those solutions.
Reducing reliance on (or getting rid of) your data management platform
I’m a realist: I’m completely mindful of the fact data management platforms won’t suddenly disappear from the martech landscape overnight or even be removed from many marketing teams’ stacks for failing to produce the return on investment needed to meet their business KPI targets.
But the plain truth is this:
Data management platforms just aren’t as dependable or practical as they once were — and they’ve already proven to be a letdown.
My advice? Spend some time with your CMO and marketing colleagues to determine if maintaining yours is worthwhile — or if there’s another martech system that allows you to build relationships with your customers through consented and authenticated ways.
The best way to get data is to ask for it — and if you’ve asked for it, you have to use it effectively.
Leverage your first-party data with customer profiles in a CDP, and you’ll see the difference it can make.
Still curious how a DMP and CDP differ? Download our eBook to see how the two marketing platforms stack up (and why the former doesn’t stack up to the latter).