A critical component of any data management platform is the ability to centralize your audience data from multiple systems into a single interface. They do this through a NoSQL database management system that imports your data from multiple systems using a match key between each system that they form via, what else, a cookie sync. It sounds complicated but it isn’t. Let’s take an example from the marketer side to explain the concept.
Identity Syncing and the Data Management Platform
Say you run a large eCommerce store and want to create audience-based marketing campaigns around different customer groups. You send a weekly newsletter with a few hundred thousand users signed up, you have a site analytics tool, you have an order management database, or other CRM system, and you buy media through a Demand Side Platform (DSP). Each system fulfills a specific business need, but generally speaking operate in parallel and do not talk to each other. So there’s no way for you to specifically target users on your DSP that are also signed up for your newsletter, or who are signed up for your newsletter and have also visited three or more pages in the mystery novels section of your site in the past 30 days. You have a site analytics cookie on the user’s machine, but no newsletter cookie, and even if you did, how do you know how to identify the same user in both systems? In order to get your newsletter system to talk to your site analytics system and push that information to your DSP for future media campaigns you need to find a way to identify the same user between systems. This is where the data management platform comes in.
Making Cross Platform Data Actionable
Data management platforms are more than just being able to identify characteristics on the same user from multiple systems however. DMPs offer profiling and segmentation technology that allows you to look at data from both directions. You can certainly create audiences with rich characteristics that you know, but you can also use a DMP to figure out what a high performance audience has in common. To take our example, perhaps 5% of your customers purchase at least $250 in goods once per month, or spend more than $1,000 in the month of December. You can use a DMP to understand what each group has in common, either from your own data, or in 3rd party data segments. Once you understand the meaningful trends, you can scale that audience by looking for people with the same characteristics on the open exchange that are not yet customers.