Digital Publishers and Advertisers that have access to a Data Management Platform (DMP) can bootstrap their own data modeling, or lookalike model capabilities with some simple index-based approaches. That is to say, if you can understand both the total population of users for every segment and for any specific segment, how many users of every other segment overlap in that target segment, you can build a fast and easily understood audience model with a little legwork. It’s not the rocket science approach of a regression model or black box algorithm, but it works, and it’s pretty easy for people without a degree in data science to execute once you figure out how to get the right data out of your system.
How to Do Lookalike Modeling Yourself
The first step to building a lookalike segment is to first define what you are trying to model, that is, what audience you want want more of. This will be your ‘target’ – for our example here, let’s consider the following audiences:
|Segment||Qualified Users||% of Total
|Women|| 20,000 ||20%
|Pet Owners|| 5,000 ||5%
|Coffee Drinkers|| 8,000 ||8%
|Outdoor Enthusiasts|| 9,000 ||9%
|Total Users|| 100,000 ||100%
Let’s say we’re trying to reach females. Unfortunately, we only have 20,000 we can identify, out of a total population of 100,000. Now let’s assume that our content isn’t skewed to one gender or another, and therefore there’s clearly some users in the 80,000 other users that we can expect would be female. But we need to find a signal within that group that directs us to which other audiences are likely to be female. (more…)
Before the internet and digital advertising, direct mail solicitation was perhaps the most technologically advanced form of data driven marketing out there. Even today, as much as interactive marketers like to poke fun at traditional media people, the direct mail industry is far more sophisticated at accurate audience segmentation and message delivery than most of the digital realm. Since everything in the snail mail world works off your actual name and address, data management is far simpler and can easily connect the data points in your life – the car you drive, your credit score, your age, gender, and plenty else from public records. Start adding information about your purchase habits from catalogs, your credit cards, and all the hotel and airline loyalty cards stuck in your wallet and the direct marketers can profile you three ways to Sunday. The truth is that it’s far easier to move data offline by matching on a name and address than to move it online with nothing but a cookie. That said, data companies and marketers alike have a huge incentive to try, because offline data is generally much more reliable and therefore valuable than its online competitors. (more…)
To get the full value out of a relationship with a data management platform (DMP), you want to provide the platform with as much data as possible. That said, the low hanging fruit in any organization will be to integrate 1st party data for which you already have a cookie to the DMP. The mechanism to accomplish this is your standard cookie sync,which passes a user ID from one system to another via a query string appended to a pixel call, and ideally, a server-to-server integration after that.
Practically speaking this means that when a user hits your site and calls your site analytics tag, either independently or through a container tag, that site analytics tag redirects the user to the DMP, and simultaneously passes the site analytics user ID to the DMP. When the DMP receives that call, it cookies the same user and also records what the site analytics user ID is. Now the DMP knows how to associate data from the site analytics tool to its own cookie ID. The beauty of this system is only the user IDs need to be synced at this time, and the actual data that the site analytics tool records can be passed to the DMP later, without slowing down the user experience on site. Now imagine replicating this process with all 3rd party tools, and syncing all systems into the DMP. (more…)
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. (more…)
If you’re working in digital advertising today and not losing sleep over your data management strategy (or lack thereof), climb out from under your rock and join the rest of us trying to figure out how to leverage the mountain of consumer intent and behavior collecting on the doorstep each day. From both the marketer and publisher perspective, data isn’t the problem, access is the problem. Each party has access to vast amounts of data, either directly or through 3rd party channels, but centralizing, organizing, analyzing, and segmenting are very difficult for all but the largest companies. Unless you have a pedigreed team that speaks SAS and Oracle, understands how to use an IBM supercomputer, or has a team of PhDs on the payroll, building your own solution to this problem just isn’t realistic. It just doesn’t exist in the DNA of most advertising companies today, at least not yet. (more…)