Announcing Audience Lab — A Tool to Improve Testing
Improved Testing With Audience Lab
Audience Lab gives you the ability to split one of your audience segments into mutually exclusive buckets. For example, if you have an audience segment of males, you could split that segment into two identical but exclusive groups of males to use for testing. With the audience variable controlled, you can layer different targeting variables to determine what drives the most conversions — for instance, you might test different ad creative to see which one gets a better response from your male audience.
An Easier Way to Test Advertising Elements
Marketers are always looking for the best ad creative, targeting criteria, or targeting platform to drive the best results. It is challenging to ensure that users in test groups don’t overlap when testing multiple targeting platforms, even more so when testing different creatives across targeting platforms — and both are use cases that Audience Lab supports.
For example, when testing two different demand-side platforms (DSPs), you don’t want to send the same set of users to both systems — you won’t know which DSP truly drove the conversion for a given user. Another option is to use two different, mutually exclusive segments; but, that would introduce an audience variable to the test. To obtain a really accurate measurement, you have to minimize the number of variables between the two systems so you can confidently say that the difference in performance is due to a feature of the DSP and not an outside factor.
Audience Lab makes testing different DSPs — to choose the best one — much easier, allowing you to minimize the number of variables between the two DSPs. If you were to put two different segments — male and female, for example — into two different DSPs, each DSP would perform differently because each one is targeting a different audience. Instead, Audience Lab allows you to take one segment, split it equally into mutually exclusive test segments, and then run those test segments through two different DSPs. Since you’re using the same audience, any differences in performance will be related to the DSPs themselves, giving you the information you need to choose the one that performs better.
To kick things up another notch in complexity, what about the challenges presented when a consumer uses multiple devices? Audience Manager’s Profile Link — a cross-device feature set — lends a hand there. Audience Lab uses Profile Link’s profile merge-rule framework to qualify a user’s multiple devices into the same test segment. This helps further control the audience variable. Users won’t see different creative or be bid on by both DSPs simply because they use more than one device.
The Right Information to Know What Works
With Audience Lab, you can confidently say, “We used the same audience, the same creative, and the same strategies, so any differences in performance must be related to the DSP itself.” The differences could be related to the DSP’s algorithms, inventory, or something else that is inherent to the DSP you are testing. When you narrow those things down, you can make a more informed decision about which DSP drove more conversions.
Market Demand for Improved Data-Management Platforms (DMPs)
Market demand was a major driver behind Audience Lab’s development. Clients were looking for this kind of segmentation in their data-management platforms — and many were trying to create these mutually exclusive segments manually. That can be a real challenge, possibly involving a lot of manual effort such as creating rules of segmentation to determine which mutually exclusive segment a user should be in. These rules are rarely user-friendly.
Audience Lab makes the whole testing workflow easier, allowing users to split these segments and providing them with reporting so they can see one source of truth on conversion. Audience Lab takes Adobe’s already competitive DMP, Adobe Audience Manager, and makes it even more useful for marketers.