You can use several different types and sources of models in
Audience Lab offers an easy way to compare your
customers' conversion rates, across your active models.
In this use case, you are comparing different models. You can either
use models created via an in-house data warehouse and import them in
Audience Manager as
Onboarded Traits or you can use the
Create two models, either in the
Model Builder, or via an
algorithmic traits from the algorithmic
model or import your own models as onboarded traits.
Create mutually exclusive segments so users in both models don't
- Create a
Model 1 Segment and a
Model 2 Segment.
- Have the segment
Model 1 Segment be model 1 trait AND NOT model 2 trait, and
Model 2 Segment.
Create two segment test groups in
Audience Lab, one with
Model 1 Segment as the baseline, the other with
Model 2 Segment as the baseline.
- Keep the
variables the same for both test groups: same destinations, creative,
- Make sure the
test segments have similar numbers of users (e.g. 1.6 million and 1.8 million
is alright, 1.6 million and 16 million is not).
- Reserve a
control segment in each test segment test group. This way, you can set aside a
small part of each segment and not target them explicitly in the test.
Examine the results:
Audience Lab reporting view will show
the number of conversions each model is driving. For conversion based
campaigns, the test segment that drives the most conversions will signify the
model that is performing best.
- Because you have
control segments, you can also evaluate how the model did against "standard
targeting." You are not only just testing one model versus the other, but
testing the question of "did this model do better than normal practices?"