In this article
A review of the Modeling tab in the Permutive platform
Lookalike Modeling is a method of audience expansion that uses machine learning to identify users on your site with similar behaviors to your segment of choice.
Example 1: If you don't produce much sport content but want to sell an audience of 'Sport Fans', you could build a small audience of users who read sport content on your site and create a model off of it to identify other users who are also likely to be 'Sport Fans'.
Example 2: If you are collecting declared demographic data on your site and want to build an audience of 'Females', you can build a lookalike model on top of your declared female users to identify other users who are likely to be 'Female'.
Please see our doc on setting up a lookalike model for a step-by-step walkthrough on building a model.
How to read the model
Once the model has been created, a graph will be returned with lookalike audiences you can create at different reach/similarities. Every point on the graph is an audience that can be automatically created by clicking on the point.
If you hover over any of the points you will see the size of the audience and how similar it is to your seed segment (the segment you based the model off of):
To see which audiences influenced the model, see the list of weights below the graph:
- High positive weight: Indicates that users in the segment are very similar to users in the seed segment.
- High negative weight: Means users in the segment are likely not to be similar to the seed segment.
- Closer to zero: Indicate that these segments don't significantly influence the model on their own.