In this article
This guide will walk you through setting up a Lookalike (LAL) Model in the Permutive platform.
Lookalike (LAL) Modeling uses machine learning to find new people with features and characteristics similar to those in the seed segment chosen. Similarity and reach metrics allow you to find a model with the highest similarity at the reach required for your campaign.
Note: before you begin, make sure you already have:
- Access to the Permutive dashboard
- A Permutive segment already set up, for which you'd like to create a LAL Model
Creating a LAL model
1. Click on Modeling
2. Click "+ Add Model"
3. Choose your seed segment, the data set you want to train the model on, and click "Create"
- All: Create your look-alike model based on all existing segments.
- All With Exclusions: Create your look-alike model based on all existing segments minus ones selected.
- Fixed Set: Create your look-alike model based only on selected existing segments.
4. The LAL Model will take up to 24 hours to build. During this time, the segment will display that it's provisioning a lookalike model and the time this work started.
5. Once the model has finished building, you will find it under the Modeling Tab, under the name of the Seed Segment. Models that are built will say Completed and models that are still building will say In progress.
A Reach/Similarity graph is returned with a distribution of users you can create a LAL model from. Select the most relevant audience by clicking on one of the dots on the graph. Any number of lookalike segments can be deployed from one lookalike model by clicking on the segment you wish to create.
- 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 belong to the seed segment.
- Closer to zero: Indicate that these segments don't significantly influence the model on their own.
Note: The sorting by absolute value is by design. It effectively sorts the features by segments that influence the model the most (whether it's a high positive weight or a high negative weight). A Const of 0 indicates a 50% probability a randomly selected user is in the seed segment. As it becomes negative that probability goes down.
Creating a LAL Segment
Once you have chosen a reach and similarity, the deployed lookalike segment will appear in the list of segments as a separate segment with an automated name:
As well as underneath the Seed Segment in the Modeling section:
For FAQs on Lookalike modelling, please click here.
If you have any questions, please contact customer support by emailing email@example.com or chat to the Customer Operations Team via the LiveChat icon in the bottom right corner of your screen.