Set up a Model

In this article

Lookalike Modelling

Lookalike (LAL) Modelling uses machine learning to find new people with features and characteristics similar to those in the seed cohort 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 and a Publisher cohort already set up, for which you'd like to create a LAL Model.

Creating a Lookalike Model

In the Permutive Dashboard:

  1. Go to Cohort Models
  2. Click on 'Models'
  3. Click '+ Add Model'


4. Select the cohort you would like to build the lookalike model from

5. Select which of your Publisher cohorts you want the LAL model to be trained on.

  • All: Create your look-alike model based on all existing cohorts.
  • All With Exclusions: Create your look-alike model based on all existing cohorts minus ones selected.
  • Fixed Set: Create your look-alike model based only on selected existing cohorts.


The LAL Model will take up to 24 hours to build. During this time, the cohort will display that it's provisioning a lookalike model and the time this work started.


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 cohorts can be deployed from one lookalike model by clicking on the cohort you wish to create.mceclip3.png



  • High positive weight: Indicates that users in the cohort are very similar to users in the seed cohort.
  • High negative weight: Means users in the cohort are likely not to belong to the seed cohort. 
  • Closer to zero: Indicate that these cohorts don't significantly influence the model on their own.

Note: The sorting by absolute value is by design. It effectively sorts the features by cohorts 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 cohort. As it becomes negative that probability goes down.

Creating a Lookalike Cohort

Once you have chosen a reach and similarity, the deployed lookalike cohort will appear in the list of cohorts as a separate cohort with an automated name
e.g. Very Engaged — [Lookalike: 32.1% similarity, including seed].Screenshot_2021-07-09_at_08.44.38.png

You can edit or delete a lookalike model at any time by going to the seed cohort you used to create it, and clicking either Edit or Delete above the graph.


For FAQs on Lookalike modelling, please click here.


If you have any questions, please contact customer support by emailing or chat to the Customer Operations Team via the LiveChat icon in the bottom right corner of your screen.

Was this article helpful?
0 out of 0 found this helpful
Have more questions? Submit a request