A. Permutive uses machine learning to find other users with common features to the seed cohort to generate a lookalike model.
The goal of lookalike (LAL) cohorts is to expand a cohort (the "seed cohort") by finding similar users.
For example, if we want to expand a cohort of "Tennis Lovers", our lookalike model may learn that many users in the cohort are also in a "Sports Lover" cohort. This means that if we see a user that is in "Sports Lover", within some given probability, they are "almost" a "Tennis Lover" as well. Conversely, the model may learn that users who are "Tennis Lovers" are not "Basketball Lovers", and a negative importance can be given to the "Basketball Lovers" cohort.
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