Abstract: System and method including accessing a feature associated with a plurality of user identities (IDs); accessing a structure specifying mappings between the plurality of user IDs and a plurality of user canonical IDs; generating groups of feature values of the feature based on the mappings, each group of feature values being associated with a corresponding group of user IDs and with a corresponding user canonical ID; aggregating each group of feature values to calculate an aggregate feature value of the feature, each aggregate feature value associated with the corresponding user canonical ID; computing predictive traits associated with the plurality of user canonical IDs, the predictive traits including likelihoods of events or trait values, the computation of the predictive traits using the aggregate feature values associated with the corresponding user canonical IDs; and causing display, at a user interface (UI) of a computing device, of the computed predictive traits.

Abstract: Methods, systems, and computer programs are presented for estimating a propensity to buy a product or service. One method includes accessing events generated at a website. Each event comprises a data structure describing an operation performed by a user when accessing the website. Further, the method performs operations, for each user from a group of users associated with an audience, comprising: providing event information for a time window, information of the user, and information for a product as input to a propensity machine-learning (ML) model, the model being trained with training data comprising values for features that include event features, user information features, and audience labels; and generating, by the propensity ML model, a score for the user indicating a probability that the user will purchase the product. Further, the method generates a forecast of purchases of the product for the users in the audience based on the scores.

Abstract: Methods, systems, and computer programs are presented for estimating if a user belongs to an audience category. One method includes an operation for accessing events generated at a website. Each event comprises a data structure describing an operation performed by a user, from a group of users, when accessing the website. Further, the method includes an operation for providing event information and information of a first user, for a predefined time window, as input to an audience machine-learning (ML) model. The audience ML model is trained with training data comprising values for features that include event features, user information features, and audience labels. The method further includes operations for generating, by the audience ML model, a score for the first user indicating a probability that the first user belongs to the audience, and for determining if the user belongs to the audience based on the score.