Abstract: Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.

Abstract: In some implementations, the techniques described herein relate to a method including: loading a current and a new model, the new model including the most recent version of the current model; computing a migration duration based on computed properties, namely the jitter in predictions between the current and the new models based on imputing the same inference data to both models; blending outputs of the current model with outputs of the new model according to weights computed for a current time step in the migration process; and serving new predictions using the new model when the migration duration expires.

Abstract: The disclosed embodiments relate to devices, computer-readable media, and methods for generating training data for training an ordinal, regression-based classifier, the method including grouping client data based on client keys associated with the client data, pairwise matching records in the client data to generate feature signatures and inferring a label based on client key statuses for the pairwise-matched records, and building a training dataset from the inferred labels and feature signatures, the training dataset used to train the classifier.

Abstract: Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.

Abstract: Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.

Abstract: Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving a vector, the vector including a plurality of features related to a user; predicting a return probability for the user based on the vector using a first predictive model; adjusting the return probability using a fitted sigmoid function to generate an adjusted return probability; and predicting a lifetime value of the user using the adjusted return probability and at least one other prediction by combining the adjusted return probability and the at least one other prediction.

Abstract: The example embodiments are directed toward improvements in generating affinity groups. In an embodiment, a method is disclosed comprising generating probabilities of object interactions for a plurality of users, a given object recommendation ranking for a respective user comprising a ranked list of object attributes; calculating interaction probabilities for each user over a forecasting window; calculating affinity group rankings based on the probabilities of object interactions and the interaction probabilities for each user; and grouping the plurality of users based on the affinity group rankings.

Abstract: The example embodiments are directed toward improvements in predicting an ideal audience size. In an embodiment, a method is disclosed comprising receiving a set of users associated with an object attribute; selecting samples from the set of users; computing hit rates for the samples, a respective hit rate in the hit rates computed by calculating a total number of users in a respective sample associated with an interaction associated with the object attribute; and selecting a recommended sample from the samples, the recommended sample comprising a sample having an associated hit rate that meets a preconfigured hit rate threshold.

Abstract: The example embodiments are directed toward predicting the lifetime value of a user using an ensemble model. In an embodiment, a system is disclosed, including a generative model for generating a first prediction representing a first lifetime value of a user during a forecasting period and a discriminative model configured for generating a second prediction representing a second lifetime value of the user during the forecasting period. The system further includes a meta-model for receiving the first prediction and the second prediction and generating a third prediction based on the first prediction and the second prediction, the third prediction representing a third lifetime value of the user during the forecasting period.