Abstract: A feature engineering engine is included in an analytics application provided to at least one subscriber from a plurality of subscribers. The feature engineering engine generates a reduced discovery dataset based on an input dataset and stores at least a portion of the reduced discovery dataset in cache memory associated with the analytics application. While displaying at least a portion of the reduced discovery dataset, the feature engineering engine performs one or more entity resolution operations and generates an instantiated set of features. In some embodiments, the instantiated set of features is generated based on a previously generated, reusable feature definition. In some embodiments, using the instantiated set of features, a trained machine learning model is automatically selected from a plurality of models based on a performance metric determined for the instantiated set of features.

Abstract: Methods and systems for determining consumer spend analytics are provided. The method includes identifying a plurality of microsegments of a population, retrieving transaction data associated with a first cardholder from a payment processing network, and matching the first cardholder to a first microsegment of the plurality of microsegments. The method also includes calculating, based at least in part on the typical income and the typical spend of the consumers in the first microsegment, a cardable spend for the first cardholder. The method further includes calculating, based at least in part on the cardable spend and the transaction data, a carded spend share that the first cardholder spent using a first payment device over the payment processing network; determining at least one consumer spend analytic based on the carded spend share; and reporting the carded spend share and the at least one consumer spend analytic.