Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for assessing a performance and/or determining a programmatically expected performance of a marketing campaign. In one embodiment a method for creating a unified data set from a plurality of data sets received from third-party service providers is disclosed. In one embodiment, a graphical user interface is used to facilitate user access to visual representation of the unified data set. In one embodiment, the unified data set may be used to train a machine learning model. In some implementations, the machine learning model may predict an expected performance for marketing campaigns. In one embodiment, the machine learning model may adjust one or more parameters of the marketing campaign in order to increase the effectiveness of the marketing campaign and the associated revenue.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for assessing a performance and/or determining a programmatically expected performance of a marketing campaign. In one embodiment a method for creating a unified data set from a plurality of data sets received from third-party service providers is disclosed. In one embodiment, a graphical user interface is used to facilitate user access to visual representation of the unified data set. In one embodiment, the unified data set may be used to train a machine learning model. In some implementations, the machine learning model may predict an expected performance for marketing campaigns. In one embodiment, the machine learning model may adjust one or more parameters of the marketing campaign in order to increase the effectiveness of the marketing campaign and the associated revenue.