Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that cause the one or more processors to perform: training a machine learning model using one or more training features, wherein each training feature of the one or more training features is segmented in a respective group of one or more groups by a respective time period; executing, after training the machine learning model, the machine learning model to generate a forecasting system to predict at least a predicted return on an ad spend (ROAS) value; and providing access to the forecasting system to predict one or more input parameters for an electronic advertising campaign to forecast one or more campaign predictions associated with the electronic advertising campaign. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing access to a forecasting system that includes a machine learning model configured to forecast one or more campaign predictions associated with an electronic advertising campaign; extracting historical campaign data from one or more databases using a distributed processing system; generating training features for training the machine learning model; executing a training procedure that is configured to train the machine learning model, at least in part, using the training features; and executing, after completion of the training procedure, the machine learning model to forecast a predicted ROAS value or a predicted budget value. Other embodiments are disclosed herein.