Systems and methods for forecasting campaign parameters using machine learning architectures and techniques

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.