Abstract: Devices, systems, and methods for consumer communication can include obtaining actual product traffic data concerning consumer products, applying a mixed model to determine an incremental product traffic value, and predicting as an output of a machine learning model, impending incremental product traffic based on the incremental product traffic values, and determining a ranking of each consumer product based on the impending incremental product traffic.

Abstract: Systems and methods are provided for receiving a data set that includes demographic data and population data; training, based on the data set, a neural network to establish a relationship between different physical layouts of messages and responses to the different physical layouts of the messages; applying the trained neural network to a user profile to predict a physical layout of a message; generating instructions for an electronic device based on the predicted physical layout of the message, the instructions comprising the message; and transmitting the instructions to the electronic device to create a physical label having a layout corresponding to the predicted physical layout of the message.

Abstract: Systems and methods are provided for receiving a data set that includes demographic data and population data; training, based on the data set, a neural network to establish a relationship between different physical layouts of messages and responses to the different physical layouts of the messages; applying the trained neural network to a user profile to predict a physical layout of a message; generating instructions for an electronic device based on the predicted physical layout of the message, the instructions comprising the message; and transmitting the instructions to the electronic device to create a physical label having a layout corresponding to the predicted physical layout of the message.

Abstract: Devices, systems, and methods for allocating consumer communications can include obtaining consumer activity data, designating a number of consumer communication campaigns concerning consumer features based on the consumer activity data, entering consumer activity data as inputs to one machine learning model for each determined consumer communication campaign, each machine learning model configured determine a campaign consumer activation profile based on the entered consumer activity data, and assigning the consumer communication campaigns to consumers based on the determined campaign consumer activation profiles.

Abstract: Devices, systems, and methods for managing inventory can include an inventory management system obtaining a gap-on-shelf status. The inventory management control system can determine an out-on-shelf profile based on the gap-on-shelf status, and can determine a response register based on the out-on-shelf status. The response register can include prioritized store-level actionable features for user access to conduct inventory management activities.