| CPC G06Q 30/0271 (2013.01) [G06N 3/088 (2013.01); G06Q 30/0255 (2013.01); H04L 41/16 (2013.01)] | 20 Claims |
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1. A system for improving distributed network data flow efficiency using machine learning, the system comprising:
an agent device configured to wirelessly communicate with at least one user device, wherein the agent device is configured to execute a software application;
a computer with one or more processor and memory, wherein the computer executes computer-readable instructions to guide interactions between the agent device and the at least one user device; and
a network connection operatively connecting the agent device, the at least one user device, and the computer, the network connection configured to permit network data flow between the agent device, the at least one user device, and the computer;
wherein, upon execution of the computer-readable instructions, the computer is configured to:
train a machine learning program to predict a targeted action that can be communicated through digital communication and has an increased likelihood of user engagement, the training including:
iteratively simulating a prediction of a target variable value using training test data;
comparing and testing the prediction to the target variable value; and
iteratively updating weights in calculations used to improve predictability of the target variable value during each subsequent iteration;
deploy the trained machine learning program as a predictive model;
initiate providing, via a user interface of a user device of the at least one user device, a user software application to a user for installation on the user device, wherein the user device is configured to wirelessly communicate with the computer via the user software application;
receive, via the user software application installed on the user device, personal data of the user, wherein the personal data includes financial transactions by the user and facilitated by the system;
generate a personal data set based upon the personal data of the user, the personal data set comprising data relating to responses to queries, where the queries are posed to the user by the computer and linked to a particular topic, where the particular topic is included as part of an organized campaign;
apply the personal data set of the user to the predictive model to identify a predicted targeted action likely to engage the user, wherein identification of the predicted targeted action incorporates a change in value of a specific data entry of the personal data set due to a specific response to a query of the responses to queries;
identify, based on applying the personal data set to the predictive model, a user-specific targeted action predicted to have an increased probability of positive engagement with the user such that digital communications transmitted to the user device are more user-specific;
transmit, via the network connection, a communication including the user-specific targeted action to the agent device, wherein at least one triggering condition causes the communication including the user-specific targeted action to be provided to the agent device;
initiate displaying the communication including the user-specific targeted action on the agent device;
transmit the communication comprising the user-specific targeted action to the user device; and
initiate displaying, via the user software application, the user-specific targeted action on a graphical user interface of the user device.
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