| CPC G06Q 30/0202 (2013.01) [G06Q 30/016 (2013.01)] | 17 Claims |
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1. A system for micro-movement personalization across channels, the system comprising:
one or more memory devices having computer readable code stored thereon;
one or more processing devices operatively coupled to the one or more memory devices, wherein the one or more processing devices are configured to execute the computer readable code to:
receive a request from a customer to generate a custom application;
determine customer accounts for inclusion in the custom application;
generate the custom application and create custom interfaces and channels, wherein the custom application comprises the customer accounts;
generate an application builder interface for the customer to customize the custom interfaces and channels;
identify customer acceptance level with channel personalization, wherein identifying the customer acceptance level further comprises performing clustering analysis of the customer and generation of a forecasting database, wherein the clustering analysis uses a machine learning engine configured to assign a customized level confidence providing the acceptance level the customer has with an amount of personalization on the channels and a type of personalization on the channels, wherein the type of personalization on the channels comprises aesthetics, textures, color, and font;
generate a persona for the customer acceptance level with channel personalization utilizing a rules set and clustering datasets;
forecast the channel personalization utilizing the machine learning engine, the persona, and the rules set, wherein the rules set comprises customer accounts, customer inputs, and comparing similar customers and the customer inputs;
provide the customer with a value exchange for an additional personalization of a customer interaction, wherein the additional personalization of the customer interaction allows for customer control of use and distribution of information;
receive inputs from the customer for the additional personalization of a customer interaction and updating the custom interfaces and the channels via the application builder interface;
execute updates to the custom interfaces and the channels based on the inputs from the customer;
present a customized level of personalization of channels to the customer, wherein the customized level of personalization of the channels is based on a customization level confidence, the persona, and the additional personalization of the customer interaction;
monitor the level of personalization of channels for the customer;
perform correction adjustment to the level of personalization of channels based on the monitoring;
capture micro-movements to determine micro-changes to customer responses based on colors, regional dialects for audio presentation, fonts, and items displayed on screens;
perform micro-movement analysis to capture micro changes to the customer responses to channels and perform real-time micro-movement adjustments to the personalization of the channels, wherein the real-time micro-movement adjustments modify patterns in the regional dialect, the items displayed on screens, and the fonts and colors of the channels; and
provide a feedback loop model for customer acceptance level to the forecasting database based on the customer acceptance, use of the personalization of the channels, and the persona.
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