Techniques for applying predictive analytics to usage data in an event-driven architecture

A method of operating a system having an event-driven architecture that facilitates proactive engagement with a customer, the method comprising the steps of:

• storing instructions in a memory;
• storing prompt data of one or more customer actions in a data storage, wherein the prompt data is created by generating and storing prompts produced for and by a neural network-based model;
• and
• one or more processors executing the instructions to:
• receive customer input data from the customer;
• provide the customer input data to a streaming inference engine that identifies one or more customer usage patterns, wherein the streaming inference engine is trained on usage data associated with the customer to generate data structures comprising the one or more customer usage patterns;
• generate a customer servicing model based on the one or more customer usage patterns and the prompt data;
• store the customer servicing model in the data storage;
• provide an output to the customer based on the customer servicing model;
• and
• continuously update the customer servicing model stored in the data storage.