AI Engine Predicting Call Drivers via Digital Footprints

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Solution Overview

Problem

Call centers face challenges in predicting the intent behind customer calls, leading to inefficiencies in response and service quality.

Innovation Solution

A method involving the construction of a digital footprint from customer data, inputting it into an AI engine, and obtaining probability values to predict the call driver, which is then provided to the call center.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional call center methods are used without AI prediction, then the system is simpler and easier to operate, but the quality of service and ability to predict customer intent deteriorates

Engineering Contradiction:

Improvequality of serviceVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system constructs a digital footprint of the customer before the call occurs, pre-processing customer data including digital breadcrumbs from enterprise data warehouse. This preliminary action enables the AI engine to immediately predict call drivers when the call is received, improving service quality without adding complexity during the actual call handling

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

An AI engine serves as an intermediary between the call center system and customer data. The AI engine receives the digital footprint as input and outputs predicted call drivers, acting as a mediator that translates raw customer data into actionable insights for call center agents without requiring them to directly process complex data structures

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If AI prediction systems are implemented to predict call drivers, then the quality of service and customer satisfaction improves, but the loss of time for data processing and system setup increases

Engineering Contradiction:

Improvecustomer satisfactionVSAvoiddata processing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The digital footprint construction and data retrieval from enterprise data warehouse are performed in advance before the call is received. This pre-processing eliminates the need for real-time data collection during the call, ensuring that the AI prediction can be made quickly when the call arrives, thus improving customer satisfaction without significant time loss

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Manual data gathering and analysis processes are replaced with an automated AI engine that processes the digital footprint and generates call driver predictions. This substitution of mechanical human processes with automated computational processes reduces the time required for data processing while maintaining high accuracy in customer satisfaction prediction

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12278929B2Machine-learning system for incoming call driver prediction

Publication Date: 2025.04.15 CHARLES SCHWAB & CO INC

AI summary

A method includes selecting a customer of a company; constructing a digital footprint of the selected customer. The method includes inputting the digital footprint to an artificial intelligence (AI) engine. The method includes obtaining one or more probability values from the AI engine based on the input digital footprint. The method includes selecting a call driver, from among a plurality of call drivers, as a predicted call driver. The method includes providing the predicted call driver to a call center associated with the company.