Propensity-Based Call Volume Reduction via Targeted Service Content

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Solution Overview

Problem

Telecommunication service providers face high costs due to frequent customer calls to call centers for issues that could be resolved with proper guidance, and existing methods lack efficiency in identifying and addressing customers with a high propensity to call regarding specific reason codes.

Innovation Solution

A system that identifies customers with a high propensity to call by analyzing shared traits using machine learning algorithms based on network event data and customer account data, and delivers automated customer service content to prevent unnecessary calls, utilizing a processor to determine reason codes, shared traits, and communication modalities for personalized service delivery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated customer service content is delivered to customers with high propensity to call, then call volume reduction is achieved, but system complexity increases due to machine learning algorithms and data analysis requirements

Engineering Contradiction:

Improvecall volume reductionVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of network event data and customer account data to identify customers with high propensity to call before they actually call. By proactively delivering customer service content in advance, the system prevents calls from occurring, thus reducing call volume while using automated processing to manage complexity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables customers to serve themselves by delivering relevant customer service content directly to them based on their propensity scores and matched reason codes. This self-service approach reduces the need for human agent intervention and handles complexity through automated content delivery rather than complex human coordination

Inventive Principle:

Principle #25Self-service

2Productivity

If machine learning algorithms are used to identify customers with high propensity to call, then call center resource optimization is achieved, but data processing requirements and computational resources increase

Engineering Contradiction:

Improvecall center resource optimizationVSAvoidcomputational resources

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system changes parameters by analyzing multiple data dimensions (network event data, customer account data) and transforming them into a propensity to call score. This parameter transformation allows the system to identify high-risk customers efficiently and allocate computational resources selectively to those customers rather than processing all customer data uniformly

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system applies local quality by focusing computational resources on specific customers with high propensity scores rather than uniformly processing all customer data. By matching reason codes and delivering targeted content only to relevant customers, the system optimizes resource usage while maintaining effective call volume reduction

Inventive Principle:

Principle #3Local quality

3Ease of operation

If personalized customer service content is delivered via determined communication modalities, then customer satisfaction is enhanced, but data privacy and security requirements increase

Engineering Contradiction:

Improvecustomer satisfactionVSAvoiddata privacy and security

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system applies local quality by determining and delivering customer service content through communication modalities that are specific to each customer's preferences and characteristics. This personalized approach enhances customer satisfaction while maintaining data security by using established, secure communication channels that are already trusted by each customer

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS10412218B2Call volume reduction based upon a propensity to call in connection with a reason code

Publication Date: 2019.09.10 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Devices, computer-readable media and methods for delivering customer service content associated with a reason code are disclosed. Examples of the present disclosure may include a processor of a telecommunication network identifying a reason code associated with calls from customers to a customer call center and determining a set of shared traits among the customers. The shared traits may be based upon first network event data and first customer account data associated with the customers. The processor may further determine a customer with a propensity to call score that exceeds a threshold and with a customer profile that matches the set of shared traits. The customer profile may be based upon second network event data and second customer account data associated with the customer. The processor may further deliver a customer service content associated with the reason code to the customer via a communication modality that is determined for the customer.