US-12621387-B2 - Voice analysis-based agent routing

US12621387B2US 12621387 B2US12621387 B2US 12621387B2US-12621387-B2

Abstract

A computer system and method for improving call center routing through analysis of customer interactions including obtaining identifying information for a caller upon initiation of a call, identifying the caller as a repeat customer using the identifying information, retrieving historical interaction data associated with the repeat customer from a database, analyzing any combination of customer audio data, customer call log information, or customer feedback, utilizing an artificial intelligence algorithm to determine a current mood indicator of the customer, calculating a customer behavior score for the repeat customer based on the historical interaction data and the current mood indicator of the customer, and matching the repeat customer to a call agent, based on the customer behavior score.

Inventors

Parul Ghosh

Assignees

WELLS FARGO BANK, N.A.

Dates

Publication Date: 20260505
Application Date: 20240426

Claims (20)

1 . A method for improving call center routing through analysis of customer interactions, the method comprising: obtaining identifying information for a caller upon initiation of a call; identifying the caller as a repeat customer using the identifying information; retrieving historical interaction data associated with the repeat customer; analyzing the historical interaction data, utilizing an artificial intelligence algorithm, to determine a current mood indicator of the repeat customer; calculating a customer behavior score within a predefined numeric range for the repeat customer based on the historical interaction data and the current mood indicator of the repeat customer, wherein the customer behavior score quantifies quantitative and qualitative aspects of the historical interaction data and the current mood indicator for the repeat customer; and matching the repeat customer to a call agent based on the customer behavior score.
2 . The method of claim 1 , further comprising analyzing agent audio data of the call agent to determine a current agent sentiment indicator.
3 . The method of claim 2 , wherein matching the repeat customer to the call agent considers both the customer behavior score and the current agent sentiment indicator.
4 . The method of claim 2 , further comprising providing a notification upon the current agent sentiment indicator meeting or exceeding a defined threshold.
5 . The method of claim 1 , further comprising creating call handling instructions for the call agent, based on the customer behavior score.
6 . The method of claim 5 , wherein the call handling instructions include a chat template for directing a conversation with the repeat customer, based on the customer behavior score.
7 . The method of claim 1 , further comprising identifying one or more products or services for promotion to the repeat customer, based on at least one of the historical interaction data or the current mood indicator.
8 . The method of claim 1 , wherein the artificial intelligence algorithm employs both a speech emotion recognition model and an emotional sentiment model.
9 . The method of claim 8 , wherein the speech emotion recognition model is configured to analyze at least one of a pitch, volume or speech rhythm of customer audio data.
10 . The method of claim 8 , wherein the emotional sentiment model is configured to analyze at least one of a word choice, grammatical choice or inflection of customer audio data.
11 . A computer system for improving call center routing through analysis of customer interactions, comprising: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, cause the computer system to: obtain identifying information for a caller upon initiation of a call; identify the caller as a repeat customer using the identifying information; retrieve historical interaction data associated with the repeat customer from a database; analyze any combination of customer audio data, customer call log information, or customer feedback, utilizing an artificial intelligence algorithm to determine a current mood indicator of the repeat customer; calculate a customer behavior score within a predefined numeric range for the repeat customer based on the historical interaction data and the current mood indicator of the repeat customer, wherein the customer behavior score quantifies quantitative and qualitative aspects of the historical interaction data and the current mood indicator for the repeat customer; and match the repeat customer to a call agent, based on the customer behavior score.
12 . The computer system of claim 11 , wherein the computer system is further configured to analyze agent audio data of the call agent to determine a current agent sentiment indicator.
13 . The computer system of claim 12 , wherein matching the repeat customer to the call agent considers both the customer behavior score and the current agent sentiment indicator.
14 . The computer system of claim 12 , wherein the computer system is further configured to provide a notification upon the current agent sentiment indicator meeting or exceeding a defined threshold.
15 . The computer system of claim 11 , wherein the computer system is further configured to create call handling instructions for the call agent, based on the customer behavior score.
16 . The computer system of claim 15 , wherein the call handling instructions include a chat template for directing a conversation with the repeat customer, based on the customer behavior score.
17 . The computer system of claim 11 , wherein the computer system is further configured to identify one or more products or services for promotion to the repeat customer, based on at least one of the historical interaction data or the current mood indicator.
18 . The computer system of claim 11 , wherein the artificial intelligence algorithm employs both a speech emotion recognition model and an emotional sentiment model.
19 . The computer system of claim 18 , wherein the speech emotion recognition model is configured to analyze at least one of a pitch, volume or speech rhythm of the customer audio data.
20 . The computer system of claim 18 , wherein the emotional sentiment model is configured to analyze at least one of a word choice, grammatical choice or inflection of the customer audio data.

Description

BACKGROUND In the evolving landscape of customer service and support, call centers serve as an important interface between businesses and their customers. The environment in call centers, characterized by high volumes of interactions often with frustrated or dissatisfied customers, leads to significant stress, anxiety, and emotional fatigue among employees. This affects their ability to provide high-quality service, resulting in decreased customer satisfaction and loyalty, increased employee turnover, and ultimately, negative impacts on the company's brand and financial performance. The demand for quick resolution of customer issues, combined with the complexity of these interactions, necessitates a solution that supports the employees' mental well-being and enhances their operational efficiency. SUMMARY Embodiments of the disclosure are directed to improving call center routing through analysis of customer interactions, including obtaining identifying information for a caller upon initiation of a call, identifying the caller as a repeat customer using the identifying information, retrieving historical interaction data associated with the repeat customer from a database, analyzing any combination of audio data, call log information, or customer feedback, utilizing an artificial intelligence algorithm to determine a current mood indicator of the customer, calculating a customer behavior score for the repeat customer based on the historical interaction data and the current mood indicator of the customer, and matching the repeat customer to a call agent, based on the customer behavior score. Embodiments also encompass a computer system for managing security vulnerabilities in software development. The computer system is equipped with one or more processors and non-transitory computer-readable storage media which, when executed by the one or more processors, cause the computer system to obtain identifying information for a caller upon initiation of a call, identify the caller as a repeat customer using the identifying information, retrieve historical interaction data associated with the repeat customer from a database, analyze any combination of audio data, call log information, or customer feedback, utilizing an artificial intelligence algorithm to determine a current mood indicator of the customer, calculate a customer behavior score for the repeat customer based on the historical interaction data and the current mood indicator of the customer, and match the repeat customer to a call agent, based on the customer behavior score. The details of one or more techniques are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of these techniques will be apparent from the description, drawings, and claims. DESCRIPTION OF THE DRAWINGS FIG. 1 shows an example of a computer system for improving call center routing through analysis of customer interactions. FIG. 2 shows an example call handling recommendation device of the computer system of FIG. 1. FIG. 3 shows an example speech emotion recognition module of the call handling recommendation device of FIG. 2. FIG. 4 shows an example emotion sentiment model module of the call handling recommendation device of FIG. 2. FIG. 5 shows a method for improving call center routing through analysis of customer interactions. FIG. 6 shows example physical components of the call handling recommendation device of FIG. 2. DETAILED DESCRIPTION This disclosure relates to improving call center routing through analysis of customer interactions. The disclosed system enhances call center routing efficiency by analyzing customer interactions. The system begins with a collection of identifying information as a call is made, which facilitates recognition of repeat customers. The system can then retrieve a customer's historical interaction data from a comprehensive database. Utilizing artificial intelligence algorithms, the system can analyze a combination of customer audio data, call log information, and/or customer feedback to determine a current mood indicator of the customer. Based on this analysis, a customer behavior score can be calculated, reflecting the customer's historical interactions and current emotional state. This score can then guide the system in matching the customer with a call agent whose expertise aligns with the customer's needs and mood, thereby improving a potential for any interaction between the customer and the call agent to be completed successfully and according to customer protocol guidelines. In some embodiments, the system can additionally be configured to analyze call agent audio data to determine a current sentiment indicator of the call agent. Determination of a current sentiment indicator enables the matching process to account for both the customer behavior score and the agent's current sentiment, improving interaction outcomes. In scenarios where the current agent sentiment indicator crosses a predefined