AI Voice Mood Routing for Repeat Call Center Customers
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Solution Overview
Problem
Call centers face challenges in efficiently routing customer interactions due to high stress and emotional fatigue among employees, leading to decreased customer satisfaction and increased turnover, necessitating a solution that supports employee well-being and enhances operational efficiency.
Innovation Solution
A computer system utilizing artificial intelligence algorithms to analyze customer interactions, including audio and textual data, to determine a customer's mood and behavior score, and match them with suitable call agents, while also generating personalized interaction strategies and product recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional call routing methods are used, then call center operations continue with current processes, but employee stress and emotional fatigue increase leading to decreased customer satisfaction
Solution Approach 1:
The system changes the routing parameters from basic customer identification to a comprehensive behavioral score that incorporates mood indicators, historical interaction data, and emotional state analysis. This parameter transformation enables more nuanced agent-customer matching that considers employee well-being alongside customer satisfaction
Solution Approach 2:
The patent replaces traditional mechanical routing systems with an AI-driven analysis system that processes audio data, call logs, and customer feedback to generate behavioral scores. This substitution automates the complex task of evaluating both customer mood and agent suitability, reducing manual intervention while improving matching quality
2Productivity
If AI analysis of customer interactions is implemented, then optimal agent-customer matching is achieved, but system complexity increases
Solution Approach 1:
The AI system performs multiple functions within a single integrated platform: analyzing audio data for mood indicators, processing call log information, evaluating customer feedback, calculating behavioral scores, and generating routing recommendations. This multi-functionality consolidates what would otherwise require separate systems into one unified solution
Solution Approach 2:
The behavioral score acts as an intermediary metric that synthesizes complex multi-source data (audio analysis, call logs, feedback) into a single actionable value. This intermediary simplifies the decision-making process by providing a unified basis for routing decisions without requiring agents to directly interpret raw data from multiple sources
3Ease of operation
If comprehensive customer data analysis is performed, then personalized interactions are enhanced, but data processing time increases
Solution Approach 1:
The system performs preliminary analysis of customer data by maintaining running calculations of behavioral scores based on historical interaction data. When a new call arrives, the system has already processed and stored relevant customer information, mood patterns, and behavioral metrics, enabling rapid retrieval and matching without performing complete re-analysis of all historical data
Solution Approach 2:
The system continuously processes and updates customer behavioral data in the background during and between calls. This continuous action ensures that the most current behavioral scores and mood indicators are always available, eliminating the need for batch processing or interrupting service to perform data analysis
Data Source
AI summary
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.