Emotion-Based Call Routing System for Customer Service
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing call center systems often route customer calls randomly or based on availability, leading to inappropriate representatives being assigned to handle frustrated or angry customers, which can prolong call times and lead to customer dissatisfaction and representative burnout.
Innovation Solution
A system that uses machine learning to determine a customer's emotion type based on interaction data, tone of voice, word choice, and talking speed, and routes calls to experienced representatives with high average call scores, while also updating representative scores based on call outcomes and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If calls are routed randomly or based on availability, then wait time to reach a representative is reduced, but call duration increases and customer satisfaction deteriorates
Solution Approach 1:
The system performs preliminary analysis of the customer's emotional state and call purpose before routing the call to a representative. By detecting emotions through voice analysis and analyzing call intent through speech recognition prior to connection, the system pre-matches customers with appropriate representatives, preventing both excessive wait times and prolonged call durations.
Solution Approach 2:
The patent replaces the mechanical random or availability-based routing system with an intelligent emotion-based routing system. This substitution uses machine learning models and voice biometric analysis to automatically determine customer emotional states and route calls accordingly, eliminating the need for manual or simplistic automated routing while improving both efficiency and effectiveness.
2Loss of energy
If inexperienced representatives handle all calls, then operational costs are reduced, but call quality and customer satisfaction worsen for complex or emotionally charged calls
Solution Approach 1:
The system applies local quality by matching specific customer needs with specific representative capabilities. Instead of uniformly assigning all calls to inexperienced representatives, the system analyzes each call's emotional complexity and intent, then routes to representatives with appropriate experience levels and skill sets for that particular situation, ensuring optimal call quality while managing costs.
Solution Approach 2:
The patent changes the routing parameter from simple availability or random assignment to a multi-dimensional assessment including customer emotional state, call intent, and representative expertise. This parameter transformation enables dynamic optimization of call quality by matching customers with appropriately skilled representatives based on real-time analysis rather than static cost considerations.
3Reliability
If experienced representatives handle all calls, then call quality improves, but representative frustration increases and staff retention worsens
Solution Approach 1:
The system ensures that experienced representatives are not uniformly overloaded with complex calls, but rather are strategically assigned to calls matching their expertise. By analyzing call complexity and emotional intensity, the system creates a balanced distribution where experienced handlers focus on challenging cases while simpler calls are routed to less experienced staff, preventing burnout and maintaining satisfaction.
Solution Approach 2:
The patent implements feedback mechanisms where representative performance and emotional state are continuously monitored. This feedback loop allows the system to adjust routing decisions in real-time, preventing experienced representatives from becoming overwhelmed with repetitive or overly simple calls that could cause frustration, thereby maintaining their engagement and reducing turnover.
4Device complexity
If simple routing systems are used, then system complexity is reduced, but routing accuracy and customer experience worsen
Solution Approach 1:
The patent replaces simple mechanical routing logic with intelligent voice biometric analysis and machine learning-based emotion detection systems. These advanced technologies automatically analyze customer voice patterns, emotional states, and call intent, providing highly accurate routing decisions without requiring complex manual configuration or multiple manual steps, thus achieving high precision with manageable system complexity.
Data Source
AI summary
A system may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.