Predictive Call Routing Using Sentiment and Performance Scores
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Solution Overview
Problem
Conventional call routing systems randomly route calls to customer service representatives, leading to unpredictable experiences for callers, as they cannot predict whether the selected representative will provide a positive or negative experience based on the caller's needs.
Innovation Solution
The implementation of predictive mapping techniques that utilize sentiment scores, experience scores, and performance scores to identify the most compatible customer service representative for each caller, ensuring a positive experience by mapping callers to representatives with optimal personal compatibility based on the topic of conversation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional call routing systems randomly route calls to customer service representatives, then calls can be distributed evenly among available representatives, but the caller experience becomes unpredictable and may be negative
Solution Approach 1:
The system performs preliminary actions by pre-assessing caller needs through IVR interactions and pre-evaluating representative competencies before the actual call routing decision is made. This allows the system to predict compatibility and make informed routing decisions rather than random assignments.
Solution Approach 2:
The system implements feedback mechanisms by analyzing call outcomes and using this information to continuously improve routing predictions. The system learns from past interactions to refine its understanding of which representatives are best suited for specific caller needs, thereby improving experience consistency.
2Reliability
If predictive mapping techniques are implemented to identify the most compatible customer service representative for each caller, then caller experience consistency improves, but system complexity increases
Solution Approach 1:
The system segments the call routing process into distinct functional modules: IVR-based caller need assessment, representative competency evaluation, compatibility prediction algorithms, and routing decision execution. This modular segmentation manages complexity by making each component independent and manageable.
Solution Approach 2:
The system introduces an intermediary predictive mapping layer between the caller and representative. This intermediary component analyzes compatibility metrics and makes intelligent routing decisions, simplifying the overall system architecture while improving outcomes compared to direct random routing.
3Object-affected harmful factors
If predictive mapping is used to select representatives with optimal personal compatibility, then negative interactions are minimized, but processing time for call routing increases
Solution Approach 1:
The system performs preliminary assessments of both caller needs and representative competencies before calls are routed. By pre-establishing compatibility metrics and maintaining updated profiles, the system reduces the processing time required at the moment of routing while still achieving optimal matching.
Solution Approach 2:
The system changes parameters by using standardized compatibility metrics and pre-calculated competency scores. This allows for rapid comparison and decision-making based on predefined thresholds, reducing processing time while maintaining the ability to minimize negative interactions.
Data Source
AI summary
Predictive mapping technology is used to route a telephone call from a user to a customer service representative. The disclosed technology can use any one or more of the following factors to map a telephone call from a user to a customer service representative: (1) a sentiment score based on a topic of conversation; (2) an experience score of the customer service representative with a topic of conversation; and (3) a performance score of the customer service representative in managing a topic of conversation.