Call Center Routing Engine Using Self-Service History Analysis
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Solution Overview
Problem
Conventional call centers often route customers to inappropriate agents due to poor navigation through self-service options, leading to costly transfers and negative customer experiences.
Innovation Solution
Recording and analyzing customer self-service history to route contacts to generalists when necessary, avoiding unnecessary transfers by using the routing engine to interpret customer behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If customers navigate self-service options in conventional call centers, then self-service functionality is provided, but customers may be routed to inappropriate agents leading to costly transfers
Solution Approach 1:
The system performs preliminary analysis of customer self-service navigation behavior before final routing decisions. By evaluating how customers interact with self-service options in advance, the system predicts appropriate agent routing and prevents misrouting before it occurs, thereby maintaining automation while improving routing reliability
Solution Approach 2:
The system implements feedback mechanisms by monitoring customer interactions with self-service options and using this information to improve routing decisions. The behavioral data collected from self-service navigation provides feedback that enhances the accuracy of agent routing, resolving the contradiction between automation and reliability
2Ease of operation
If customers are transferred to appropriate agents based on self-service history, then customer experience is improved, but additional routing logic and data collection are required
Solution Approach 1:
The routing system is designed to perform multiple functions: it not only routes calls based on traditional criteria but also analyzes self-service navigation behavior, predicts customer needs, and determines optimal agent routing. This multi-functionality allows the system to improve customer experience while consolidating complexity into a single unified routing platform
Solution Approach 2:
The system introduces an intermediary routing layer that sits between the self-service interface and agent network. This intermediary analyzes customer behavior and makes intelligent routing decisions, simplifying the overall system architecture by centralizing the decision-making logic rather than requiring complex direct connections between all components
Data Source
AI summary
A method, apparatus and computer program product for determining customer routing in a call center is presented. Information relating to a customer in a communication session with the call center is recorded. The information relating to the customer in a communication session with the call center is evaluated and a determination made whether to transfer the customer to a generalist. The customer is transferred to a generalist when a result of the determining is that the customer should be transferred to a generalist and the customer is not transferred to a generalist and when the result of the determining is that the customer should not be transferred to a generalist.