Context-Aware Customer Service Routing System
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Solution Overview
Problem
Television viewers face challenges in resolving issues related to their accounts or content selection, requiring assistance from customer service agents, but existing systems lack efficient methods for seamless communication and context sharing between customers and agents.
Innovation Solution
A network system that allows customers to initiate instant-message chats or audio/video calls with customer service agents, providing context information such as account details and browsing history to facilitate issue resolution, and routing calls based on agent expertise and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If customers contact customer service agents through traditional phone systems, then they can resolve issues through verbal communication, but they must repeatedly explain their problems and lose context information
Solution Approach 1:
The system performs preliminary actions by automatically gathering and preparing context information (account details, browsing history, issue descriptions) before the customer reaches the agent. This pre-prepared information is then presented to the agent, eliminating the need for repeated explanations during the interaction.
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between the customer and the agent. This intermediary automatically collects, processes, and transmits relevant context information, freeing both the customer and agent from the burden of repeated information exchange.
2Adaptability or versatility
If customer service agents receive all customer information manually, then they can provide personalized service, but the system complexity and processing time increase
Solution Approach 1:
The system enables self-service functionality by automatically gathering, organizing, and presenting relevant customer information without requiring manual collection by agents. The system serves itself by automating the information preparation process, reducing complexity while maintaining personalized service capability.
Solution Approach 2:
The system achieves multi-functionality by consolidating multiple information gathering tasks (account lookup, browsing history retrieval, issue tracking) into a single automated process. This universal approach handles diverse information needs through one integrated system, reducing overall complexity.
3Productivity
If the system provides detailed context information to agents, then issue resolution efficiency improves, but customer privacy concerns may increase
Solution Approach 1:
The system applies local quality by selectively providing different levels of information detail to different agents based on their specific needs and authorizations. Rather than uniformly disclosing all information, the system tailors the information presentation to each context, maintaining efficiency while protecting privacy through localized information disclosure.
Solution Approach 2:
The system implements feedback mechanisms that allow agents to request additional information only when necessary for resolving specific issues. This on-demand information retrieval approach maintains productivity by providing relevant details while minimizing unnecessary privacy exposure through controlled feedback loops.
Data Source
AI summary
A method may include receiving session control protocol messages to establish a session between a workstation and a user device. The session control protocol messages may include context information. The context information may include a browser history associated with the user of the user device. The method may further include establishing the session between the workstation and the user device. Further, the method may include displaying information on a display for an operator of the workstation, wherein the displayed information is based on the received context information.