Integrated Service Centre Support System
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Solution Overview
Problem
Existing service delivery models, such as self-assist and agent-assist delivery models, are mutually exclusive, limiting the ability to integrate capabilities and effectively resolve customer queries in a holistic manner.
Innovation Solution
An integrated service centre support system that includes a receiver and an advisor, communicatively coupled with a database, uses machine learning to process service requests, generates intermediate responses, and solicits external system inputs to ensure accurate recommendations, enabling holistic integration of delivery models for efficient service delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If self-assist delivery models are used for full automation, then productivity is improved, but reliability deteriorates due to lack of human intervention for complex queries
Solution Approach 1:
The system dynamically switches between self-assist and agent-assist delivery models based on the complexity and type of service request. Simple requests are handled automatically by the machine learning system for high productivity, while complex requests are routed to human agents for reliable resolution, optimizing both speed and accuracy
2Reliability
If agent-assist delivery models are used for human intervention, then reliability is improved, but productivity deteriorates due to manual handling of all queries
Solution Approach 1:
The system segments service requests into different categories based on complexity, type, and required expertise. Routine, straightforward queries are segmented for automated handling by the ML system, while complex, nuanced queries are segmented for human agent handling, ensuring each request receives appropriate attention without unnecessary manual or automated overhead
3Ease of operation
If delivery models are kept mutually exclusive, then ease of operation is improved, but adaptability deteriorates due to inability to integrate capabilities
Solution Approach 1:
The system creates a universal service delivery platform that can perform both self-assist and agent-assist functions through a single integrated architecture. The machine learning system serves as a universal component that handles simple queries directly and routes complex queries to human agents, eliminating the need for separate mutually exclusive systems while maintaining operational simplicity
Data Source
AI summary
A curator captures input data corresponding to service tasks from an external source. Further, a browser extension collects intermediate service delivery data for the service tasks from the external source. Subsequently, a learner stores the input data and the intermediate service delivery data as training data. Then, a receiver receives a service request from a client. The service request is indicative of a service task to be performed and information associated with the service task. Further, an advisor processes the service request to generate an intermediate service response. Thereafter, the advisor determines a confidence level associated with the intermediate service response and ascertains whether the confidence level associated with service response is below pre-determined threshold level. If the confidence level is below a pre-determined threshold level, the advisor automatically generates a final service response corresponding to service request based on training data.