Predictive Customer Care Framework for Telecom Ticket Resolution
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Solution Overview
Problem
Current customer care support systems face inefficiencies due to disjointed data sources and manual troubleshooting processes, leading to long wait times and increased operational costs for telecommunications service providers and customers.
Innovation Solution
A predictive customer care support framework that includes a decision engine for validating and categorizing trouble tickets, requesting relevant data from multiple sources, and using machine learning to identify root causes and implement solutions, thereby preemptively addressing potential issues before they become tickets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual troubleshooting processes are used with disjointed data sources, then technicians can access various information about customer accounts and service issues, but the time to resolve tickets increases and operational costs increase
Solution Approach 1:
The patent consolidates multiple disjointed data sources (customer accounts, service orders, network inventory, performance data, trouble tickets, and troubleshooting knowledge) into a unified data platform. This merging eliminates the need for technicians to manually access separate systems, directly reducing ticket resolution time while maintaining comprehensive information access for accurate problem diagnosis.
Solution Approach 2:
The system performs preliminary actions by automatically gathering and organizing relevant data from all sources before the technician begins troubleshooting. The unified data platform pre-processes and presents consolidated customer and service information, allowing technicians to immediately focus on resolution rather than data collection, thereby reducing overall ticket resolution time.
2Reliability
If technicians access multiple disjointed data sources to resolve tickets, then comprehensive customer information can be obtained, but the complexity of data management increases
Solution Approach 1:
The patent merges six distinct data sources into a single unified data platform, reducing system complexity from multiple disconnected systems to one integrated platform. This consolidation maintains complete customer information while simplifying data management architecture and reducing the operational complexity of accessing and correlating data across multiple systems.
3Productivity
If traditional customer care support processes are used, then service requests can be logged and resolved, but customer wait times increase and customer experience deteriorates
Solution Approach 1:
The unified data platform performs preliminary data consolidation and organization before tickets reach technicians. By pre-gathering all relevant customer account information, service order details, network inventory data, and historical troubleshooting knowledge, the system enables technicians to immediately begin resolution work, significantly reducing customer wait time while maintaining high productivity throughput.
4Reliability
If trial and error troubleshooting approaches are used, then problems can be identified and solved, but operational costs increase and resolution time increases
Solution Approach 1:
The system performs preliminary analysis by consolidating all relevant data including historical trouble tickets, network performance data, and troubleshooting knowledge before the technician begins work. This pre-prepared information context enables technicians to move directly from systematic problem analysis to targeted solutions, eliminating trial-and-error approaches and improving resolution efficiency while maintaining accurate problem identification.
Data Source
AI summary
Decreasing the time to resolution of trouble tickets and preemptively resolving potential network issues using a predictive customer care support framework communicatively coupled to a telecommunications service provider via a network is described. Techniques described herein include preprocessing ticket data associated with a trouble ticket corresponding to a subscriber using a decision engine that can implement a decision tree scheme to automatically identify appropriate data sources from which relevant data can be routed to the decision engine in a bundled format. The techniques further include detecting potential network issues not yet reported by subscribers using an analytical engine to identify a root cause for the potential network issues and implementing recommended solutions to reduce an influx of trouble tickets at a customer support terminal.