Proactive Customer Experience Management in Communication Networks
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Solution Overview
Problem
Customer experience management systems in communication networks struggle to provide real-time troubleshooting and proactive monitoring of key performance indicators, leading to delayed identification of root causes and potential service degradation issues, which can result in poor customer satisfaction and loss of subscribers.
Innovation Solution
A proactive customer experience management method that includes obtaining performance-indicating alerts, identifying relevant issues, gathering trace data, determining root causes, and providing recommendations for resolution, using a rule-based selective tracing mechanism and analytics to automate fault detection and root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional customer experience management systems are used, then system simplicity is maintained, but real-time troubleshooting capability and root cause identification speed deteriorate
Solution Approach 1:
The system segments the troubleshooting process into distinct functional modules: alert generation module, correlation engine, root cause analysis module, and resolution recommendation module. Each module handles specific tasks independently, enabling real-time processing while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary correlation engine that acts as a mediator between alert generation and root cause analysis. This intermediary component correlates multiple alerts and performance data before passing them to the root cause analysis module, enabling faster identification without overwhelming the system with raw data.
2Measurement precision
If proactive monitoring of all key performance indicators is implemented, then customer experience measurement completeness is improved, but data processing load and system resource consumption increase
Solution Approach 1:
The system implements partial monitoring by focusing on critical key performance indicators that have the highest impact on customer experience. Rather than monitoring all possible parameters continuously, the system selectively monitors essential metrics with appropriate thresholds, reducing resource consumption while maintaining measurement completeness for critical issues.
Solution Approach 2:
The system employs self-service mechanisms through automated alert generation and correlation. The correlation engine automatically processes performance data, identifies patterns, and generates alerts without requiring continuous human intervention or excessive processing resources, enabling efficient proactive monitoring.
3Loss of time
If manual troubleshooting processes are used, then system complexity is reduced, but troubleshooting time and customer satisfaction deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-configuring correlation rules, alert thresholds, and root cause analysis logic before issues occur. The automated system is prepared in advance with predefined patterns and procedures, enabling immediate troubleshooting when problems arise without requiring complex real-time decision-making, thus reducing troubleshooting time while managing complexity through pre-planning.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors performance data, compares it against predefined thresholds and patterns, and automatically generates alerts and recommendations. This closed-loop feedback system enables automated troubleshooting by continuously adjusting and responding to network conditions, reducing troubleshooting time while maintaining manageable complexity through rule-based decision-making.
Data Source
AI summary
The present disclosure relates to methods and systems for improving customer experience through real time troubleshooting in relation to customer experience management. In one embodiment, a proactive customer experience management method is disclosed, comprising: obtaining a performance-indicating alert (PA); identifying relevant alerts from the alert database in absence of possible fault condition from the PA; determining a possible problem condition from the PA and identified relevant alerts; raising trace trigger for gathering relevant trace data; determining specific problem condition and relevant cause, based on gathered trace data and relevant data from PM/FM, CDR, OSS systems; determining appropriate recommendation for resolution of the determined specific problem condition; updating a user interface dashboard using the determination of the root cause of the possible problem and the recommendation for resolution of the possible problem; and updating new knowledge into a knowledge base with problem-context, resolution, relevant adjustments to alerts, thresholds and rules.