Q-score Regression Model Predicts Customer Complaints
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Solution Overview
Problem
Existing communication systems face challenges in proactively managing user-perceived quality of service, particularly in identifying service quality degradation and predicting customer complaints, due to varying user expectations and responses to service issues across different networks and devices.
Innovation Solution
A Q-score system that utilizes regression analysis to quantify relationships between network performance indicators, user feedback, and user activity, enabling proactive assessment and prediction of service quality issues, and adaptive allocation of customer care resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network performance indicators are monitored and analyzed to predict service quality issues, then proactive management of service quality is improved, but system complexity increases
Solution Approach 1:
The patent introduces a Q-score as an intermediary metric that simplifies the complex relationship between multiple network performance indicators and user-perceived service quality. The Q-score acts as a mediator that aggregates various network indicators (packet loss, jitter, latency) into a single predictive score, reducing system complexity while maintaining proactive service quality management capabilities.
Solution Approach 2:
The patent transforms multiple complex network performance parameters into a simplified Q-score parameter that directly predicts service quality issues. By changing the parameter representation from raw network indicators to a derived Q-score, the system reduces complexity while preserving the ability to monitor and manage service quality proactively.
2Measurement precision
If multiple network performance indicators are collected and analyzed, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple network performance indicators (packet loss, jitter, latency, throughput) into a single integrated Q-score metric. This combining approach maintains prediction accuracy by preserving the informational content of all original indicators while significantly reducing data processing complexity through aggregation and dimensionality reduction.
Solution Approach 2:
The patent extracts the essential predictive information from multiple complex network indicators and concentrates it into the Q-score. By taking out only the critical predictive components and discarding redundant information, the system maintains high prediction accuracy while reducing data processing requirements.
3Reliability
If real-time monitoring of network performance is implemented, then proactive issue detection is improved, but computational resources consumed increase
Solution Approach 1:
The patent applies partial action by monitoring only the most critical network performance indicators that have the highest impact on service quality prediction, rather than continuously monitoring all possible network parameters. This selective monitoring approach maintains effective proactive issue detection while reducing computational resource consumption.
Solution Approach 2:
The patent creates a simplified copy of the complex network performance data in the form of the Q-score. Instead of processing all raw network indicator data in real-time, the system uses the pre-computed Q-score as a representative copy that captures essential service quality information, reducing computational resources while maintaining detection capability.
Data Source
AI summary
A system that incorporates teachings of the present disclosure may include, for example, obtaining regression coefficients that quantify a relationship between premises feedback and first network and premises performance indicators, obtaining second network performance indicators for the network elements, obtaining second premises performance indicators for the customer premises equipment, and predicting customer complaints by applying the obtained regression coefficients to at least the second network performance indicators and the second premises performance indicators. Other embodiments are disclosed.