Predictive Indicator for Wireless Network Customer Satisfaction
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Solution Overview
Problem
Traditional Net Promoter Score (NPS) surveys are costly, obtrusive, and sporadic, covering only a subset of customers, requiring human responses and not providing continuous customer satisfaction measurement for wireless service providers.
Innovation Solution
A system and method using a radio access network with a network load traffic management component to generate a predictive indicator based on network performance metrics, such as quality of service and behavioral analytics, eliminating the need for human intervention and enabling continuous monitoring of customer satisfaction across the entire customer base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NPS surveys are used to measure customer satisfaction, then customer loyalty can be assessed, but the process becomes costly and obtrusive
Solution Approach 1:
The patent replaces the mechanical survey system (questionnaires, human responses) with an automated electronic system that collects network performance data, behavioral analytics, and transaction information to calculate predictive indicators and Net Promoter Scores automatically, eliminating the need for manual survey distribution and analysis
Solution Approach 2:
The system enables self-service by automatically collecting data from multiple sources (network events, customer interactions, transaction records) and computing satisfaction metrics without requiring customer participation in surveys, allowing the organization to self-generate intelligence about customer sentiment
2Measurement precision
If traditional NPS surveys are conducted, then customer satisfaction can be measured, but coverage is limited to a subset of customers and performed sporadically
Solution Approach 1:
The system implements continuous measurement by constantly collecting network performance data, monitoring customer interactions, and updating predictive indicators in real-time, replacing sporadic survey campaigns with an ongoing, continuous intelligence-gathering process that covers the entire customer base
Solution Approach 2:
The system achieves universal coverage by designing a multi-functional platform that handles data collection from diverse sources (network events, customer service interactions, transaction systems), processes multiple types of analytics, and generates comprehensive satisfaction metrics for all customers simultaneously, rather than sampling subsets
3Loss of information
If traditional NPS surveys require human responses, then direct customer feedback is obtained, but the process becomes costly and requires framing questions
Solution Approach 1:
The system introduces intermediaries (predictive indicators, behavioral analytics models, network performance metrics) that mediate between raw customer actions and satisfaction measurement, translating implicit customer behavior into explicit satisfaction scores without requiring direct customer questioning or human interpretation
Solution Approach 2:
The patent replaces the mechanical process of question framing, survey distribution, and human response collection with an automated computational system that derives satisfaction metrics from objective data sources, eliminating the need for human survey design and administration while preserving feedback quality
Data Source
AI summary
A network load traffic management component is communicatively coupled to a radio access network (RAN). The network load traffic management component includes a subscriber scoring module and a subscriber scoring database that operates on a server processor and a server memory. The network load traffic management component identifies at least one network event corresponding to a RAN control plane. The network load traffic management component monitors each network event for a session time, in which each session time is associated with each subscriber interacting with the RAN. The network load traffic management component, repeatedly, determines a performance measurement for each network event associated with each session time. The performance measurement for each network event is recorded. A subscriber score is generated based on the performance measurements for each network event. A predictive indicator score is generated based on subscriber scores and behavioral analytics associated with each subscriber.