Customer Experience Scoring for Spatial Clusters in Heterogeneous Networks
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Solution Overview
Problem
Existing network optimization methods in telecommunications networks focus on improving Key Performance Indicators (KPIs) of network elements without considering the actual locations of customers with poor experience, leading to ineffective customer experience improvement.
Innovation Solution
A system and method to identify customers with poor experience by aggregating and clustering metrics such as signal quality, strength, interference, and drop occurrences to derive a Customer Experience (CE) score, enabling targeted network optimization by pinpointing spatial clusters of poor experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network optimization focuses on improving KPIs of network elements, then network element performance is improved, but actual customer experience improvement is not achieved
Solution Approach 1:
The patent introduces an intermediary layer between network element monitoring and customer experience assessment. This intermediary aggregates multiple network metrics (signal strength, signal quality, interference, cell throughput, drop occurrences, mute occurrences) and combines them with customer feedback to create a comprehensive Customer Experience Score. This mediator translates technical network parameters into meaningful customer experience indicators, resolving the disconnect between network element performance and actual customer experience.
Solution Approach 2:
The patent transforms the optimization focus from traditional network KPIs to customer experience metrics. By changing the parameter set from purely technical indicators (signal strength, throughput) to composite customer experience scores that incorporate both network performance and customer feedback, the system enables optimization decisions that directly impact customer experience rather than just network element performance.
2Productivity
If network optimization improves overall network performance, then network capacity increases, but identification of specific problem areas is lost
Solution Approach 1:
The patent segments the network optimization problem by creating a hierarchical structure: first identifying individual customers with poor experience through their CE scores, then clustering these customers into spatial groups. This segmentation allows the system to maintain overall network capacity monitoring while simultaneously pinpointing specific geographic problem areas. The clustering algorithm groups customers based on their locations and experience patterns, enabling targeted optimization of specific zones rather than treating the network as a monolith.
3Reliability
If traditional network optimization methods are used, then network element performance is tracked, but spatial clustering of poor experience customers is not identified
Solution Approach 1:
The patent adds a spatial dimension to traditional network optimization by incorporating geographic location data into the customer experience assessment. While traditional methods track network element performance in isolation, this patent overlays customer locations and experience scores onto a geographic map, creating spatial clusters of poor experience. This dimensional addition transforms one-dimensional network performance data into two-dimensional spatial information, enabling identification of geographic problem zones that were previously invisible.
Data Source
AI summary
The present disclosure provides novel solution for Network Optimization in telecommunications network that has traditionally always been driven by measuring and improving Key Performance Indicators (KPIs) of network elements vis-à-vis advancement for identification of the customers with poor experience and identification of spatial clusters of these customers to pinpoint the exact location of the problem, allowing for more targeted network optimization. The systems and methods contained in this invention enable the identification of these customers with poor experience and identifies spatial clusters of these customers to pinpoint the exact location of the problem, allowing for more targeted network optimization. This disclosure provides solution by aggregating a multitude of metrics pertinent to the user's voice, data and coverage experience and deriving a single KPI, it is possible to benchmark and correspondingly track and improve their experience.