Service Configuration Optimization via Regression Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Telecommunication customers often experience dissatisfaction due to rate plans not meeting their needs, leading to frequent reconfigurations and potential provider changes, resulting in revenue loss and user dissatisfaction.
Innovation Solution
A method and system that rank users based on reconfiguration frequency, using a linear regression model to identify optimal user configurations for services, reducing reconfigurations by providing personalized service plans tailored to individual needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standardized rate plans are offered to all customers, then operational simplicity is maintained, but customer satisfaction deteriorates due to mismatched service configurations
Solution Approach 1:
The system dynamically changes service configuration parameters based on customer behavior patterns. By analyzing reconfiguration frequency and applying linear regression models, the system automatically adjusts service parameters to match individual customer needs, transforming standardized plans into personalized configurations without manual intervention.
Solution Approach 2:
The system performs preliminary analysis of customer reconfiguration patterns before service issues arise. By ranking users based on reconfiguration frequency and pre-determining optimal configurations through linear regression modeling, the system proactively configures services to prevent future reconfigurations and customer dissatisfaction.
2Reliability
If personalized service configurations are implemented for each customer, then customer satisfaction improves, but system complexity increases due to individualized service planning
Solution Approach 1:
The system enables self-service through automated configuration determination. Rather than requiring manual personalized planning for each customer, the system autonomously analyzes reconfiguration patterns, applies linear regression models, and determines optimal service configurations automatically, reducing the need for complex human-driven customization processes.
Solution Approach 2:
The system manages complexity by changing parameters through systematic analysis rather than arbitrary customization. By using linear regression models to determine service parameters based on reconfiguration frequency rankings, the system transforms complex individualized planning into a structured parameter-adjustment process that scales efficiently.
3Adaptability or versatility
If frequent reconfigurations are allowed to meet customer needs, then service adaptability improves, but network stability deteriorates due to continuous service changes
Solution Approach 1:
The system performs preliminary determination of optimal service configurations before reconfigurations occur. By analyzing historical reconfiguration patterns and using linear regression models to predict stable configurations, the system proactively sets service parameters that adapt to customer needs while preventing the need for frequent subsequent reconfigurations, thereby maintaining network stability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring reconfiguration frequency and using this information to refine service configurations. The linear regression model incorporates reconfiguration patterns as feedback to determine optimal configurations, creating a closed-loop system that adapts to customer needs while reducing unnecessary network changes through data-driven decision making.
Data Source
AI summary
A method for determining user configurations of services in a radio communication network is performed in a support system node, and includes ranking users of a service based on frequency of occurrences of user reconfiguration in the radio communication network, creating a linear regression model of user configuration for a plurality of users of the service in the radio communication network, identifying a user configuration per user of the service for a selected number of ranked users by fitting the selected number of ranked users in the linear regression model, in order to reduce user reconfiguration of the service, and providing the identified user configuration per user to the selected number of ranked users. A support system node, a computer program and a computer program for determining user configurations of services in a radio communication network are also presented.