ML-Based Retail Location Selection for Wireless Networks
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Solution Overview
Problem
Current cellular networks face challenges in efficiently managing wireless networks and customer relationships, particularly in predicting user behavior, such as payment patterns, churn rates, and data usage, which affects profitability and customer retention.
Innovation Solution
The implementation of machine learning-based techniques to collect and process data on user behavior, allowing for the estimation of customer lifetime value (CLV) and the prediction of future payments, costs, and churn rates. This data is used to inform decisions on resource allocation, data quota management, and location selection for network infrastructure and retail locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If bandwidth throttling is applied to users who exceed data quotas, then network costs are reduced in the short-term, but user experience deteriorates and churn rate increases
Solution Approach 1:
The system dynamically changes the data quota parameter for individual users based on their predicted customer lifetime value. High-value customers receive increased data quotas while low-value customers maintain standard quotas, allowing the network to manage costs differently across user segments and prevent churn among valuable customers.
2Reliability
If additional data usage quotas are allocated to high-value customers, then churn rate is reduced and user experience is maintained, but network costs increase
Solution Approach 1:
The system applies different data quota policies to different user segments based on their individual characteristics and predicted value. Instead of a uniform approach, each customer receives a customized quota allocation that reflects their specific contribution to network profitability, optimizing the balance between retention and cost.
3Ease of manufacture
If traditional methods are used to select retail locations and infrastructure sites, then deployment is straightforward, but profitability and customer acquisition efficiency are suboptimal
Solution Approach 1:
The system performs preliminary analysis of customer lifetime value and geographic distribution patterns before selecting retail locations and infrastructure sites. By pre-identifying high-value customer concentrations and predicting future profitability metrics, the system guides deployment decisions to maximize customer acquisition efficiency from the outset.
Data Source
AI summary
A method includes estimating using one or more machine learning models, for each wireless subscriber of a plurality of wireless subscribers, a score that is indicative of an expected profitability associated with the corresponding wireless subscriber. The method also includes identifying a geolocation, wherein within a pre-defined radius from the geo-location, there is at least a threshold data usage level by a subset of the plurality of wireless subscribers. The method also includes determining an aggregate profitability metric associated with the geolocation based upon the estimated scores corresponding to wireless subscribers included in the subset of the plurality of wireless subscribers; The method also includes determining that the aggregate profitability metric associated with the geolocation satisfies a threshold condition, and responsive to determining that the aggregate profitability metric associated with the geolocation satisfies the threshold condition, selecting the identified geolocation as a candidate location for a retail location.