Customer Lifetime Value Model for Wireless Subscriber Retention
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Solution Overview
Problem
Current cellular networks face challenges in accurately predicting user payments, costs, and churn rates, leading to inefficient resource allocation and customer management.
Innovation Solution
The implementation of machine learning models that estimate predicted payment amounts, costs, and churn rates for wireless subscribers based on historical data and demographic features, allowing for the calculation of customer lifetime value (CLV) and informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If bandwidth throttling is implemented to limit data usage costs, then network provider costs are reduced, but user experience deteriorates and churn rate increases
Solution Approach 1:
The system dynamically changes the data quota parameter for specific users based on their predicted future data needs and CLV. Instead of applying uniform bandwidth throttling to all users who exceed a threshold, the model identifies high-CLV users and allocates additional data quotas to them, thereby maintaining their user experience while controlling overall network costs through targeted rather than universal restrictions.
2Reliability
If additional data usage quotas are allocated to high-value customers, then churn rate is reduced, but network provider costs increase
Solution Approach 1:
The system applies different data quota policies to different user segments based on their individual characteristics. High-CLV users receive additional data quotas while other users maintain standard quotas. This localized differentiation ensures that retention efforts are concentrated on the most valuable customers, optimizing the balance between retention costs and expected lifetime value.
Solution Approach 2:
The system allocates additional data quotas selectively to only those users who are predicted to have high future data needs and high CLV, rather than providing universal quota increases. This partial action approach ensures that cost increases are limited to the specific subset of users where the retention benefit justifies the additional network cost.
3Productivity
If machine learning models are implemented to predict user behavior, then resource allocation is optimized, but system complexity increases
Solution Approach 1:
The machine learning system performs multiple functions using a unified framework: it predicts future data usage, calculates CLV, identifies churn risk, and determines optimal data quota allocations. This multi-functional approach consolidates what could be separate complex systems into a single integrated model, reducing overall system complexity while maintaining high resource allocation efficiency.
Data Source
AI summary
A method includes identifying one or more features corresponding to a wireless subscriber associated with a wireless service provider. The method also includes predicting, based on the one or more features corresponding to the wireless subscriber and using one or more machine learning models, one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider. The method also includes determining a score that is indicative of an expected profitability associated with the wireless subscriber based on the one or more parameters associated with future engagement of the wireless subscriber with the wireless service provider. The method also includes determining that the score satisfies a threshold condition, and responsive to determining that the score satisfies the threshold condition, performing one or more actions to decrease a probability that the wireless subscriber churns.