Cloud Subscription Platform Automating Customer Grouping
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Solution Overview
Problem
Consumers face challenges in joining group-based subscriptions due to lack of connections and fear of liability for unpaid debts, as existing systems lack efficient methods for intelligent customer grouping and automated payment processes.
Innovation Solution
A cloud-based subscription management platform that uses customer preferences and account attributes to intelligently match and group customers for enrollment in group-based subscriptions, automating the enrollment and payment processes by determining lead and non-lead accounts for proportional billing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual customer grouping and subscription enrollment is used, then human judgment and flexibility are applied, but time consumption and labor costs increase
Solution Approach 1:
The system automatically performs customer grouping and subscription enrollment without manual intervention. The automated matching model evaluates customer preferences, account attributes, and subscription criteria to assign customers to groups and process enrollments independently, eliminating the need for manual processing while maintaining operational flexibility through programmable decision logic.
Solution Approach 2:
Manual mechanical processes of customer evaluation and grouping are replaced with an automated computing system that uses algorithms and data models. The system substitutes human judgment with computational algorithms that process customer information, match criteria, and execute enrollments automatically, significantly reducing processing time and labor requirements.
2Productivity
If automated subscription management is implemented, then processing speed and efficiency improve, but system complexity increases
Solution Approach 1:
The subscription management system is divided into distinct functional modules: customer data collection module, preferences analysis module, account attributes evaluation module, matching model module, grouping module, and enrollment processing module. Each module handles a specific aspect of the automation process, making the overall complex system manageable through modular architecture while maintaining high processing efficiency.
3Measurement precision
If customer matching models are used to predict successful matching, then grouping accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system applies the matching model selectively rather than universally. The model is used to evaluate and score customers based on their preferences and account attributes, but only for customers who meet certain preliminary criteria or when enrollment is actually needed. This partial application of the computationally intensive model maintains high matching accuracy for relevant cases while reducing overall computational resource consumption.
Data Source
AI summary
A computing resource of a cloud computing environment receives, from customers in a customer population, requests to enroll in group-based subscriptions provided by third-party providers. The requests include customer preferences associated with the customers. The computing resource determines customer account attributes for customer accounts, and determines, using a customer matching model, scores for the customers based on the customer preferences and the customer account attributes. The scores predict a successful matching among the customers in the customer population. The computing resource assigns a first subset of the customers in the customer population to a first group of customers based on the scores, enrolls the first group of customers in a first account for a first group-based subscription provided by a first third-party provider, and allocates a payment for the first group-based subscription among a first group of customer accounts associated with customers in the first group of customers.