Dashboard Interface for Intelligent Subscription Product Selection
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Solution Overview
Problem
Current systems lack an efficient method for generating personalized subscription product recommendations that balance provider financial goals, employee perception, and market competitiveness, particularly in the context of health insurance plans, often resulting in suboptimal product offerings.
Innovation Solution
An automated system that processes client intake forms, consolidates member demographic information, and uses machine learning algorithms to calculate probabilities of product selection, presenting optimized recommendations in a dynamic GUI that adjusts based on user input and weighting adjustments, generating over a million possible scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used for product recommendation, then system complexity is low, but productivity and recommendation quality deteriorate
Solution Approach 1:
The system performs automated data processing, probability calculation, and recommendation generation without human intervention. The machine learning model autonomously analyzes demographic data, calculates selection probabilities, and generates optimized product recommendations, eliminating the need for manual analysis while maintaining high productivity.
Solution Approach 2:
The patent replaces manual mechanical processes with computational systems. Instead of human analysts manually evaluating product options and demographic data, the system uses automated algorithms, probability calculations, and machine learning models to generate recommendations, significantly improving productivity and consistency.
2Measurement precision
If comprehensive demographic analysis is performed for all members, then measurement precision improves, but loss of time and computational resources increases
Solution Approach 1:
The system segments the member population into census divisions based on demographic characteristics. By dividing the large population into smaller, homogeneous groups, the system can efficiently calculate selection probabilities for each segment rather than processing every individual member separately, reducing overall processing time while maintaining precision.
Solution Approach 2:
The system calculates selection probabilities for census divisions (groups) rather than for every individual member. This partial action approach provides sufficiently precise recommendations for product offering decisions without the excessive time cost of analyzing each individual's complete demographic profile.
3Reliability
If multiple product offering scenarios are generated and analyzed, then recommendation quality improves, but device complexity and processing requirements increase
Solution Approach 1:
The system dynamically generates and evaluates multiple product offering scenarios by varying product features, pricing, and target census divisions. The optimization score adjusts in real-time based on probability calculations and provider goals, allowing the system to explore multiple scenarios and select the most reliable recommendation without requiring permanently complex processing infrastructure.
Data Source
AI summary
In an illustrative embodiment, an automated system provides for generating product recommendations for clients. The system may include computing systems and devices for receiving a client intake information with product preferences, member demographic information, and current product information, and in response, generating product offerings including at least a portion representing variations of the current product. The member demographic information may be consolidated into census divisions that are each associated with a category of the member demographic information, and probabilities of selecting the product offerings may be calculated for each of the census divisions. The product offerings may be presented in a user interface with optimization scores that are a function of the probability of selecting the product offerings as well as provider financial goals, employee perception goals, and market competitiveness goals. Responsive to receiving filter adjustments, the optimization scores for the product offerings may be modified.