Membership Platform Pricing Recommendation System
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Solution Overview
Problem
Content creators on membership platforms face challenges in determining optimal pricing for their benefit items to maximize subscriber acceptance and revenue, as existing methods lack effective data-driven approaches to set prices that drive growth and engagement.
Innovation Solution
A system utilizing machine learning, benefit information, and consumption data to train models that recommend optimal pricing for benefit items, enhancing acceptance and revenue through personalized pricing strategies for content creators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content creators manually determine pricing for benefit items, then they have control over their pricing strategy, but they lack effective data-driven approaches to optimize pricing for maximum subscriber acceptance and revenue
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between content creators and pricing optimization. The model processes consumption data and benefit information to generate recommended pricing, serving as a mediator that provides data-driven pricing suggestions without requiring creators to manually analyze complex pricing data. This resolves the contradiction by providing automated pricing optimization while maintaining creator control through recommendation-based rather than direct pricing control.
Solution Approach 2:
The patent replaces manual pricing determination with an automated machine learning system. Instead of creators manually analyzing pricing effectiveness, the system uses AI algorithms to process consumption patterns and benefit characteristics, substituting mechanical manual analysis with automated intelligent processing. This enables effective data-driven pricing optimization while eliminating the information loss that would occur with manual analysis approaches.
2Quantity of substance
If content creators offer more benefit items with higher pricing, then revenue potential increases, but subscriber acceptance decreases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting pricing parameters based on benefit item characteristics and consumption patterns. The machine learning model analyzes which benefit items have higher acceptance rates and adjusts pricing parameters accordingly, allowing content creators to optimize the quantity and pricing of benefit items to maximize both revenue and subscriber acceptance rather than simply increasing quantity regardless of acceptance impact.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring consumption data and using it to refine pricing recommendations. The machine learning model processes feedback from subscriber behavior patterns to adjust future pricing suggestions, creating a closed-loop system that balances benefit item quantity with subscriber acceptance to optimize revenue while maintaining high acceptance rates.
3Productivity
If content creators use personalized pricing strategies, then revenue optimization improves, but system complexity increases
Solution Approach 1:
The patent implements self-service by enabling the machine learning system to automatically generate and update pricing recommendations without requiring manual intervention from content creators. The system self-adjusts based on consumption data and benefit information, providing automated personalized pricing strategies that optimize revenue while keeping the operational complexity manageable through automation rather than manual configuration.
Solution Approach 2:
The machine learning model serves multiple functions within a single system: it processes consumption data, analyzes benefit characteristics, generates pricing recommendations, and provides personalized strategies for different content creators. This multi-functionality consolidates what would otherwise require multiple separate systems into one unified platform, improving revenue optimization while managing overall system complexity through consolidation.
Data Source
AI summary
Systems and methods are provided for recommending price of benefit items offered through a membership platform. Exemplary implementations may: obtain benefit information for content creators of a membership platform; obtain consumption information, the consumption information describing acceptance of offers for the benefit items at the requested amounts by the subscribers of the content creators; train a machine learning model on input/output pairs to generate a trained machine learning model, the individual input/output pairs including training input information and training output information; store the trained machine learning model; determine, using the trained machine learning model, recommended amounts of consideration for the benefit items that correspond to greater acceptance; generate recommendations for individual content creators conveying the recommended amounts for the benefit items offered by the individual content creators; and/or perform other operations.