Dynamic Crowd Pricing System with Tier-Based Ratings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital content pricing systems, such as the Apple App Store, face challenges in generating revenue for developers due to price competition driving prices towards $0, and existing rating systems are subjective and ineffective in determining the value of digital content.
Innovation Solution
A computer-based crowd pricing system with tier-based ratings that calculates download popularity and adjusts pricing tiers based on user ratings, using control factors to regulate movement between tiers and prevent 'churn', while providing cumulative user ratings to indicate the value of downloadable media items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prices are reduced to attract customers, then sales volume increases, but developer revenue decreases
Solution Approach 1:
The patent segments the pricing system into multiple pricing tiers (free, low-cost, premium) based on download popularity and user ratings. Instead of uniform pricing, content is divided into different price categories, allowing high-popularity content to command higher prices while new content remains accessible at lower prices, thus increasing overall revenue without sacrificing volume.
Solution Approach 2:
The patent implements dynamic pricing that automatically adjusts based on real-time download popularity metrics and user ratings. Prices are not static but evolve over time as content gains or loses popularity, allowing the system to capture maximum revenue at each stage of the content lifecycle while maintaining accessibility.
2Ease of operation
If subjective rating systems are used, then user feedback is collected, but the system fails to accurately reflect content value
Solution Approach 1:
The patent implements a feedback loop where user ratings and download behavior continuously inform pricing adjustments. The system collects user feedback through ratings and uses this feedback to automatically adjust prices and tier placements, creating a self-correcting mechanism that progressively improves accuracy in reflecting content value.
Solution Approach 2:
The patent transforms subjective rating data into objective pricing parameters by combining user ratings with download popularity metrics. This conversion changes the nature of the data from purely subjective to a composite parameter that more accurately reflects market value and content quality.
3Loss of energy
If prices are dynamically adjusted based on popularity, then revenue optimization is achieved, but excessive price fluctuations occur
Solution Approach 1:
The patent incorporates buffer mechanisms that prevent extreme price adjustments by limiting the magnitude of price changes between tiers and implementing evaluation periods. These cushions absorb market volatility and prevent runaway price fluctuations while still allowing meaningful revenue optimization through tier-based pricing.
4Loss of energy
If tier-based pricing is implemented, then revenue generation is improved, but system complexity increases
Solution Approach 1:
The patent implements automated tier assignment and price adjustment mechanisms that operate without manual intervention. The system self-adjusts content placement across pricing tiers based on algorithmic evaluation of download popularity and user ratings, reducing operational complexity while maintaining sophisticated pricing strategies.
Data Source
AI summary
A crowd pricing system and method having tier-based ratings includes a pricing table hosted on a memory. The pricing table has a plurality of pricing tiers, wherein the pricing table is correlated to at least one downloadable media item. A download popularity value is calculated based on a quantity of downloads of the at least one downloadable media item within an evaluation period, wherein a position of the at least one downloadable media item is movable between the plurality of pricing tiers based on the download popularity value, wherein position movement of the at least one downloadable media item is regulated by at least one control factor. A ratings designation is paired with each of the plurality of pricing tiers, the ratings designation having a cumulative user ratings score calculated by the processor, wherein the cumulative user ratings score provides an indication of cumulative user rating of the at least one downloadable media item.