Virtual Item Valuation via Probability Bundles
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Solution Overview
Problem
Valuing new virtual content in online games is challenging due to unknown demand, leading to potential revenue loss if the sales price is set too low or non-sale if set too high.
Innovation Solution
A system and method that utilize probability item bundles, where new virtual items are sold alongside existing items at the same price, with distribution probabilities ensuring comparative sales analysis to determine the value of the new item without revealing its true sale price, facilitating market research and accurate pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the sales price of new virtual content is set high, then potential revenue is maximized, but the content may not sell due to unknown demand
Solution Approach 1:
The patent applies preliminary action by conducting market research through probability item bundles before officially launching the new virtual content. Sales data from these bundles are collected and analyzed in advance to determine optimal pricing strategies, ensuring that when the content is officially released, the price is already optimized based on preliminary market feedback, thus maximizing potential revenue while minimizing the risk of poor sales performance
Solution Approach 2:
The patent uses probability item bundles as an intermediary mechanism to indirectly gauge market demand for new virtual content. Instead of directly pricing the new content (which creates a binary sell/no-sell outcome), the system introduces bundled offers with probabilistic elements that allow users to express interest at different price points, providing nuanced market data that bridges the gap between high pricing and sales performance
2Reliability
If the sales price of new virtual content is set low, then sales volume increases, but potential revenue generation is lost
Solution Approach 1:
The patent applies dynamics by implementing flexible, data-driven pricing strategies based on observed user behavior from probability item bundle sales. Rather than setting a fixed low price, the system dynamically adjusts pricing recommendations based on actual demand signals, allowing the content to achieve high sales volume while optimizing revenue potential through evidence-based price point selection
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring and analyzing sales data from probability item bundles. This feedback loop provides real-time insights into user demand and price sensitivity, enabling the system to recommend optimal pricing that balances sales volume with revenue generation, ensuring that low pricing only occurs when actually supported by market data
3Loss of information
If the true sale price of new content is disclosed to users, then transparency is improved, but market research accuracy is compromised
Solution Approach 1:
The patent applies segmentation by separating the market research phase from the official sales phase. During the research phase, users interact with probability item bundles where the true price of new content is not disclosed, preserving data accuracy. In the official sales phase, full price transparency is provided. This segmentation allows both market research accuracy and user transparency to be optimized at their respective stages without conflict
Data Source
AI summary
An online gaming system for valuing new virtual items introduced into an online game. The system may comprise one or more processors configured to execute computer program modules. The system may include a virtual shop module configured to present offers to sell instances of probability item bundles including a first offer to sell instances of a first probability item bundle that includes a first primary item and a first set of secondary items for a price, and a second offer to sell instances of a second probability item bundle that includes a second primary item and a second set of secondary items for the same price. The system may include a price determination module configured to determine a value for the second primary virtual item based on comparative sales of the first probability item bundle and the second probability item bundle.