Transaction Optimization System for Media Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Media networks face challenges in determining whether to sell their audience as a whole or in segments, as current technologies lack the necessary information to optimize revenue strategies, leading to potential underutilization of inventory and reduced revenue.
Innovation Solution
A system and method that utilize a processor and computer-readable storage medium to receive transaction information, generate models for selling media content inventory, simulate sales of audience segments, and provide an optimized transaction strategy based on past and potential transaction data, allowing media networks to determine optimal pricing and revenue maximization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the media network sells the entire audience to a content provider, then the transaction is simple and quick, but the revenue may be lower compared to selling segmented audience at premium prices
Solution Approach 1:
The system performs preliminary simulation of different selling strategies (whole audience vs. segmented audience) before making the actual transaction decision. By using historical transaction information and simulated selling scenarios, the system pre-evaluates potential revenue outcomes, allowing the media network to choose the optimal strategy in advance rather than making decisions based on incomplete information.
2Quantity of substance
If the media network sells segmented audience at premium prices, then the revenue may be maximized, but the risk increases if remaining segments do not sell
Solution Approach 1:
The system uses feedback from historical transaction information and simulated selling results to continuously improve transaction strategy recommendations. By analyzing past transaction outcomes and incorporating them into the simulation model, the system learns from previous successes and failures, providing more reliable predictions about which segmented selling strategies are likely to succeed and which carry excessive risk.
3Device complexity
If the media network lacks transaction optimization information, then the decision-making process is simple, but the revenue optimization capability is reduced
Solution Approach 1:
The system introduces an intermediary optimization module that processes historical transaction information and generates simulated selling scenarios. This intermediary layer analyzes complex patterns in the data and translates them into actionable transaction strategy recommendations, bridging the gap between raw data and decision-making without requiring the media network operators to directly manage the complexity of the analysis themselves.
Data Source
AI summary
Systems, methods and computer-readable storage media for optimizing transactions in a household addressable media network are described. Transaction information associated with past transactions involving the sale of inventory for the media network, such as the sale of an audience and/or segments of the audience. Potential transaction information may be configured to indicate projected conditions for a future sale media network inventory for certain media content of a potential purchaser, such as a media content provider. An example media content provider is an advertiser seeking to purchase an audience or segments thereof for broadcast of an advertisement. A model for selling the inventory may be configured based on the past information. A simulated selling of the inventory and/or segments thereof may be performed to generate potential sales information that may be used to optimize future inventory sales. An illustrative simulation model may include an agent-based computational economics (ACE) model.