Option-Priced Risk Pools for Predictive Cashback Advertising
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Solution Overview
Problem
Traditional communication systems fail to accurately predict user behavior and initiate targeted advertisements without explicit user input, leading to inefficient marketing efforts and high processing requirements.
Innovation Solution
A connection and communication platform that uses algorithms and data analytics to predict user behavior by posing prompts to consumers about planned purchases, correlating item types with advertisements, and providing monetary rewards for future purchases, thereby reducing the need for extensive data processing and improving transaction conversion ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional communication systems are used to transmit advertisements, then electronic communications are sent to users, but the systems fail to accurately predict user behavior and require high processing requirements
Solution Approach 1:
The system performs preliminary actions by collecting user interaction data and generating risk pools before advertisement transmission. Option pricing models are pre-calculated based on user behavior patterns, allowing the system to make accurate predictions without complex real-time processing during advertisement delivery.
Solution Approach 2:
The patent introduces an intermediary layer consisting of risk pools and option pricing models that mediate between user behavior data and advertisement delivery. This intermediary structure simplifies the decision-making process by using pre-established pricing frameworks rather than requiring complex real-time analysis.
2Loss of information
If traditional advertising systems transmit advertisements without generating input data, then marketing efforts are executed, but the systems lack data accuracy and require high processing resources
Solution Approach 1:
The system implements self-service by automatically collecting user interaction data, generating risk pools, and calculating option pricing without requiring extensive external processing resources. The platform serves itself by creating the necessary data infrastructure and analytical models internally, reducing the need for additional processing power.
Solution Approach 2:
Input data and risk pools are generated in advance through preliminary data collection and analysis. Option pricing models are established before advertisement campaigns begin, allowing the system to operate with high data accuracy while minimizing real-time processing requirements during actual marketing execution.
3Productivity
If the platform provides monetary rewards for future purchases, then transactional conversion ratios are enhanced, but the platform must manage risk pools and option pricing
Solution Approach 1:
The system uses option pricing models that dynamically adjust parameters such as strike prices, expiration dates, and reward amounts based on user behavior and risk assessments. By changing these financial parameters systematically, the platform enhances conversion rates while managing risk through established options trading frameworks rather than ad-hoc risk management.
Solution Approach 2:
Risk pools serve as an intermediary mechanism between the platform and users receiving monetary rewards. The option pricing models act as another layer of intermediary that systematically manages the distribution of rewards while controlling exposure. This structured approach simplifies risk management compared to direct, unmediated reward distribution.
Data Source
AI summary
Systems and methods of predicting future interactions with devices. For example, the system may receive search criteria and determine one or more values, including an interaction value of the search criteria and a cost per views value of the search criteria. When the cost per views value exceeds a threshold value, the system may increase a dynamic cashback amount associated with the search criteria and transmit an electronic communication that includes the dynamic cashback amount, a predetermined period of time to initiate a transaction for the product, and an identifier associated with the product. When the identifier associated with the product is received in association with the transaction of the product within the predetermined of time, the system may transfer the dynamic cashback amount from the aggregation revenue pool of funds to a digital wallet of the consumer user.