Ad Delivery Probability Calculation for Resource Optimization
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Solution Overview
Problem
Dynamic advertisement insertion systems incur costs for partial advertisement delivery even if viewers change channels quickly, leading to inefficient resource usage and potential losses for advertisers.
Innovation Solution
A computing system determines the probability of a threshold portion of an advertisement being presented and calculates the expected revenue based on this probability and delivery costs, communicating the advertisement only when the expected revenue exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If advertisements are delivered dynamically based on content and viewing habits, then advertisement targeting effectiveness is improved, but system resource consumption increases
Solution Approach 1:
The system pre-calculates and stores probability data regarding viewer behavior patterns and advertisement completion likelihood before actual advertisement delivery. This preliminary preparation allows the system to make quick delivery decisions without real-time complex calculations, reducing resource consumption during actual advertisement operations while maintaining targeting effectiveness.
2Speed
If advertisements are delivered without assessing completion probability, then delivery speed is improved, but cost efficiency deteriorates
Solution Approach 1:
The system performs a partial assessment by calculating only the essential probability metrics needed for delivery decisions rather than comprehensive analysis. It determines delivery based on key probability thresholds, performing just enough analysis to make informed decisions without excessive computation, thus balancing speed and cost efficiency.
3Loss of energy
If the system calculates expected revenue before advertisement delivery, then cost optimization is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary probability calculation layer that mediates between the complex factors of viewer behavior, advertisement performance, and cost considerations. This intermediary layer simplifies the decision-making process by converting multiple complex variables into a single expected revenue metric, making the system more manageable while achieving cost optimization.
4Quantity of substance
If the system delivers all selected advertisements, then advertisement coverage is improved, but resource waste increases due to channel switching
Solution Approach 1:
The system pre-calculates the probability of advertisement completion based on historical viewer behavior data before delivery. By assessing whether viewers are likely to complete watching the advertisement or switch channels, the system can selectively deliver only those advertisements with high completion probability, ensuring adequate coverage while avoiding resource waste from guaranteed incomplete deliveries.
Data Source
AI summary
A method includes determining, by a computing system, a probability of whether at least a threshold portion of a particular advertisement will be presented on a particular content presentation device, and a cost associated with presentation of the particular advertisement on the particular content presentation device. The computing system calculates an expected revenue associated with presentation of the particular advertisement based on the probability and the cost. When the expected revenue exceeds a threshold revenue, the computing system communicates the particular advertisement to the particular content presentation device.