Probability-Based Ad Exposure Optimization System
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Solution Overview
Problem
Advertisers face challenges in determining the optimal number of exposures for their advertisements within a given time frame and budget, as traditional methods like clicks are unreliable for measuring awareness and existing bidding models do not effectively adjust for achieving minimum exposure goals.
Innovation Solution
A computer-implemented method and system that receive selection criteria, including a minimum number of exposures and a maximum aggregate bid value, to determine the probability of reaching the exposure goal within the specified time, and adjust bids accordingly to ensure the advertisement is displayed to the correct device identifiers, updating exposure data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bidding models are used, then budget control is maintained, but minimum exposure goals cannot be reliably achieved
Solution Approach 1:
The system performs preliminary calculations of probability and expected exposures before the bidding process. It pre-determines the likelihood of achieving minimum exposure goals and uses this information to adjust bids in advance, rather than reacting after exposures occur. This allows the system to prepare bid strategies that are mathematically optimized for meeting exposure targets while controlling budget.
Solution Approach 2:
The system changes the bidding parameter from fixed bid amounts to probability-weighted bid adjustments. By calculating the probability of achieving minimum exposures and adjusting bids based on this probability, the system transforms the bidding process into a dynamic optimization problem that balances exposure goals with budget constraints.
2Reliability
If bids are increased to ensure minimum exposures, then exposure reliability improves, but budget consumption increases
Solution Approach 1:
The system changes the bidding approach by introducing probability as a weighting factor. Instead of uniformly increasing bids to guarantee exposures, it calculates the probability of achieving minimum exposures and adjusts bids proportionally. This ensures that budget is allocated efficiently based on the actual likelihood of exposure goals being met, rather than assuming worst-case scenarios that would waste budget.
Solution Approach 2:
The system uses feedback from probability calculations to adjust bid strategies. By continuously monitoring the probability of achieving minimum exposures and using this feedback to refine bid amounts, the system creates a closed-loop optimization that balances the risk of not meeting goals with the cost of over-bidding.
3Productivity
If probability-based bid adjustment is implemented, then exposure optimization improves, but computational complexity increases
Solution Approach 1:
The system performs probability calculations and bid optimizations in advance before the actual bidding process. By pre-calculating the probability of achieving minimum exposures and determining optimal bid strategies beforehand, the system reduces the computational burden during real-time bidding operations.
Solution Approach 2:
The system segments the complex optimization problem into separate calculable components: probability calculation, expected exposure computation, and bid adjustment determination. By breaking down the overall complexity into these discrete, manageable segments, the system can process each component independently and efficiently.
Data Source
AI summary
Methods and systems for providing an advertisement based on a minimum number of exposures may include receiving selection criteria to specify a device identifier that meets the selection criteria, receiving a minimum number of exposures to the advertisement and an interval of time for the minimum number of exposures to occur, and a maximum aggregate bid value for each device identifier that is exposed to the advertisement for the minimum number of exposures, determining a probability that the device identifier reaches the number of exposures within the interval of time and within the maximum aggregate bid value, selecting a bid for each exposure for the device identifier based on the determined probability and the maximum aggregate bid value; and based on the selection of the bid, providing display data indicative of the advertisement.