Location-Based Bid Modifiers for Advertising ROI
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Solution Overview
Problem
Current systems for selecting and displaying third-party content in conjunction with first-party content lack effective mechanisms to adjust bids based on the likelihood of a user completing a transaction at a physical establishment, leading to suboptimal advertising ROI.
Innovation Solution
A method and system that calculates a location-based auction bid modifier using data on the likelihood of a user completing a transaction, average transaction amount, and expected ROI, adjusting the base bid amount to optimize content placement costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If base bid amount is used for content auctions without location-based adjustments, then bidding process is simple, but return on investment is suboptimal
Solution Approach 1:
The patent applies local quality by adjusting bid amounts based on the user's proximity to physical establishments. Different bid modifiers are applied to different geographic locations, making the bidding strategy location-specific rather than uniform. This resolves the contradiction by optimizing ROI through localized bid adjustments while maintaining a relatively simple overall system structure.
Solution Approach 2:
The patent changes the bid amount parameter dynamically based on location data and transaction likelihood. By modifying the bid parameter according to user proximity to establishments and predicted transaction probability, the system optimizes ROI without requiring complete system redesign, thus balancing improvement with acceptable complexity.
2Reliability
If uniform bid amounts are used for all content placements, then bidding management is easy, but advertising effectiveness varies by location
Solution Approach 1:
The patent introduces dynamics by making bid amounts adaptive rather than static. Bid modifiers are automatically adjusted based on real-time location data and transaction likelihood predictions, allowing the system to respond to changing conditions while automating management tasks to maintain ease of operation.
Solution Approach 2:
The system uses feedback from location data and transaction outcomes to continuously optimize bid amounts. By incorporating transaction likelihood predictions and actual performance data, the system automatically adjusts bid modifiers to improve advertising effectiveness while reducing manual management burden through automated feedback loops.
3Loss of energy
If location-based bid modifiers are calculated using multiple factors, then ROI is optimized, but calculation complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating bid modifiers based on historical data and established models. Transaction likelihood predictions and bid modifier calculations are performed in advance or in real-time using pre-defined algorithms, reducing the computational burden during actual bidding while still achieving optimized ROI through multi-factor consideration.
Data Source
AI summary
Systems and methods for determining location-based bid modifier suggestions include determining a content placement cost based in part on a likelihood of a user that has entered a physical establishment completing a transaction, an average transaction amount for the establishment, and an expected return on investment (ROI). A location-based bid modifier may be determined using the computed cost and a base bid amount. In some implementations, the location-based bid modifier may also be based on a probability model that models the probability of the user visiting the establishment.