Dynamic Bid Adjustment Using Predicted Performance Metrics
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Solution Overview
Problem
Advertisers face challenges in optimizing their bids for online advertising slots, as existing methods do not effectively account for the varying performance potential of advertisements across different placements, leading to inefficient resource allocation and potential overpayment.
Innovation Solution
The system predicts performance metrics for advertisements using historical data and adjusts bids based on comparisons between predicted and baseline performance, submitting the adjusted bids to auctions to optimize placement opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advertisers bid based on fixed target bid amounts, then the bidding process is simple, but the bid does not account for varying performance potential across different placements leading to inefficient resource allocation
Solution Approach 1:
The patent applies dynamics by transitioning from static fixed bids to dynamic bid adjustments. The system automatically adjusts bid amounts based on predicted performance metrics for different placements, making the bidding process adaptive to varying conditions while improving resource allocation efficiency without requiring manual intervention.
Solution Approach 2:
The patent implements feedback mechanisms by using predicted performance metrics from historical data to adjust future bids. The system continuously learns from actual performance and refines bid predictions, creating a closed-loop system that optimizes resource allocation based on real-world outcomes while automating the complexity management.
2Loss of energy
If advertisers bid based on impressions or clicks without performance prediction, then the bidding process is straightforward, but overpayment occurs when ads are served in placements with lower than expected performance
Solution Approach 1:
The patent applies preliminary action by predicting performance metrics before the auction occurs. The system uses historical data to pre-calculate expected performance for different placements and adjusts bids accordingly, allowing advertisers to avoid overpayment before it happens rather than reacting after performance is realized.
Solution Approach 2:
The patent implements parameter changes by adjusting bid amounts based on predicted performance metrics. The system transforms fixed bid parameters into variable bid parameters that reflect expected performance, enabling precise control over ad spend efficiency while managing the complexity of performance prediction through automated algorithms.
3Manufacturing precision
If the system adjusts bids based on predicted performance metrics, then bid accuracy and placement optimization improve, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies self-service by implementing automated bid adjustment systems that operate without manual intervention. The system automatically predicts performance metrics, calculates optimal bid adjustments, and submits revised bids, allowing the system to manage its own complexity through standardized algorithms and reducing the need for human involvement in complex calculations.
Solution Approach 2:
The patent uses an intermediary approach by introducing a performance prediction module as a mediator between historical data and bid adjustment decisions. This intermediate layer processes complex performance predictions and translates them into actionable bid modifications, simplifying the overall system architecture while maintaining high precision in bid adjustments.
Data Source
AI summary
A predicted performance metric of a candidate advertisement in an advertising slot can be received and compared to a baseline predicted performance metric for the candidate advertisement. A target bid associated with the candidate advertisement can be adjusted based upon the comparison, and the adjusted bid can be submitted to an auction for the advertising slot.