Proposed Bid Generation Using Historical Bidding Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing advertising campaign bidding processes lack efficiency in matching content distributors with publishers, often resulting in bids that are too low, leading to under-delivery of content, or too high, causing inefficient use of resources, due to the lack of accurate data-driven decision-making.
Innovation Solution
A computer-implemented method and graphical user interface that utilize historical data to generate a proposed bid for content distribution, projecting future bidding conditions and optimizing bid amounts to maximize campaign value within set goals, such as budget and impressions, thereby providing more accurate and efficient bidding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content providers submit bids without accurate data-driven decision-making, then the bidding process is simpler and faster, but the bid amounts are inaccurate leading to under-delivery or over-delivery of content
Solution Approach 1:
The system performs preliminary analysis of historical bidding data and market conditions before the actual bidding process to generate recommended bid amounts. This preliminary action provides accurate bid recommendations upfront, eliminating the need for complex real-time calculations during bidding while maintaining high accuracy.
Solution Approach 2:
The system introduces an intermediary component that analyzes historical data and market conditions to generate recommended bid amounts. This intermediary acts as a mediator between the content provider and the bidding process, providing data-driven recommendations without requiring the content provider to perform complex analysis themselves.
2Productivity
If content providers manually determine bid amounts, then the process requires less computational resources, but the efficiency and accuracy of matching content distributors with publishers decreases
Solution Approach 1:
The system performs computational analysis of historical data and market conditions in advance to generate recommended bid amounts. This preliminary computational action consolidates resource consumption before the bidding process, allowing faster and more efficient bidding decisions without requiring intensive real-time computational resources.
Solution Approach 2:
The system automatically analyzes historical bidding data and market conditions to generate recommended bid amounts without requiring manual intervention from content providers. This self-service approach improves bidding efficiency and accuracy while the computational resources are optimized through automated processes.
3Measurement precision
If the system uses historical data to generate recommended bid amounts, then bid accuracy improves, but the time required to process bidding information increases
Solution Approach 1:
The system performs preliminary analysis of historical bidding data and market conditions before the actual bidding process to generate recommended bid amounts. This preliminary action pre-processes the data, so when bidding occurs, the system can quickly retrieve and use pre-analyzed information, maintaining high accuracy while minimizing processing time during the bidding event.
Data Source
AI summary
A computer-implemented method for generating a proposed bid includes receiving an input generated by a first content provider as part of a bidding process where content providers bid for opportunities to provide content for publication. The input indicates (i) a resource for publishing first content from the first content provider and (ii) a campaign characteristic associated with the first content. The method includes determining a proposed bid for the first content provider based on the input and on historical data from the bidding process associated with the resource. The method includes presenting the proposed bid to the first content provider.