Ad Placement Intermediary for Webpage Performance Prediction
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Solution Overview
Problem
Existing advertisement servers struggle to optimize advertisement placement on a retailer's own website, as they lack the ability to determine which webpages to display advertisements, leading to suboptimal performance in terms of click-through rates and impression goals.
Innovation Solution
A computer-implemented method and system that analyzes existing advertisement placements to predict the performance of new advertisements on various webpages, using context data and historical performance data to rank webpages for optimal advertisement placement, thereby recommending the most suitable webpage for displaying the advertisement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a third party advertising server serves advertisements on a retailer's website, then advertisement delivery is automated, but the advertiser cannot determine which webpages receive advertisements
Solution Approach 1:
The patent introduces an intermediary system that sits between the third-party advertising server and the retailer's webpages. This intermediary extracts context data from webpages, predicts advertisement performance, and provides recommendations to advertisers about which webpages will deliver expected performance. This resolves the contradiction by maintaining automation while restoring information transparency and advertiser control through the intermediary layer.
Solution Approach 2:
The system implements feedback loops where advertisement performance data is collected, analyzed, and used to improve future placement recommendations. The intermediary system continuously learns from actual performance outcomes and refines its predictions, providing advertisers with actionable feedback about which webpages are most likely to meet their performance goals while maintaining automated delivery.
2Adaptability or versatility
If advertisements are prioritized across multiple advertisers, then all advertiser goals are met, but individual advertiser control over placement is reduced
Solution Approach 1:
The patent applies local quality by providing customized placement recommendations for each advertiser based on their specific performance requirements and context data from individual webpages. Instead of a uniform prioritization system, each advertiser receives tailored recommendations about which webpages will meet their specific goals, allowing them to maintain control while the system handles multi-advertiser coordination.
Solution Approach 2:
The system performs preliminary analysis of webpage context data and advertisement characteristics before placement decisions are made. By pre-processing and evaluating compatibility between advertisements and webpages, the system can provide advertisers with informed recommendations about optimal placements that meet their goals, enabling them to make informed decisions before the actual prioritization process occurs.
3Speed
If advertisement placement decisions are made by an automated server, then placement speed is increased, but performance optimization is reduced
Solution Approach 1:
The system performs preliminary performance prediction and analysis before final placement decisions. By pre-evaluating which webpages are likely to deliver expected performance based on context data and historical patterns, the system narrows down options in advance, allowing fast automated selection from a pre-filtered set of high-probability placements rather than searching all possible webpages.
Solution Approach 2:
The intermediary system acts as a smart mediator between automated servers and performance optimization requirements. It provides guidance and recommendations to the automated placement system, enabling fast decision-making while maintaining high precision by steering the automated process toward pre-identified high-performance opportunities based on analyzed context data.
Data Source
AI summary
In some aspects, a system for enhancing performance of directed information delivery is provided. In one example, an advertising recommendation server receives an advertisement request including an advertisement for display on at least one of a plurality of webpages and a requested performance of the advertisement. The server extracts context data from the advertisement, and provides the extracted context data to a data model useable to generate predictions of performance of the advertisement when displayed on each of a plurality of webpages, trained based on performance data associated with previous displays of one or more previous advertisements on one or more of the plurality of webpages and context data from the one or more previous advertisements. A webpage is identified that meets expected performance, and a recommendation is provided to a decision platform.