Recovery-Aware Bid Optimization for Search Ads
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Solution Overview
Problem
Advertisers face challenges in determining optimal bid values for keywords due to lack of visibility into organic search results, leading to inefficient advertising strategies and potential loss of revenue.
Innovation Solution
A system that utilizes a neural network-based approach to analyze both paid and organic search data, providing recovery-aware bid optimization by predicting organic search rankings and adjusting bids to maximize net return and optimize ad placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advertisers place bids on keywords without visibility into organic search results, then paid search advertising can be implemented, but bidding accuracy deteriorates and revenue is lost
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between the advertiser and the search engine's organic search results. This system crawls and collects organic search result data, then processes and delivers it to advertisers as bid optimization recommendations, enabling informed bidding decisions without direct access to the search engine's internal data
Solution Approach 2:
The system implements a feedback loop where organic search result data is continuously collected, analyzed, and used to generate bid optimization recommendations that are fed back to advertisers. This feedback mechanism enables advertisers to adjust their bids based on actual organic search performance, improving bidding accuracy over time
2Productivity
If manual analysis of organic search data is performed, then some bidding optimization can be achieved, but time consumption increases significantly
Solution Approach 1:
The patent replaces the manual mechanical process of analyzing organic search data with an automated computer-based system. The system uses web crawlers to collect data, processing algorithms to analyze it, and automated tools to generate bid recommendations, eliminating the need for manual analysis while significantly improving productivity
Solution Approach 2:
The system enables self-service bid optimization where the automated platform continuously monitors organic search results, analyzes the data, and generates optimization recommendations without requiring manual intervention. Advertisers can implement these recommendations directly, freeing up time while maintaining high productivity
Data Source
AI summary
Bid values submitted for various keywords can take into account the recovery propensity between paid search and organic search. When submitting a bid to a search engine provider for a keyword, an entity may get a certain level of performance in return. If not submitting a bid, however, the entity will likely still get some level of performance, although likely less than for paid search. In order to optimize for a parameter such as impressions, purchases, or profit, the recovery propensity can be taken into account in order to adjust the bid price, taking into account the relative performance of paid and organic search and then optimizing for the determined goal. Organic search data in some embodiments can be obtained through testing or modeling, or a combination thereof.