Amazon Technologies, Inc.

Luke Aron Snyder

Eric Alfred Herrmann

Stephan G Betz

Lucas Paul Hansen

David Richard Brandt

Robert Lockhart Reid

David J Langrock

A tiered approach to selecting landing pages to be associated with specific keywords allows optimal landing pages to be determined dynamically and updated continually. Suggestions for landing pages can come from a number of sources based upon any of a number of factors. A scalable set of algorithms can be used to analyze these suggestions based upon factors including those used for determining the suggestions. Each algorithm can generate a decision as to an optimal landing page to be associated with each keyword. A subsequent algorithm can analyze each decision as these decisions relate to at least one goal, and can select an optimal landing page based thereon. A sponsored link or other element can be associated with the keyword such that a user submitting a request containing the keyword can be presented with the sponsored link to the optimal landing page.

Landing page selection for linked advertising

A new release advertisement system generates advertisement sets for new release items that have a release date. To generate the advertisement sets, the system identifies items with release dates within a 'new release advertising window.' The new release advertisement system then identifies item and keyword pairs from the text within the attributes of the items. After identifying item and keyword pairs for each of the new release items, the system removes pairs that do not satisfy a filtering criterion. The system then generates advertisements from the text of the attributes of the items remaining after the removing based on the filtering criterion. The system then creates advertisement sets for the remaining pairs that include the advertisement generated for the item of the pair and the keyword of the pair.

Release advertisement system

The accuracy of bid amounts for electronic advertising is improved by accounting for factors such as the cost-per-click (CPC) ratio for each ad. When a provider such as a search engine selects advertisements using automated auctions, it can be desirable for an advertiser to avoid underbidding for ads when the ads generate a significant amount of revenue or profit, or are otherwise performing well. Various algorithms can be used to generate a bid adjustment factor that allows bid values to be increased (or decreased) as appropriate, based on information such as CPC information. By calculating a separate adjustment factor, the amount of adjustment can be monitored and/or capped to avoid overspending. The algorithms also can utilize information at various levels of categorization, and at different time intervals, depending on the amount and type of information available, in order to provide an accurate and significant result.

Automatic bid adjustments for electronic advertising

Bid amounts for electronic advertising can be adjusted throughout the day, or for different days, according to a day parting schedule based on factors such as variations in efficiency. For any set of advertisements grouped based on factors such as keyword, landing page, geographic location, user demographic, etc., values can be forecast for each time period throughout the day. A variation threshold is used to group adjacent periods of time into partitions, and the threshold can be varied until the number of partitions meets the requirements of the provider with whom the advertisement is to be placed. An adjustment factor can be determined for each partition using the forecast information, and these factors can be uploaded to the provider to adjust the base bid price for any advertisement in that group at the appropriate times throughout the day.

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Luke Aron Snyder