Targeted Ad Selection via Bid Grouping and Segmentation
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Solution Overview
Problem
Conventional approaches to selecting advertisements for display in electronic environments fail to effectively maximize revenue by not targeting the most interested audience, as they do not efficiently manage bids and group competing products or services into targeted audiences.
Innovation Solution
The system allows advertisers to competitively bid for displaying advertisements to highly targeted audiences by grouping competing products or services into 'baskets' associated with specific customer demographics, using a bid processing module to select the winning bidder based on various criteria, including multiple basket bids over single basket bids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional advertisement selection approaches are used, then advertisements can be displayed to users, but revenue maximization is not achieved due to ineffective audience targeting
Solution Approach 1:
The patent segments the audience into distinct interest groups based on their browsing and search behavior. Users are categorized into different segments (e.g., tech-savvy, bargain hunters, brand loyalists) allowing advertisements to be precisely targeted to specific segments rather than displayed universally, thereby reducing revenue loss from ineffective targeting.
Solution Approach 2:
The system dynamically changes advertisement parameters (which ads to display) based on user segment parameters (browsing history, search queries, time of day). This parameter-based adaptation allows the system to optimize revenue by serving different advertisements to different user segments based on their demonstrated interests and behaviors.
2Quantity of substance
If multiple advertisers bid for advertisement space, then advertisement revenue can increase, but managing bids and selecting winning advertisers becomes complex
Solution Approach 1:
The system performs preliminary actions by pre-segmenting users into interest groups and pre-evaluating bid criteria before advertisement requests are processed. This advance preparation simplifies the real-time bid management process, allowing the system to efficiently select winning advertisers without becoming overwhelmed by complexity as the number of advertisers increases.
Solution Approach 2:
The system implements feedback mechanisms where bid outcomes are continuously evaluated and used to refine the bid processing algorithm. By analyzing which bids win and which user segments convert to sales, the system learns and adjusts its bid selection criteria, managing the complexity of multiple advertiser bids through data-driven optimization rather than rigid complex rules.
3Productivity
If advertisements are targeted to specific user groups, then revenue from interested users increases, but the system complexity for managing user groups and bids increases
Solution Approach 1:
The patent creates a universal user segmentation framework that serves multiple functions simultaneously: it identifies user interests for targeted advertising, groups users for bid evaluation, and enables revenue optimization across different product categories. This multi-functional segmentation system reduces overall complexity by using a single unified approach rather than separate systems for each function.
Solution Approach 2:
The system introduces an intermediary layer (the user segmentation and bid processing module) that mediates between raw user data and advertisement delivery. This intermediary translates complex user behavior data into simplified user segments, and translates multiple advertiser bids into selected winning advertisements, thereby managing system complexity through abstraction and intermediate processing layers.
Data Source
AI summary
Systems and methods are provided for determining an electronic advertisement to be displayed to a highly targeted set of customers. Items are grouped into item groups of related and competing products and advertisers competitively bid against other advertisers having products in the same item group to determine whose advertisement will potentially be shown to customer associated with the item group.