Tiered Ad Bidding Using User Quality Assessment
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Solution Overview
Problem
Existing online advertising models are inefficient due to pricing schemes that do not account for the quality of user interactions, leading to wastage of advertising budgets on low-quality traffic.
Innovation Solution
Implementing a tiered bidding system that assesses user quality based on multi-dimensional metrics, including user account information, context, and general context, to determine the cost of displaying advertisements, thereby optimizing ad placement and reducing wastage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional pricing scheme is used where advertisers specify payment per impression or click, then the advertising system is simple to operate, but advertising efficiency deteriorates due to wastage on low-quality traffic
Solution Approach 1:
The patent segments the advertising system into multiple components: user quality assessment module, tiered bidding module, and ad selection module. The user quality assessment module evaluates users based on multi-dimensional metrics (device information, browsing behavior, account attributes) to assign quality tiers. This segmentation allows the system to differentiate between high-quality and low-quality traffic, enabling efficient ad placement while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces new parameters for user quality assessment including device type, operating system, browsing history, click-through rates, and account attributes. These parameter changes enable the system to objectively measure user quality and implement tiered pricing structures. By changing from a single pricing parameter (cost per impression) to multiple quality indicators, the system achieves better advertising efficiency while the structured parameter framework keeps complexity controlled.
2Loss of energy
If advertising is displayed to all users regardless of quality, then the system is easy to implement, but advertising budget wastage increases on low-quality traffic
Solution Approach 1:
The patent implements preliminary user quality assessment before ad delivery. The system pre-evaluates users based on their device characteristics, historical browsing behavior, and account attributes, assigning quality tiers in advance. This preliminary action allows advertisers to target only high-quality users, reducing budget wastage. The assessment framework uses readily available data points (device type, OS, browsing history) to make quality determination before the advertising interaction occurs.
Solution Approach 2:
The patent replaces subjective human judgment of user quality with an automated computational assessment system. The system uses algorithms to objectively evaluate user profiles based on multiple data dimensions (device information, behavior patterns, account attributes) and automatically assigns quality tiers. This substitution eliminates the difficulty of manual quality detection while providing consistent, scalable assessment across all users, significantly reducing advertising budget wastage.
3Productivity
If a tiered bidding system is implemented to assess user quality, then advertising efficiency is improved by targeting high-quality users, but system complexity increases
Solution Approach 1:
The patent segments the user base into distinct quality tiers (e.g., high-quality, medium-quality, low-quality) based on multi-dimensional assessment criteria. Each tier receives differentiated treatment in the bidding and ad placement process. This segmentation enables the tiered bidding system to operate efficiently by matching ads to appropriate user tiers, improving advertising efficiency while managing complexity through clear categorical divisions rather than continuous variable management.
Solution Approach 2:
The patent creates a universal user quality assessment framework that can be applied across different advertising campaigns, industries, and platforms. The same core assessment metrics (device information, browsing behavior, account attributes) and tiering methodology serve multiple purposes: user quality evaluation, bid pricing determination, and ad placement optimization. This multi-functionality reduces overall system complexity by using a single unified framework rather than separate systems for each function.
Data Source
AI summary
Tiered advertisement bidding is disclosed. One or more quality metrics associated with a user profile are determined. An advertisement bid is selected from a plurality of tiered bids based at least in part on the determined quality metrics. Determining the quality metrics can include determining a conversion assessment. Determining the quality metric can also include determining whether a need associated with a user profile has been met for a category. In some cases, persona detection is performed with respect to the user profile.