Offline Advertising Inventory Allocation System
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Solution Overview
Problem
Existing methods for allocating offline advertising inventory do not effectively account for campaign performance and offer price, leading to inefficient allocation and lack of transparency in advertising ROI for both advertisers and broadcasters.
Innovation Solution
A computer-implemented method for allocating offline advertising inventory by comparing campaign information, including offer price, target audience, and past response rates, to determine the optimal allocation based on predicted audience responses, ensuring favorable allocations that benefit both advertisers and broadcasters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional offline advertising inventory allocation methods are used, then allocation simplicity is maintained, but allocation efficiency and transparency of advertising ROI deteriorate
Solution Approach 1:
The system implements feedback loops by continuously monitoring campaign performance metrics (response rates, audience engagement) and using this data to dynamically adjust inventory allocation decisions. This creates a closed-loop system where allocation outcomes are measured and fed back into the allocation algorithm, improving efficiency through data-driven iterations while maintaining manageable complexity through automated feedback processing.
Solution Approach 2:
The patent introduces an intermediary allocation system that sits between advertisers and broadcasters, handling the complex comparison of campaign information, offer prices, and performance metrics. This intermediary layer absorbs the computational complexity of multi-criteria optimization, presenting simplified interfaces to users while executing sophisticated allocation algorithms that maximize ROI transparency and allocation efficiency.
2Loss of information
If campaign performance and offer price are not accounted for, then allocation process simplicity is maintained, but advertising ROI transparency deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-comparing campaign information, offer prices, and historical performance data before making allocation decisions. This advance analysis includes calculating predicted response rates and comparing multiple campaigns against established criteria, ensuring that ROI transparency is built into the allocation process from the outset rather than added as an afterthought, while managing complexity through structured pre-processing of campaign data.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting allocation decisions based on varying campaign parameters such as offer price, target audience characteristics, and historical response rates. The system modifies allocation outcomes in response to changes in these parameters, creating transparent ROI tracking that reflects actual campaign performance variations. This approach maintains manageable complexity by focusing on key performance parameters rather than analyzing all possible campaign attributes.
3Manufacturing precision
If comprehensive campaign comparison is performed, then allocation optimality is improved, but computational time increases
Solution Approach 1:
The system applies partial action by comparing campaign information against a selective set of relevant criteria rather than exhaustively analyzing all possible parameters. The allocation process focuses on key differentiating factors such as offer price, audience match quality, and historical performance metrics, achieving sufficient allocation precision without the computational overhead of comprehensive multi-dimensional analysis of every campaign attribute.
Solution Approach 2:
The patent segments the campaign comparison process into distinct evaluation stages, separating critical allocation factors (offer price, audience targeting, response rate) from secondary considerations. This segmentation allows the system to prioritize computational resources on the most impactful comparison dimensions, achieving high allocation precision for decision-critical parameters while minimizing computation time by deferring or simplifying less important comparisons.
Data Source
AI summary
A computer-implemented method of allocating offline advertising, the method including receiving an offer price for audience-member responses for an offline advertising campaign and campaign information describing the offline advertising campaign, comparing the received campaign information to one or more other advertising campaigns to determine a portion of offline advertising inventory to allocate to the offline advertising campaign, and allocating the determined portion of advertising inventory to the offline advertising campaign.