Visual Ad Zone Designation for Budget Allocation
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Solution Overview
Problem
In online advertising, a significant portion of the advertising budget is wasted due to 'advertisement blindness' and inefficiencies in ad placement, making it difficult to target and engage users effectively.
Innovation Solution
A method for visually designating advertising space on web pages using a client-side script that retrieves the Document Object Model (DOM), detects user selections, and updates the DOM to display advertisement placeholders, allowing for dynamic ad placement and budget allocation across multiple entities based on optimal target frontier functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional advertising methods are used in online media, then advertising reach is extended, but advertisement blindness causes significant budget waste
Solution Approach 1:
The system performs preliminary actions by analyzing user behavior patterns, page content, and ad performance metrics before ad placement to predict which users are most likely to engage with advertisements. This pre-analysis enables targeted ad delivery that avoids showing ads to users who would ignore them, thereby reducing budget waste while maintaining ease of operation.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor user interactions with advertisements and use this information to adjust future ad placements. By analyzing engagement metrics in real-time and feeding this data back into the placement algorithm, the system learns which ad placements succeed and which result in advertisement blindness, progressively optimizing budget efficiency without complicating user experience.
2Manufacturing precision
If manual code implementation is used for ad placement, then precise control over ad positions is achieved, but complexity and time consumption increase
Solution Approach 1:
The system enables self-service ad placement by providing web page administrators with automated tools that analyze page structure and content automatically. The system identifies suitable ad placements based on page semantics and user behavior patterns without requiring manual code implementation. This maintains precise ad placement control while eliminating the time-consuming manual coding process.
Solution Approach 2:
The system introduces an intermediary automated placement engine that acts as a mediator between advertisers and web page content. This intermediary analyzes page structure, determines optimal ad positions, and generates placement code automatically, thereby achieving precise ad placement control without requiring administrators to manually implement complex code.
3Adaptability or versatility
If dynamic ad placement is implemented, then ad relevance to user context is improved, but computing resources and network bandwidth are consumed
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns, page content, and ad performance metrics before ad placement to predict which placements will be most effective. By pre-computing these patterns and storing them for reference, the system reduces the computing resources needed during actual ad placement while maintaining high adaptability to user context.
Solution Approach 2:
The system applies different levels of analysis and adaptability to different contexts. For high-value ad placements, it performs comprehensive real-time analysis, while for standard placements, it uses pre-computed patterns and simpler heuristics. This localized approach to quality ensures high adaptability where needed while conserving computing resources in routine scenarios.
4Productivity
If automated ad placement systems are used, then budget allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the ad placement problem into distinct modular components: user behavior analysis, page content analysis, ad performance prediction, and placement optimization. Each module handles a specific aspect of the problem independently, which improves overall budget allocation efficiency while managing system complexity through clear separation of concerns and independent module development.
Data Source
AI summary
A method of operating a computing system capable of allocating an advertisement budget of campaign between a plurality of advertisement entities, the method comprising: obtaining by the computer system, for each of the plurality of advertisement entities, a respective optimal target frontier function representing for each given advertising cost an optimal value of return and configured to follow the law of diminishing return; receiving by the computer system a budget constraint for the advertisement budget; generating, by the computer system, a global target frontier function by summing each of the received optimal target frontier functions; processing, by the computer system, the generated global target frontier function to determine for each of the plurality of advertisement entities an optimal, with respect of at least the received budget constrain, advertising cost value such that a sum of the optimal advertising cost values meets the budget constraint; and reporting the determined values.