Self-Expanding Online Ad Campaign Optimization
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Solution Overview
Problem
Current online advertisement campaign optimization methods are inefficient, requiring long convergence times and high costs due to a trial-and-error approach, often focusing on static best-performing spaces while neglecting potentially better-performing spaces and lacking dynamic exploration of new opportunities.
Innovation Solution
A self-expanding online advertisement campaign system that uses machine learning and intelligent bidding to identify high-performing candidate spaces through small-scale testing, dynamically adjusting bid prices and creative elements, and continuously exploring new spaces to optimize budget allocation and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial-and-error approach is used to optimize online advertisement campaign, then comprehensive testing of advertisement spaces is achieved, but convergence time becomes excessively long and costs increase
Solution Approach 1:
The system performs preliminary small-scale testing of candidate advertisement spaces before full-scale deployment. By conducting initial experiments with limited budgets and analyzing performance metrics early, the system identifies high-potential spaces without committing full resources, thereby reducing overall convergence time while maintaining optimization reliability
Solution Approach 2:
The system dynamically adjusts the testing strategy by continuously monitoring performance metrics and reallocating budget in real-time. High-performing spaces receive increased budget allocation while underperforming spaces are被淘汰, creating a dynamic optimization process that converges faster than static trial-and-error methods
2Reliability
If traditional trial-and-error approach is used to optimize online advertisement campaign, then comprehensive testing of advertisement spaces is achieved, but costs increase significantly
Solution Approach 1:
The system performs preliminary small-scale testing of candidate advertisement spaces before full-scale deployment. By conducting initial experiments with limited budgets and analyzing performance metrics early, the system identifies high-potential spaces without committing full resources, thereby reducing overall convergence time while maintaining optimization reliability
Solution Approach 2:
The system dynamically adjusts the testing strategy by continuously monitoring performance metrics and reallocating budget in real-time. High-performing spaces receive increased budget allocation while underperforming spaces are被淘汰, creating a dynamic optimization process that converges faster than static trial-and-error methods
3Measurement precision
If static selection of best-performing advertisement spaces is used, then current performance optimization is achieved, but potential better-performing spaces are neglected
Solution Approach 1:
The system continuously explores new candidate advertisement spaces while dynamically adjusting budget allocation based on real-time performance feedback. This dynamic approach allows the system to adapt to changing performance patterns and discover potentially better-performing spaces that static methods would miss
Solution Approach 2:
The system implements continuous feedback loops where performance metrics from tested spaces inform the selection of new candidate spaces. By analyzing performance data and using it to guide future exploration, the system maintains both measurement precision and adaptability, constantly improving its understanding of which spaces perform best
4Measurement precision
If small-scale testing of candidate spaces is conducted, then performance evaluation accuracy is improved, but the number of spaces that can be tested decreases
Solution Approach 1:
The system performs preliminary small-scale testing of candidate advertisement spaces before full-scale deployment. By conducting initial experiments with limited budgets and analyzing performance metrics early, the system identifies high-potential spaces without committing full resources, thereby reducing overall convergence time while maintaining optimization reliability
Solution Approach 2:
The system dynamically adjusts the testing strategy by continuously monitoring performance metrics and reallocating budget in real-time. High-performing spaces receive increased budget allocation while underperforming spaces are被淘汰, creating a dynamic optimization process that converges faster than static trial-and-error methods
Data Source
AI summary
A computerized method of dynamically expanding an online advertisement campaign, comprising receiving from an advertiser an advertisement policy for an online advertisement campaign for offered item(s), the advertisement policy includes policy setting(s) and expanding the online advertisement campaign through multiple iterations. Each iteration comprises bidding to purchase candidate online advertisement space(s) of a plurality of available online advertisement spaces for small-scale use where the candidate online advertisement space(s) are selected by analyzing a performance of each of the available online advertisement spaces with respect to the online advertisement campaign according to the policy setting(s), placing an advertisement at the purchased online advertisement space(s) and determining a performance of the purchased online advertisement space(s) by calculating a performance score, adding the purchased online advertisement space(s) to a group of large-scale online advertisement spaces in case the performance score satisfies predefined criterion(s) and bidding for purchase of the group for large-scale use.