Advertising Budget Allocation Optimization via Test Mixture Ratios
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Solution Overview
Problem
Advertisers face challenges in determining optimal advertising budget allocations across different channels, especially when historical data is lacking for new channels or products, leading to suboptimal return-on-investment.
Innovation Solution
The method involves defining an advertising mixture space, determining test advertising mixture ratios, and selecting a preferred ratio based on performance measures to optimize budget allocation across channels such as search, display, and content media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical advertising data is used to determine budget allocation, then return-on-investment can be optimized for established channels, but the approach becomes ineffective for new channels or products without historical data
Solution Approach 1:
The system performs preliminary testing by allocating test budgets to new advertising channels before full-scale deployment. This allows performance data to be collected in advance, enabling data-driven decisions for new channels without relying on historical data that doesn't exist yet.
Solution Approach 2:
The system changes the approach from relying on historical performance parameters to using test-period performance parameters. By shifting the time parameter from historical to contemporary testing, the system can evaluate both established and new channels using the same performance-based methodology.
2Productivity
If advertising budget is allocated based on past performance, then established advertising channels can be optimized, but the system cannot accommodate new advertising channels or products
Solution Approach 1:
The system transitions from a static allocation model based on historical data to a dynamic model that continuously adapts based on test-period performance. This allows the budget allocation to be optimized for established channels while simultaneously accommodating and evaluating new channels on equal footing.
Solution Approach 2:
The system implements a feedback mechanism where test-period performance measures are fed back into the budget allocation decision. This closed-loop approach allows continuous optimization for both established and new channels, with performance data from any channel informing future allocation decisions.
3Loss of information
If multiple advertising channels are tested simultaneously, then comprehensive performance data can be collected, but the complexity of determining optimal budget allocation increases
Solution Approach 1:
The system segments the advertising channels into distinct test units, each evaluated independently during the test period. This segmentation allows comprehensive performance data collection for each channel while simplifying the overall analysis by treating each channel's performance as a separate, comparable metric that feeds into the automated optimization algorithm.
Data Source
AI summary
Methods, systems, and apparatus, including computer program products, for determining a mixture ratio for allocating portions of an advertising budget among different advertising channels (e.g., print, online, radio, television) to optimize a performance measure, such as cost-per-action. A mixture space is used to define the available advertising channels and any constraints placed on those channels, such as no more than fifty percent of the advertising budget being allocated to a particular channel, and test mixture ratios are selected according to an optimality criterion. The selected test mixture ratios are used during a testing period on live traffic. The performance measures from the test mixture ratios are used to select a preferred mixture ratio from the mixture space.