Multivariate Digital Campaign Content Exploration Using Rank-1 Best-Arm Identification
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Solution Overview
Problem
Conventional A/B/n testing systems become inefficient and resource-intensive when dealing with large numbers of parameter combinations, as they require testing every possible combination, leading to excessive time and resource consumption.
Innovation Solution
The system employs alternating best-arm identification in multiple dimensions using a rank-one matrix assumption to estimate the highest sampling value for each dimension, allowing for the execution of a digital campaign with the best combination without testing every possible combination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional A/B/n testing is used to test every possible parameter combination, then measurement precision of the best combination is improved, but loss of time and resource consumption increases significantly
Solution Approach 1:
The patent segments the parameter space into multiple dimensions, where each dimension represents a separate parameter category. Instead of testing all combinations across dimensions simultaneously, the system performs best-arm identification independently within each dimension, then combines results to determine the overall best combination. This segmentation reduces the testing burden from exponential (product of all dimensions) to linear (sum of dimensions).
Solution Approach 2:
The patent transforms the traditional single-dimension A/B/n testing approach into a multi-dimensional framework. By treating each parameter category as a separate dimension and applying best-arm identification across dimensions, the system achieves accurate combination selection without requiring exhaustive testing of all cross-dimensional combinations. This dimensional transformation enables scalable testing for campaigns with many parameters.
2Productivity
If the number of parameter combinations is increased to analyze more campaign variations, then productivity of campaign optimization is improved, but device complexity and resource requirements increase
Solution Approach 1:
The system segments the complex multivariate testing problem into independent dimensional analyses. Each dimension can be analyzed separately using best-arm identification, avoiding the need to manage and track exponentially growing combination sets. This segmentation maintains system simplicity while enabling analysis of many more parameter combinations than traditional methods.
Solution Approach 2:
Instead of requiring complete testing of all possible combinations, the patent applies partial action by performing best-arm identification within each dimension independently. This approach tests only the necessary subsets of combinations needed to identify the best arm in each dimension, then combines these results to determine the overall best combination, significantly reducing the total number of tests required.
3Reliability
If complete A/B/n testing is performed for all parameter combinations, then reliability of the selected combination is improved, but loss of substance in terms of computing and networking resources increases
Solution Approach 1:
The patent implements partial action by performing best-arm identification within each dimension rather than requiring complete testing of all cross-dimensional combinations. This approach achieves sufficient reliability for combination selection by independently identifying the best arm in each dimension and combining these results, while consuming significantly fewer computing and networking resources compared to exhaustive testing.
Solution Approach 2:
By segmenting the testing process into independent dimensional analyses, the system reduces resource consumption. Each dimension's best-arm identification can be performed with limited sampling, and the results are combined to determine the overall best combination. This segmentation maintains statistical reliability while dramatically reducing the total resources needed compared to testing all combinations.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for determining parameters for digital campaign content in connection with executing digital campaigns using a rank-one assumption and a best-arm identification algorithm. For example, the disclosed system alternately explores response data in the first dimension and response data in the second dimension using the rank-one assumption and the best-arm identification algorithm to estimate highest sampling values from each dimension. In one or more embodiments, the disclosed system uses the estimated highest sampling values from the first and second dimension to determine a combination with a highest sampling value in a parameter matrix constructed based on the first dimension and the second dimension, and then executes the digital campaign using the determined combination.