Methods and systems for testing, comparing, and optimizing creatives with multiple factors in digital advertising is presented. Experiments are designed for testing a plurality of factors combined to form a creative. Ad campaigns are launched or continue according to the design and the creatives' campaign performance data is collected. Statistical modeling and hypothesis testing are used to predict the performance of the creatives based on the performance data. The creatives are compared based on the predictions and either activated or deactivated based upon their relationship to statistical confidence levels. All the stages are executed automatically and iteratively.
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