Naytev's data-driven testing prioritizes messages based on their performance. Test budgets are dynamically allocated across all messages in a given test. 

More budget is allocated to better performing messages, and less budget is allocated to worse performing messages.

Naytev's machine learning approach to message testing greatly outperforms traditional A/B testing approaches. 

Traditional A/B tests inefficiently allocate resources evenly across all variables, requiring significantly more budget and time to achieve the same results as Naytev's optimized testing.

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