Experimentation with AI Solutions
AI marketing platforms that have built-in solutions for automated testing save enormous amounts of time in running experiments, tracking metrics for each test case, and reporting the significance of the observed results. There are several popular ways to run, measure, and report on these experiments.
Use A/B tests to split the audience and send different messages to each subset of the audience. Remember, A/B Tests need to be statistically significant for their results to be valid.
Automatic Winner Selection
As the scale of A/B testing increases, marketers often find themselves doing repetitive analyses often in spreadsheets outside their marketing tools. Using automated winner selection with the pre-set criterion on desired goal behaviors — click rates, number of orders, revenue, etc — the system can run A/B testing on a portion of the audience and automatically choose the winner and send the winning variation to the remaining audience. This can save countless hours of analysis and reporting.
Population Testing with Test and Control Audiences
In addition to A/B testing, marketers can split audiences into test and control buckets and measure the efficacy of intervention vs no intervention. Oftentimes when the unit economics of intervention is costly it’s desirable to measure and establish the soundness of intervention before scaling such interventions.
While AI algorithms have a built-in, continuous process of testing, learning, and optimizing marketers should still track and validate how their AI-powered campaigns are performing and adjust models and marketing actions as necessary. Stick to AI solutions that are transparent both on the inputs of the models and the output of the results to ensure you’re making the right investments for your business.