How AI-Driven Automated Experimentation is Replacing A/B Testing
How AI-Driven Automated Experimentation is Replacing A/B Testing
A/B testing can be a powerful tool, but artificial intelligence can take it to the next level. Here’s how AI-Driven automation is replacing A/B testing in modern marketing. A/B testing is a tried and true methodology marketers have been using for decades to make more informed decisions and eliminate bias as much as possible. Advanced marketers and business leaders understand the importance of minimizing human error and gut-based decisions, and instead acting on relevant data gathered from reputable sources.
The problem with manual A/B testing in 2022 - Split testing is a great way for marketers to compare two pieces of marketing material (whether it’s two emails, landing pages, or types of social posts) to see which elements perform better. This is done through statistical analysis, and while doing this manually or through a dedicated tool might not be a problem when the data source is relatively small, nowadays marketers are overwhelmed by the sheer volume of data generated by different customer segments. This creates the need to move away from manual testing and embrace automated testing methodologies, but with AI-driven capabilities and machine learning systems.
AI testing facilitates automation - Automation in A/B testing is crucial for ensuring efficiency for reducing the risk of human error. Modern testing tools have the capability to automate numerous repetitive processes to cut manual labor and allow marketers to focus on more complex tasks. Artificial intelligence, however, possesses the capability to not only automate data collection and reporting, for example but also to contextualize data to derive meaningful insights.
Extracting value from real-time data - AI-driven testing tools that use machine learning methodologies and algorithms are able to collect and analyze vast amounts of data at all times, which is the level of performance we as humans simply can’t hope to match. By collecting and analyzing real-time data, and by creating value with data, then testing multiple hypotheses at once (more on that in a bit), machine learning systems are then able to deliver more tailored experiences to the individual.
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