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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.
And for a long time, that’s what A/B testing allowed them to do. However, modern marketing is always changing, and new technologies are shifting the paradigm, with artificial intelligence changing the way we conduct these tests and gather data. This should come as no surprise, because in the modern, tech-driven world, we encounter more data than we can manually analyze, and we need AI’s help.
With that in mind, today we are taking a look at AI-driven automation and testing in modern marketing, how it’s replacing A/B testing, and what it brings to marketing technology.
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. This problem is present in QA testing as well as other fields where large data quantities can make it impossible for the testers to optimize the product or personalize the messaging to the needs of various segments.
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. AI-driven automation platforms are complex systems that do require human oversight, but their machine learning capabilities allow them to become more self-reliant over time, requiring less oversight and fewer inputs to create actionable results.
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.
For modern marketers, this means that they are able to think more laterally in terms of their segmentation and target a variety of new customer segments with personalized messaging and campaigns.
Machine learning and AI test multiple hypotheses
One of the most significant advantages of AI-based systems and the machine learning subset of artificial intelligence is that it’s able to efficiently evaluate a broad set of hypotheses within a single experiment.
While the AI experiment is taking place, the machine learning algorithm is able to test which hypotheses generate a positive performance increase based on the analyzed data.
Artificial intelligence is also able to improve over time, giving the ability to run more complex tests with numerous other variables at the same time in a single experiment. The AI system can then create new experiments by combining the best-performing hypotheses, and continuously testing until it identifies the hypothesis that would generate maximum results.
AI could be the future of multivariate testing
Given the fact that AI, along with machine learning, is able to create experiments that test different hypotheses at the same time, that means that the technology might also be the future of multivariate testing in marketing. Multivariate testing allows marketers to test different combinations of variables and elements at once.
That means that AI is not only able to test multiple hypotheses, but it’s also able to test different variables within those hypotheses to make them more viable for different marketing purposes. All of this creates a more efficient and cost-effective system that marketers can use to maximize the ROI of new campaigns while targeting new segments with tailored messaging and campaigns.
Over to you
While A/B testing is not going anywhere anytime soon, artificial intelligence is changing how marketers conduct their tests and how they collect, collate, and act on vast amounts of data. Without a doubt, artificial intelligence and machine learning are going to revolutionize digital marketing in the years to come, automating testing methods, reducing test times, and learning quickly to bring data-driven solutions to the table faster than ever before.
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ABOUT THE AUTHOR
Nikola Sekulić, Brand Developer, Writer, and Storyteller
Nikola Sekulić is a seasoned brand developer, writer, and storyteller. Over the last decade, he has worked on various marketing, branding, and copywriting projects – crafting plans and strategies, writing creative online and offline content, and making ideas happen. When Nikola is not working for clients around the world, he explores new topics and develops fresh ideas to turn them into engaging stories for the online community.
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