Current A/B Testing Is Missing Opportunities
A/B testing your campaigns is marketing 101. The method of sending two different versions of a campaign—one being your business-as-usual and the other being your new initiative—is what helps marketers experiment without risking the attention of their entire customer file. Whether it’s trying out two types of copy or optimizing send times and audience segments, A/B testing is the safe but effective approach to ensuring that your content never gets stuck in a rut.
However, while the practice of A/B testing has obvious benefits, it’s not without its limitations. Due to common hurdles, it can be challenging to develop a true culture of experimentation, even when marketers are well aware of the value of A/B testing. Here, uncover how AI scales experimentation and ultimately derives more actionable data from every test.
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AI Scales Testing & Experimentation
Most of the common challenges surrounding A/B testing boil down to scalability. Marketers simply don’t have the time and manual resources to run and report on multiple tests for countless campaigns; even if they did, that wouldn’t leave them any time to create the content in the first place. And no matter how efficient the marketer, they simply cannot match the instantaneousness of automation.
With this in mind, it becomes clear that marketers need to rely on their martech stack to carry some of the load. While marketers should maintain the roles that only they can perform—such as setting key parameters and business rules for each test—they must rely on an automated AI approach for both executing and optimizing the message, as well as delivering performance results.
Run Incremental Testing
With traditional A/B testing, marketers have to prioritize the most obvious elements they want to experiment with. While this is a reasonable approach, the opportunity for small but effective changes in content will naturally be missed.
One of the most powerful ways that AI can scale A/B testing is through incremental testing. With the ability to take an always-on approach, marketers can send a control version along with an AI-powered version for every campaign. With every send, the AI models can generate fresh insights to uncover the best performing content, all while automatically optimizing the experience for the individual customer—not just the segment or entire file.
Balance Multiple Business Needs
Testing various elements in a message is key, but using that insight to produce enhanced content is paramount. By scaling A/B testing, marketers can also work towards multiple goals simultaneously. Rather than struggling to balance short-term revenue with long-term loyalty, AI’s scalability means that marketers can automatically deploy different types of content to guide customers towards specific goals.
For example, if a marketer is trying to increase email engagement, dormant and active customers will receive different messaging. The underlying reason marketers A/B test is to learn what content works for each customer, after all—with this level of scalability, they can now react quickly to this newfound knowledge
Automate Reporting
AI automation scales every phase of the A/B testing process, from performance metrics to real-time reporting and adaption.
When using AI, every send helps the solution enhance its output. This type of automated iteration is invaluable, as the AI will then power content that is predicted to resonate with customers. However, while these processes are automated, it’s not a fully set-it-and-forget-it approach—ultimately it is always the marketer who drives the ship by setting guardrails and editing content.
Create a Culture of Experimentation
Typically, A/B testing is a time consuming process for marketers. Manually, coming up with hypotheses, testing them, and learning from the results can take weeks, if not months, to gain meaningful results—especially when running advanced multivariate testing.
AI completely transforms that process and helps marketers stay a step ahead of customers’ actions and preferences in a way that would be virtually impossible through manual efforts alone. In short, optimized A/B testing requires a level of speed that cannot be achieved through typical, labor-intensive methods.
To discover more about how AI can scale your workflow, drive more value from existing investments, or boost revenue, explore the related resources below.
