What are the steps to conduct effective A/B testing for digital marketing?

The value of A/B testing is only as strong as the goal(s), the measurement, and the intention behind it. Once you've got those and run the experimentation, interpret your results and dig into the why.

This article is missing a crucial step. Experimentation is a system, and you shouldn't operate without a structured learning agenda. I've found that it helps to think about it across multiple dimensions, for example: 1) Creatives (e.g., types of messaging) 2) Audiences (e.g., broad targeting vs. narrow audiences) 3) Budget structure and marginality (e.g., prospecting vs. retargeting, CPA changes with different bids/spend volume) 4) Placements 5) Other (e.g., incrementality, landing pages) Having a learning agenda will make your experiments intentional and focused, reducing the risk of setting up tests randomly.

When conducting A/B tests, my team and I prioritize experimenting with uncertain elements or embracing creative alternatives over conservative choices. In our ad campaigns, we evaluate two diverse creative approaches, such as comparing video content to carousels; or two different headlines for a static ad. Similarly, in our email marketing, we focus on testing variations in subject lines and call-to-action elements. To succeed in A/B testing, keep these tips in mind: 1. Define clear objectives for each test. 2. Ensure your sample size is statistically significant. 3. Monitor metrics like click-through rates, conversions, and engagement. 4. Analyze results to inform future strategies. 5. Continuously refine your approach based on findings.

The decision on which variations to test in A/B testing should be guided by hypotheses grounded in reasons or theory about why those variations are relevant to what is being tested, otherwise insights on why a variation succeeds or fails become elusive and we are unlikely to learn how to improve it. In the case of message testing, A/B testing often becomes an inefficient exercise of randomly throwing spaghetti at the wall to see what sticks without a framework about what resonates with a target audience on a particular topic. This framework and underlying hypotheses should be based on audience research designed to understand their needs, attitudes and behaviors related to the product, service, topic we are communicating about.

I find that most teams that A/B test either don't test significant things, or don't take the time to properly debrief on the outcome. A/B testing has its place, but I've found that a lot of marketers don't slow down to truly think scenarios through.

Everything in marketing is a test. What works for one company/audience, won't necessarily work elsewhere. Especially early, do your due diligence to fully understand what's "working" and what isn't. For example: running Facebook ads for a week and saying it didn't work doesn't make it true. What was the targeting? How relevant was the content/messaging? Who ran the ads—did they know what they were doing? All things you to uncover clearly before you can correctly label them.