Let’s just think about how many marketing decisions are made simply because something feels like the better option.
A headline sounds stronger. A button looks better in a different place. One call to action seems more direct than another. Perhaps even a header seems more precise for the content. There is always going to be some level of judgment involved in marketing, but one of the benefits of digital marketing is that we do not necessarily have to stop there. Feedback can give us another perspective, while testing allows us to challenge our assumptions and see how people actually respond. This notion becomes especially interesting in healthcare marketing because even seemingly small communication choices can influence how someone navigates their next step.
When the Obvious Winner Isn’t So Obvious
Imagine a healthcare service page where the goal is to help potential patients connect with care. For many healthcare systems, actions that help patients take that next step can represent an important conversion. One variation of the page has a call to action (CTA) that says “Schedule an Appointment,” while another says “Find a Doctor.” Both seem reasonable for a healthcare service page, and it’s easy to see a marketing team having a preference for one over the other. But instead of choosing based entirely on preference, this is something that could be tested rather than debated in a meeting.
The more important question, though, is: What would actually make one version more effective?
If “Find a Doctor” receives more clicks, it might initially look like the obvious winner; the data supports it, doesn’t it? But what if the people who select “Schedule an Appointment” are ultimately more likely to complete an appointment request? Those patients have followed through with the scheduling process and completed the intended action. In that case, the version generating fewer clicks could actually be doing a better job of accomplishing the larger objective.
Not Every Metric Tells the Same Story
That is where I think experimentation becomes much more valuable than simply testing which headline, image, or button performs better. It forces marketers to think about what they are actually trying to accomplish before deciding what success looks like.
In his discussion of A/B testing, Anthony Miyazaki makes an important distinction between finding what performs better and assuming we have discovered the best option. An experiment can tell us which of the variations we tested performed better under those particular conditions, but it cannot tell us that the result will work best for every audience, platform, or situation. As the saying goes, one size doesn’t fit all. That distinction matters because it is easy for one successful test to suddenly become a “best practice” without considering the context behind it.
HubSpot makes a similar point in its conversation of A/B testing, recommending that marketers determine their primary goal metric before the experiment begins. This becomes especially important when different metrics tell different stories. A variation could have a higher click-through rate but a lower conversion rate, which means optimizing for clicks alone could lead the marketer toward the wrong decision. It is essential not to get caught up in the maze of metrics; be clear about your goal and know what you are actually trying to measure.
From Assumption to Evidence
To me, that is the real value of experimentation in marketing communications. It is not about testing everything just because we have the technology to do it, and it is definitely not about letting one test make every decision for us. Rather, it’s about questioning our assumptions, identifying the result that actually matters, and using evidence to make a more informed decision. In a way, experimentation gives marketers a structured way to challenge what we think will work instead of automatically treating our assumptions as facts. We’ve all heard of the scientific method, right?
The same basic thinking can apply here. We start with an idea or assumption, test it, observe what happens, and use what we learn to inform what comes next. The goal is not necessarily to prove that we were right from the beginning. Sometimes the more valuable outcome is discovering that we were wrong because that gives us something new to work with. In that sense, an unexpected result is not necessarily a loss.
Sometimes what looks better really will work better. But I would rather know why it works than assume that it does.
