smarter healthcare marketing outreach with a/b testing

The email went out at 9:47 AM on a Tuesday.

Subject line: "Important: Schedule Your Mammogram Today."

The email landed in three thousand inboxes, was opened by forty-two women, clicked by eleven, and converted into just two scheduled appointments.

The same health system sent a nearly identical campaign six weeks later. Different subject line: "We saved you a spot- your annual mammogram." Same audience size. Same clinical urgency. Same scheduling link.

This time? Four hundred seventy-three opens. One hundred twelve clicks. Sixty-three appointments booked. What changed? Six words.

But here's what didn't change: the marketing team still couldn't explain why the second version crushed the first. Was it the personalized language? The implied scarcity? The softer tone? They knew it worked. They just didn't know what to replicate.

This is the silent crisis plaguing healthcare marketing. Not a lack of effort or expertise, but a fundamental inability to distinguish signal from noise. You're making hundred-variable decisions- subject line, send time, sender name, preview text, content tone, call-to-action, visual hierarchy, and you're doing it blindly, then hoping the combination somehow resonates with patients whose attention spans are measured in milliseconds.

Every campaign becomes a high-stakes gamble where you won't know the odds until after you've already bet the entire audience. A/B testing doesn't just improve those odds. It eliminates the gambling entirely, replacing intuition-based marketing with a systematic approach to understanding exactly what makes your patients engage, click, and ultimately, schedule the care they need.

what is a/b testing in healthcare marketing?

A/B testing, sometimes called split testing, is the practice of comparing two variations of a campaign element to determine which performs better with your target audience. You split your audience randomly into two groups, send each group a slightly different version, then let the data tell you which approach wins. 

The methodology is simple, but the insights can be transformative. What works for one health system might not work for another. What resonates with Medicare patients might fall flat with your commercial insurance population. A/B testing removes the what-if factor and replaces it with patient-specific intelligence.

implementing a/b testing for patient outreach

step 1: start with your biggest questions

Don't test for the sake of testing. Begin with the questions that matter most to your campaign performance. Are your open rates struggling? That's a clear signal to focus on the first impression elements, specifically your subject lines and sender names. These are the only things patients see before deciding whether to engage with your message. 

Getting opens but no clicks? That indicates your initial hook is working, but something about your content, messaging approach, or call-to-action isn't compelling enough to drive the next step. Understanding where your campaigns are breaking down helps you prioritize which tests will deliver the most meaningful improvements.

step 2: ensure statistical validity

This is where most healthcare marketers fail because they run tests with small audiences that will not give significant results. You should have a reasonable size so that you are sure that it was not just by chance that you have scored a given way. Consider the case of testing two subject lines that contain 50 individuals each. 

Version A gets 15 opens, and Version B gets 12 opens. Does that mean Version A is definitely better? Not necessarily. With such small numbers, random variation could easily account for the difference. You might confidently roll out Version A to your entire list only to find it performs worse than the original. Statistical significance matters, and achieving it requires an adequate sample size.

step 3: choose your success metric

What defines "winning"? Such a question may appear to be straightforward; however, it is worth paying attention to, as the response to it ought to correspond to your real campaign goal. Open rates are simple to gauge; however, they do not necessarily correspond to your intended purpose. An eye-catching subject line may get opens without bringing any serious downstream action. 

Frequency of click-throughs is an indication of interest in content, which means that patients were interested in whatever you had to say to the extent of visiting to know more. Conversion rate, be it in the number of appointments made, the number of forms filled, or the number of screening tests made, is an actual action that has an effect on patient health and organizational performance.

step 4: set a testing duration

Allow your test sufficient time to normalize various behaviors in patients, since individuals use email on very dissimilar schedules and timetables. Some patients open their email accounts and read their mail before going to work in the morning.

There are others who do their browsing during the evenings after dinner when they are free to ponder over issues that are not urgent. Other respondents react instantly when they open it, and some take a couple of days pondering whether they are prepared to make an appointment or implement the suggested measure. All these behavior patterns should be included in your testing window.

In the majority of healthcare marketing campaigns, a three-to seven-day testing period is enough to get enough data and, at the same time, be able to take action based on the insights within a reasonable time frame. Time-sensitive communications, such as appointment reminders or visits scheduled in the next week, may need shorter windows since you can’t afford to wait too long to get to your entire audience.

step 5: document, learn, and compound your wins

This is where A/B testing is not a one-time campaign strategy but a strategic benefit that develops in the long term. Record what you know and make it detailed enough that the next members of the team can gain an idea not only of what worked, but why you think it did, and within what context the test was conducted.

Did evening sends outperform morning sends for your Medicare population? Test it again with a different campaign to confirm the pattern isn't just an anomaly. Did emotional messaging beat clinical messaging for preventive screenings? Apply that insight across similar use cases while recognizing that it might not hold true for acute care communications or specialty service promotions.

cured: a/b testing built for healthcare workflows

Cured is built for healthcare marketers who need advanced machine learning based testing without complicated technical baggage. Whereas numerous marketing platforms provide very simplistic A/B testing services as an ancillary feature, Cured has crafted its offerings directly around the specifics of patient outreach, HIPAA compliance, and healthcare-specific workflows.

The platform realizes that healthcare marketing is not a matter of simple click generation: it is a matter of patient outcomes, of more effective contact, and that purpose dictates the instruments that cannot be too complicated or too user-friendly.

Integrated A/B testing workflows sit at the heart of Cured's approach, allowing you to create test variations, configure audience splits, set testing durations, and monitor comparative performance all within the unified interface you already use for campaign management. 

There's no need to export data to separate analytics tools, manually split audiences in your EHR, or manually compile reports from multiple systems. Everything lives in one place, which reduces the friction that prevents many healthcare marketers from testing consistently. When testing is this accessible, it becomes a habit rather than a special-occasion activity.