May 30, 2022
We all know how important A/B testing is when it comes to optimizing email campaigns.
But what’s surprising is the fact that a lot of brands don’t conduct A/B email testing at all. In fact, 65% of brands rarely A/B test their automated emails, and 76% never test their transactional emails.
For dental practices, continued communication is crucial for helping patients feel satisfied with their dental experience and encouraging them to return for more appointments in the future.
This is why you need to get your dentist newsletters right. A/B testing can help you achieve this.
In this article, we will share with you the 7 mistakes to avoid when doing A/B email testing so you can create effective email campaigns that deliver results.
Email A/B testing is the process of sending different versions of a single email to various subsets of your subscribers to see how a small variation of your campaign can have an impact on your results.
Using the data you gathered from A/B testing, you can find out what works with your patients and create more effective campaigns that generate the best outcome for your dental practice.
Knowing the common email testing mistakes will allow you to set up tests properly and measure outcomes correctly. As a result, you can determine the most effective way to structure your dentist newsletters. This puts your email program on the path to success.
Here are the seven common email testing problems and how to fix them:
A hypothesis is a clear and specific prediction of an outcome that you can verify by experimenting.
Many marketers skip this step or sometimes base their tests on unclear hypotheses, which causes their testing to fail.
Always formulate a clear hypothesis for your email tests. This will help you to determine what variables, success metrics, and testing segments you should use for your A/B testing design or approach.
A well-structured hypothesis is testable, addresses conversion barriers, and provides marketing insights
How exactly do you write a great hypothesis for marketing experimentation?
Here’s an example:
Idea: “My newsletter needs a new subject line.”
Turn the idea into a hypothesis: "A new subject line on my newsletter will increase the open rate."
Identify the overarching theme and apply it to the hypothesis: "Improving the clarity of my subject lines will reduce confusion and increase email open rates.”
Refine your hypothesis to answer what works, why it works, where it works, and who it works on: "Changing the subject line of the newsletters to set expectations for recipients (from ‘Do you have a sec for your teeth?’ to ‘Hey Emily, is tooth sensitivity making you miserable?’) will reduce confusion about the content of my newsletter and encourage them to click and open my email.
Hypothesizing is crucial because it ensures that the results from your experiment are measurable and significant. It will also help you understand how effective the changes that you made in your tests are.
Tip: Before doing a split test, analyze your previous email campaigns to identify specific problem areas and then use that data to formulate a hypothesis.
Another factor that’s limiting the success of your email A/B testing is using the wrong metrics to measure success. The wrong metrics can mislead you into creating an email copy that won’t generate the results you want.
A lot of marketers use open-rate to determine the success of their email split tests but it’s a vanity metric. It’s so easy to manipulate; send it to your active subscribers only and your open rate will skyrocket.
Although a high open rate can make your email campaigns look better, it doesn't automatically lead to results. In fact, in a Consumer Email Tracker Report in 2021, data showed that 33% of consumers will go and compare a product they want to buy mentioned in an email, while 21% said they would click through from the email. This means that increased opens don’t automatically correlate to increased conversion.
Email is a highly trackable channel, and the most reliable and meaningful metric you should be measuring is the conversion rate.
So, what you should do is analyze your previous dental email campaigns and then create a hypothesis to determine which subject lines drive the most conversions and success for your dental practice.
It’s important to have enough data to work with to generate results that are statistically significant because then you can be sure that your findings are reliable and not just due to chance.
If your results aren’t statistically significant, it could lead you to make wrong conclusions and misinterpret your campaign’s outcome.
Make sure you run your tests long enough or have enough people to test them with to generate an ample amount of data. Experts suggest that you run your experiment for at least one to two weeks and a maximum period of four weeks.
There’s a huge chance that your recipients will react differently to different campaigns, which means that you’re not going to get accurate data after running one test.
You need to repeat your AB test multiple times to exclude anomalies. Don’t stop at one and apply the results to all your future campaigns. Instead, repeat your tests and use the standard error to compute the range of possible conversion values for a specific variation.
This will provide you with data you can use to spot trends, learn more about your audience, and discover shifts in customer behavior and attitudes. Use these to improve your dentist newsletters or overhaul your entire email marketing strategy to drive better results.
It’s important to focus your email AB testing efforts on certain elements that you know are going to advance your campaign’s performance, such as:
According to Litmus’ survey of 3,000 marketers, most brands focus their email A/B testing efforts on CTAs and subject lines. We think you should do it too.
However, unless you're doing multivariate testing, it’s best to limit your AB tests to one change per test. Things can get pretty overwhelming if you test various elements at a time. Plus, there’s no way you can clearly determine which element led to the change in performance if you have too much going on in your tests.
Having more than one difference between versions A and B makes it difficult to determine which element led to the difference in performance.
Optimizing the right elements in your email campaigns can definitely help you combat and solve low delivery rates, limited post-click activity, low open rates, and many more.
However, not all changes can help you achieve the maximum results possible for your email. For example, testing the color of a CTA button or CTA copy is only going to help you get closer to optimizing your email design.
But, testing an interactive email versus a text-only newsletter will help you determine the best way to communicate your message.
You need to assess where the variations you’re making in your copy are making incremental or radical changes.
Testing is necessary because it reveals how well your tactics are working, what needs improvement, and if you're investing in things that will help you achieve your goals now and in the long term.
As a result, you can use them to design more effective emails, test them, and develop your deliverability rates.
If your results remain on paper and are not put into action, you will never see the results you want. So, make it a point to refine your email campaign every time you run an A/B test.
A/B email testing is the best way to know how to structure effective email campaigns and create dentist newsletters that convert and deliver results for your practice.
Keep these mistakes in mind and avoid them at all costs when running tests so you end up generating data that will help you choose email strategies and tactics that are guaranteed to work on your patients.
At Digital Resource, our team of digital marketing specialists will work with you to create a custom email marketing strategy that will help you generate more patients and make more profit for your practice.
Contact us today to start getting more dental appointments tomorrow!