Here are some best practices for A/B testing:

Email Marketing A/B Test



You can test any of the parts of an email. For example:


From name

Subject line

Preheader

Content (images, copy, or call-to-action)

Send time, date, or day


Test 1 variable at a time, Limit your A/B test to isolate only one variable at a time. If you change the subject line and send time for each version, for example:

Version A: Limited Time Only Offer! 25% off our new summer line - send at 9 AM

Version B: Introducing our lightest, most breathable cotton shirt yet-- 25% off - send at 12 PM


You can’t be sure what caused one email to perform better than another. For example, if Version B outperformed Version A, was it the send time or the subject line that made the difference?

Choose a success metric based on your campaign objectives

How do you pick a winning campaign? It depends on your marketing objectives.

If your objective is to increase sales, you may want to look at the revenue generated from each test group.

If your objective is to notify customers about an upcoming software release, you may want to prioritize an open rate.

If your objective is to drive traffic to your website, you may want to prioritize the click rate.

Use a large sample size

You need a relatively large list to conduct an A/B test. If you send your test to a sample size that is too small, your results may not be statistically significant. In other words, one email may perform better than another due to chance.


Optimizely provides a free calculator to determine appropriate sample sizes for your A/B tests. You enter your baseline conversion rate (let’s say your emails normally have a conversion rate of 4%) and the minimum detectable effect (your target relative change in the conversion rate from running your experiment). Let’s say that you want to catch anything that falls within a 2-6% conversion rate range. Your minimum detectable effect would be 50%, which translates to 2% above or below our baseline rate of 4%.

According to the calculator, this would require a sample size of 1,200 per variation with 90% statistical significance. If you want 95% statistical significance, you need 1,300 subscribers per variation. This means, that if you are going to test two variations of your email, you need to send your A/B test to at least 2,400 subscribers in total.


a/b test free calculator


 

 

One way to optimize email campaign performance is to

understand if your email content is appealing to the right subscribers.


You can use customer data to segment your subscriber list and send emails with different content to customers with different preferences and needs.

How can you get this information?

Well, you could ask customers to specify their gender through a contact or an e-mail sign-up form.

You can also collect and store information on your customers that you do not explicitly ask them for on the contact form.

For example, let's say a customer buys a book on your website.

When they complete their purchase they are asked if They want to subscribe to e-mail updates from you. you now have the customer's name, mailing address, and phone number from the shipping info. You also know a little bit about the customers reading habits.

Did she buy a best seller? A sci-fi novel? A self-help book?

And you can use this information to recommend other books in the future.

Additionally, you can capture information like the data for purchase, time spent on your website.

The device she used to access your website, etc.

You could also look at past purchase history Collecting data on your customers helps you to segment your customers in different ways. Once you have customer segments, you can more easily customize email content for your various segments.

Customizing email content for

different customer segments of your list can lead to higher open rates, click rates, and conversion. you should tailor content for each customer according to his buying journey

 
Customers Buying Journey

Data as the starting point for Segmentation 

To segment your customers, you need access to data. Carefully consider what data is most important to collect. Do you want to target specific customer subgroups by

  • Age
  • Gender
  • Location
  • Purchase History (When did they last buy something? How frequently do they shop on your site? What do they buy?)
  • Stage of the Sales Process (Are they a new customer? Did they add something to their cart without completing checkout?)
  • Engagement (How often do they open your emails? Have they attended any in-store events?)

Or some other information? Collect the data that will best help you serve your customers. This information will be stored in a database. You can filter this database to create your subgroups.

Here’s what this process looks like in MailChimp:

You can access your list of contacts, and choose to "manage contacts". There you will find an option to access existing segments or create a new segment. When you choose to create a new segment, you can select from different options, following logic to build the segment that matters to you. The options you can choose from are all dependent on the data you have collected related to the email addresses in your list.

What might happen if your data set is incomplete for certain customers? This happens all the time in the real world. For instance, imagine you are advertising an in-store event to customers in your area. You collect location data in your database, but this information is missing for about 10% of your subscribers. In a case like that, only the users for whom you have the relevant information will be added to your segment. Other subscribers won't fall under the specific segment you are creating. Of course, the more precise and complete your data are, the better your options to target the customers in the future.


Summary

To recap, in this Article you learned key metrics like open rate and conversion rate.

You also explored how to use AB testing and segmentation to optimize your email campaigns. By continually collecting, analyzing, and reflecting on data, you can identify what works for your customers.

Making data-driven decisions about your email content can help you increase engagement, drive sales, and grow your business.

Congratulations, you've come a long way.

You've learned how to build your subscriber list, create effective email copy, plan an email campaign, analyze your campaign results, and finally, measure and improve your emails through AB testing and segmentation.

By now, you can start an Email Marketing Business with confidence.