Reaching the right customers with the right message at the right time has always been a challenge for marketers. In email marketing in particular, we’ve seen an evolution of tools for reaching customers more effectively. Features such as dynamic content, for example, can make emails more relevant, spurring higher click-through rates.

Still, there’s always been the need to test, analyze, and refine content to make emails better. A/B testing has long been a go-to option for marketers wanting to improve their content, yet it still relies on backward-looking data. Unfortunately, marketers haven’t always been able to predict the types of content subscribers will engage with.

This is where artificial intelligence can take some of the initial guesswork out of the email marketing equation. Marketers can combine A/B testing and Einstein Engagement Scoring to beef up email marketing efforts.


What is A/B testing, and how does it work?

A/B testing is the process of evaluating email content by sending one control email to a portion of email subscribers and the same email with one variable to the other portion of subscribers. It’s important to note that a true A/B test only changes one element at a time. This will allow you to pinpoint which pieces of content — like the subject line, preheader text, or button style — affect certain metrics.

Check out this graphic to see which parts of an email work well for A/B tests:



Once you’ve identified which pieces of content subscribers prefer, you’ll be able to refine that content further. For example, think about testing two different email subject lines during the holiday season:

  1. Subject line one: Buy one, get one free on all boots!

  2. Subject line two: Here’s a gift just for you.

One subject line states exactly what to expect (a buy one, get one free deal). The other subject line teases the gift but also sounds a little bit more personal by stating “just for you.” In an A/B test, you’ll be able to see the difference in email open rates and, hopefully, a clear subject line winner.

Explore more A/B testing advice here.


How can marketers improve A/B testing?

A/B testing, when done properly, is a game changer. But as mentioned earlier, it’s not always perfect because marketers use backward-looking data — in other words, data that can’t be predicted.

However, with Einstein Engagement Scoring, a type of artificial intelligence, marketers can score every customer’s likelihood to engage with your emails or convert on the web before diving into A/B testing. This AI-enabled feature predicts if subscribers are likely to open an email, click on email content, convert on the web, or stay subscribed to an email list.

One of the main benefits of this type of feature is helping marketers refine A/B testing. Think about it this way: If you know which subscribers are likely to unsubscribe, they probably don’t want to receive promotional email content, right? These subscribers have needs that are different from engaged customers.

As a marketer who has this knowledge, you would be able to intelligently craft content for these at-risk subscribers. In this case, you could try out two different subject lines, both of them focused on addressing specific subscriber needs. Perhaps you could use the opportunity to gather a small amount of feedback about which content these subscribers would like to see more often. Making a clear, transparent effort to personalize email content could resonate with at-risk subscribers more than if you tried to feed them particular deals or promotions.

With Einstein Engagement Scoring paired with smart A/B testing, marketers can make their email content more relevant and impactful by ensuring they are testing with the right audience. For more ways to make your email marketing more intelligent, read up on these five email call-to-action best practices and eight tips on writing concise subject lines.