Everybody is talking about artificial intelligence (AI) these days, and email marketers are no exception.
Despite the fact that email is far from the newest and most exciting digital marketing channel, it remains one of the most productive for countless companies. In fact, according to Campaign Monitor, for every $1 spent, email marketing generates a whopping $38 in revenue.
Given the importance of email to many companies’ businesses, it’s no surprise that use of AI, and machine learning (ML) specifically, is growing among email marketers.
Here’s a look at how AI is being applied to email marketing.
Multivariate and A/B Testing
Sophisticated email marketers have been using multivariate and A/B testing for years but AI and ML allow marketers to perform testing in ways that weren’t possible before.
A growing number of technology vendors offer AI and ML-powered testing platforms that enable marketers to create more robust tests, more quickly identify trends and make predictions, and identify subtle differences between tests that might go unnoticed without the assistance of AI.
Some platforms, like Optimail, even offer email marketers the ability to optimize their campaigns on the fly, eliminating the risk of lost revenue while tests run.
Subject line and copy optimization
What subject lines and email copy will produce the best results? For years, marketers have struggled to remove the guesswork from creating the perfect email – without much success.
Now, thanks to platforms offered by companies such as Phrasee and Persado, email marketers can let AI determine which subject lines, body copy and calls-to-action recipients are most likely to respond to.
Machine learning allows these platforms to learn what resonates best with a specific marketer’s audience. These platforms then use natural language technology to create subject lines, body copy and calls-to-action that not only sound like they were written by a human, but are consistent with the language typically used by the brand.
The result? According to Phrasee, its AI-generated subject lines outperform human-written subject lines more than 95% of the time, and Persado goes so far as to claim that its “cognitive content” outperforms man-made content 100% of the time.
While those kind of claims are hard to resist, even email marketers who aren’t yet comfortable letting AI take the wheel and drive have the ability to take advantage of the technology. For example, Touchstone allows marketers to create a “virtual simulation” of their email subscribers and predict impression, click and conversion rates for different subject lines.
Send time optimization
When it comes to optimizing the success of an email marketing campaign, few details are too small to ignore. Take send times. For years, marketers have recognized that when they send emails can have a meaningful impact on opens and clicks.
For example, an email recipient in London might be less likely to open an email that is delivered in the dead of night because the send time was optimized for subscribers in a far-off time zone. For this reason, some email marketers segment their subscribers in an effort to ensure that their emails are delivered to each segment at a time considered ideal.
Machine learning, however, offers an even better approach: instead of making big assumptions and creating large segments, it is possible for a machine to learn when each individual recipient is most likely to open an email and then optimize send time on a per-subscriber basis.
Doing this manually would be all but impossible, but it’s easy work for machines and a growing number of vendors, like Boomtrain, have incorporated it into their platforms.
Personalization is arguably the holy grail of email marketing and just like the real Holy Grail, it has proven to be incredibly elusive. But AI could finally change that.
For example, Adobe has integrated its Sensei AI platform into its Adobe Campaigns email marketing solution. Not only can Sensei AI’s ML technology be used to personalize subject lines, it can now personalize the images that are displayed in the email:
As an image is inserted into an email, a score is calculated based on industry data of how customers have reacted to similar images based on three million assets. The algorithm automatically recommends how to adjust the image to achieve a higher engagement rate. For example, the feature may predict that an outdoor gear retailer’s spring promotion email will perform better serving up an orange six-person tent versus a blue two-person tent.
This level of personalization would be all but impossible to achieve without AI.
In addition to using AI to optimize email campaigns, AI is being applied to the data that these campaigns generate.
For instance, because Adobe Campaigns is a part of Adobe Marketing Cloud, which includes an analytics solution, Adobe’s Sensei AI platform can incorporate data from email marketing campaigns into broader analyses. Specifically, engagement data from email campaigns is now being used to help companies using Adobe Marketing Cloud predict customer churn.
Historically, email marketing has been a largely manual, campaign-oriented activity. But in recent years, more and more companies have started to fold email into their broader marketing automation strategies.
AI and ML are an increasingly important part of marketing automation platforms, as they can help these platforms identify the behaviors and events that should trigger email-based marketing communications, and determine how the messages delivered should be tailored to produce the desired results.