Email continues to be a core investment area for marketing teams. The reason is clear–email is the channel generating the highest ROI for marketers. For every $1 spent, email marketing generates $38 in ROI. Email is also 40 times more effective at acquiring new customers than social channels, specifically Facebook and Twitter.
On the engagement side, we found that email engagement has been steadily increasing over the last three years. Open rates have increased by more than 10% from 2016 to 2018. We believe these increases can be attributed to marketing teams investing in three key levers to enhance their email programs: 1. Making personalization a priority, 2. Using machine learning to create more sophisticated targeting and personalization, and 3. Leveraging automation to enable workflow efficiencies.
Here we dive deeper into each of these levers to understand their impact. We also share suggestions for how to pull each one to enhance your own email marketing strategy.
Personalizing Emails to a Segment of One
Marketers understand that they can tailor email content to deliver highly relevant messaging and higher open rates. We’ve learned that basic personalization, whether that’s as simple as utilizing a first name in a message or swapping out offers based on subscriber behavior, can have a considerable impact on email engagement rates.
More explicit results on the ROI of email personalization are in the numbers:
- 50% of companies think that more personalization can improve email interaction among subscribers.
- Brands that personalize marketing emails experience 27% higher unique click rates and 11% higher open rates than those that do not personalize.
- Segmented, targeted, and personalized emails generate 58% of all revenue.
- Personalized email marketing generates a median ROI of 122%.
While standard segmentation-based personalization is yielding positive results, customers crave individually relevant experiences from brands. True 1-to-1 personalization is delivering content based on the unique characteristics and behaviors of each individual, and in turn, delivering individualized content for each person based on their preferences and content consumption history.
To be feasible for even smaller email lists, this level of individualization requires machine learning to create content experiences tailored to the unique behavior and context of each person.
Using Machine Learning for Scalable Individualization
As mentioned above, machine learning is the critical factor that makes individualization possible. But how does it work? In a nutshell, machine learning algorithms dynamically select pieces content for individual members of your audience. These personalized content experiences more deeply engage them through email to build a more complete relationship with your brand.
Brands with large audiences and big content engines have the luxury of volume (many pieces of content to deliver to many individuals). But these volumes can generate thousands of content combinations that can be cumbersome or even impossible to identify. With the right machine learning and AI-based tools by your side, you can seamlessly automate the delivery of the right combination of content to the right individuals.
With the right email individualization tools in place, you can deliver highly relevant content to the right people at the right time, essentially individualizing the entire content journey in real time.
Leveraging Automation to Enable Workforce Efficiencies
The final lever that enables marketing teams to enhance their email programs is automation via machine learning tools. Machine learning can more efficiently handle the executional tasks associated with running email individualization programs, enabling marketers to up-level their output and focus on more strategic work. For example, machine learning can automate tasks such as audience analysis and content selection.
After implementing automated individualization tools, one national grocery brand saw a 90% reduction in time that it would normally take to create a newsletter. What’s more, the individualized content they are delivering via email is more relevant and engaging for their audience. In addition to higher click-to-open rates, customers are engaging longer with the brand’s content once onsite.
But how much time and manpower does machine learning based automation actually save on a regular basis? Our research shows that marketers believe machine learning would save them more than 36 man hours per week, which they typically spend on manual email campaign operations. This includes tasks such as content selection, content development and list building–all in an attempt to personalize the content experience. After implementing individualization technology, these teams would shift those work hours to more strategic initiatives, like program planning and scaling.
Click here to read our full report for more data-driven insights on how email marketers can use machine learning to advance email personalization beyond traditional segmentation.
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