Blog Entry

How Email Marketers Can Balance Personalization and Segmentation

Can email marketers balance personalization and segmentation?

It’s an intriguing question for those looking to drive conversions with content that’s timely, engaging, relevant, and tailored to meet someone’s specific needs and interests.

In some respects, marketers consider the combination of personalization and segmentation their “Mount Everest.” They’re both crucial elements of email marketing. Make the two work together, and the world beats a path to your door. If you can’t, your email campaigns won’t perform well. 

How segmentation works

Segmentation helps marketers place prospects and customers into separate buckets or groups rather than one amorphous audience for every email campaign. It’s based on the understanding that specific customers behave differently. 

Email marketers can segment in many ways, such as:

  • Demographics (e.g., age, gender, location, occupation)

  • Position in the sales funnel (e.g., awareness, evaluation, purchase)

  • Level of email engagement (e.g., recent opens and engagement versus no opens over a period of time)

For example, because “Joe” expressed interest in buying a pair of jeans online, he could fall into the denim product category. As a result, he’ll receive content around jeans as opposed to other clothing items.

The power of personalization

An email campaign might go out to hundreds, if not thousands, of people, but thanks to personalization, it can strike the recipient as unique and designed just for them.

Email marketers can communicate with the audience on a human level, based on relevant data like purchase history, email open rates, or website visits. The level of personalization depends on how in-depth a marketer wants to be with a campaign. 

So how do marketers leverage the power of personalization and segmentation together?

Personalization and segmentation together

Machine learning is one of the most effective tools in digital marketing. It acts as a data analysis tool, allowing computers to analyze, predict, and act without explicit instructions. It works well with segmentation because it enables you to base communication on consumer behavior. The content attracts engagement and clicks, rather than what was labeled as their interest from some fixed point in time. 

To drive customer-centric marketing and sales, modern companies combine their segmentation strategy with machine learning. Using this approach, you can: 

  1. Measure segmentation activities against personalization experiments. This will confirm your machine learning-based personalization solution is outperforming your segments.

  2. Identify a starting point for communicating with customers, which machine-learning-based personalization can then enhance.

  3. Make sense of what you see in the data.

Segmentation is a vital piece of a targeting strategy, but on its own, it’s not robust enough to achieve a truly customer-centric experience. Combining your segmentation with machine-learning-based personalization helps you achieve higher engagement by sending the most relevant message to each customer based on their behavior, not a static label. And by creating email campaigns for individuals, they’ll hopefully buy more products from you over time. 


For more information about email segmentation and personalization, check out the following blogs:

  • Balancing Time and Effort for Personalization

  • Oracle Responsys Campaign Management

Or, to learn more about the solutions necessary to succeed as a marketer in today’s landscape, check out Oracle Marketing.