How machine learning can help email marketers

WHEN you think about how user expectations have changed, you realize the importance of personalizing your marketing messages. It’s something everyone is doing and has almost become a best practice in the industry.

However, it can be time-consuming – especially for organizations that have several different categories and groups to customize marketing messages for. Welcome to the age of machine learning.

According to the Email Individualization Imperative study, an overwhelming majority of email marketers said they were confident that machine learning could be used to personalize email content to an individual’s specific interests and improve customer experience.

“Machine learning — a powerful subset of AI — is advancing personalization beyond broad segmentation to turn every email subscriber into a segment of one. With machine learning, every person interacting with a brand should have their own unique content experience, a practice we call individualization,” said Damian Borichevsky, senior vice president of Customer Success and Business Development for OneSpot.

According to the study, here’s how machine learning can help email marketers and why businesses should pay attention to this up and coming trend:

Using machine learning helps marketers make more time for strategic action:
Among the study’s findings, most marketing teams spend more than 36 hours or the equivalent of that entire work week of time on manual email segmentation processes such as content selection and proofing, in an attempt to personalize content.

However, 70 percent of marketers say that if they used machine learning and eliminated manual processes, the time saved would be reallocated to program planning, expansion, and strategy.

More than half of marketers (51 percent) said they would instead allocate the time saved to data analysis, while subject line optimization (44 percent) and segment refinement and list cleansing (35 percent) are other activities to which marketers would like to spend time on if machine learning could help out with some of the more routine tasks.

Marketers serving the CPG and the retail industry benefit most:
To personalize email content, marketers in the Retail and in the Consumer Packaged Goods (CPG) industries spend a weekly average of 46 hours and 40 hours, according to the findings of the study.

For retailers, there is an opportunity to garner an even greater business return on individualizing content by using machine learning in transactional emails such as communications regarding purchase, cart abandonment, or returns.

CPG marketers could use machine learning to increase email frequency to build even deeper relationships with audiences through individualized content.

Marketers cautious of the efforts required to implement and train staff:
Implementation and training are key issues for marketers currently using machine learning for email personalization as well as those thinking about doing so.

For 44 percent of marketers, implementation time took an average of 3.5 months, while 25 percent of marketers cited that it took as much as 7.5 months to implement a personalization solution. Only 16 percent experienced implementation times of 45 days, and it took less than 30 days for 3.5 percent of marketers.

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