MarketingCharts staff
Through a special arrangement, presented here for discussion is a summary of articles from MarketingCharts, which provides up-to-the-minute data and research to marketers.
Virtually all marketers (96 percent) are confident that machine learning can personalize e-mail content down to an individual’s specific interests and improve the customer experience, according to a survey of more than 400 U.S. marketers from The Relevancy Group, sponsored by OneSpot.
Beyond personalizing e-mail content, the application of machine learning can eliminate some manual tasks that occupy a significant portion of marketers’ time, per the report. Those executional tasks include content selection, HTML coding and proof testing of messages, which marketers say collectively take up almost one-quarter of their work week.
Senders at enterprise companies and those at retail/e-commerce companies appear to have the most to gain, according to the study.
Were they able to free up time by introducing machine learning, seven in 10 respondents say they would use that saved time for program planning, expansion and strategy, about half would put more focus on data analysis, and more than four in 10 on subject line optimization.
But the use of machine learning in e-mail marketing doesn’t come without some challenges. Seven in 10 are concerned about the implementation and training involved, and the same ratio are concerned about switching or adding a platform to their marketing tech stack.
These concerns seem well-founded, considering that marketers who have already adopted machine learning in their e-mail programs spent four months on average on implementation.
Meanwhile, two-thirds are concerned about giving up some editorial control to the technology. That’s a shared concern held by many different constituents when it comes to artificial intelligence: a recent survey from The Integer Group found that only one-fifth of shoppers said they would be comfortable letting AI proactively order items they haven’t previously purchased but might like.
Nonetheless, The Relevancy Group believes that concerns surrounding machine learning in e-mail — including trusting its effectiveness, an issue for four in 10 marketers — will fade as marketers gain more comfort and data from using the technologies.
But the use of machine learning in e-mail marketing doesn’t come without some challenges. Seven in 10 are concerned about the implementation and training involved, and the same ratio are concerned about switching or adding a platform to their marketing tech stack.
These concerns seem well-founded, considering that marketers who have already adopted machine learning in their e-mail programs spent four months on average on implementation.
Meanwhile, two-thirds are concerned about giving up some editorial control to the technology. That’s a shared concern held by many different constituents when it comes to artificial intelligence: a recent survey from The Integer Group found that only one-fifth of shoppers said they would be comfortable letting AI proactively order items they haven’t previously purchased but might like.
Nonetheless, The Relevancy Group believes that concerns surrounding machine learning in e-mail — including trusting its effectiveness, an issue for four in 10 marketers — will fade as marketers gain more comfort and data from using the technologies.