70% of Marketers Would Reallocate Over 36 Hours a Week to Strategic Initiatives If They Eliminated Manual Processes By Using Machine Learning

Apr 05, 2018


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An overwhelming majority of email marketers (97%) are confident that machine learning can be used to personalize email content to an individual’s specific interests and improve the customer experience, according to “The Email Individualization Imperative,” a new study released by market research firm The Relevancy Group in collaboration with OneSpot, a content individualization and intelligence platform. The survey of executive marketers examined their understanding of, sentiments towards and usage of machine learning for email personalization, across industry sectors. 

Among the study’s findings is that on a weekly basis, most marketers’ teams spend over 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. But 70% of marketers say 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%) would instead allocate the time saved to data analysis, while subject line optimization (44%) and segment refinement and list cleansing (35%) are other activities to which marketers would shift their time reserve.

CPG and Retail Marketers Have Even Greater Opportunity to Benefit from Machine Learning Time Savings

To personalize email content, marketers in the Retail and Consumer Products sectors are spending a weekly average of 46 hours and 40 hours, respectively. As such, for retailers, there is an opportunity to garner an even greater business return on individualizing content by using machine learning in transactional emails e.g. 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.

Despite Proven Personalization and Operational Benefits of Machine Learning, Implementation, Training, Switching, or Adding Platforms Among Top Concerns

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

                                                            

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