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AI has infiltrated nearly every customer-related business process, including customer service, price optimization and segmented marketing campaigns. At its core, machine learning in digital marketing has the ability to automate and personalize traditional customer outreach. With the ability to garner insights through data and test marketing strategies, machine learning can dig deeper into customer intentions and preferences to create a robust consumer portfolio.
Replacing generic mass marketing with tailored, personalized marketing means that companies must source data and create layered algorithms to test different strategies quickly -- but a lack of high-quality data and a high cost of scalability are stalling development across enterprises.
Ditch mass marketing
Machine learning in digital marketing scales the number of users that can be reached and allows for content hyperpersonalization. E-commerce's success with content personalization and suggestions proved that consumers want a custom, tailored experience online.
Machine learning in marketing can offer enterprises the ability to move away from generic mass marketing and create a customer-centric paradigm, said Christian Selchau-Hansen, CEO of AI-based marketing company Formation.
Christian Selchau-HansenCEO, Formation
"With machine learning, marketers can better understand what motivates their consumers -- their needs, personalities, quirks -- and then determine how best to meet those motivations. The technology enables new marketing strategies by analyzing customer data."
However, personalization can mean a host of different things depending on the company and the user. In one study on user engagement by Iterable, a company that builds cross-channel marketing strategies, researchers found that most content media rarely included a user's name in material.
"In 1,700 test emails, I didn't see my name once. That's a base level of personalization that companies can do, and many didn't," said Michael Huard, author of the report and content marketing manager at Iterable.
The strongest marketing departments rely on a robust set of analytics and key performance indicators (KPIs) to measure their progress towards revenue and customer growth goals. One example of this is to track how machine learning for marketing impacts sales growth or total revenue. Tracking machine learning's influence on KPIs are difficult in content-based industries, like news media, Huard said.
Most industries that produce content rely on newsletters and automated weekly messages to reach a larger customer base. While newsletters and emails are some of the most widely used methods of business to customer communication, without personalization, they fail to promote engagement.
Tailoring content to each user doesn't need to mean a company-specific recommendation engine or complex research -- it can be as simple as including mobile formatting. Consumers are accessing the internet primarily through mobile phones, and marketing strategies need to take this into account by making content easy to read on mobile, adding text message integration and personalizing across multiple content channels, Huard said.
Huard noted that another positive strategy that machine learning for marketing can help most companies with is staggering communication based on location. Companies based on the east coast of the United States sending out morning roundups at 7 a.m. NYC time will reach Silicon Valley at 4 a.m. -- far too early for engagement. Local newspapers or those with staggered timelines will eventually bury the newsletter or news roundup that was sent without timelines in mind. Instead, companies sending out marketing or newsletter emails should make sure they go out to people at times when they're likely to open them.
Martech vs. digital native
Many strategies for machine learning in digital marketing are internal -- companies applying new technology to existing customer data in order to improve marketing strategies. These companies, however, have to be data-rich and digitally native -- think Amazon or Netflix. Machine learning is mostly powering recommendation engines and personalized content suggestions – with the aim of increasing relevance and improving the user experience. In these cases, digital marketing is built into the very core of the business.
Machine learning in marketing is still growing because of limited use cases and inability to perform at scale, Selchau-Hansen said. Some companies don't yet have a digital marketing strategy -- and integrating machine learning into an outdated marketing strategy is difficult. The initial cost of creating algorithms to find and develop customer insights is moderate, but then scaling it for thousands of individual customers causes the price and manual labor required to skyrocket. This can be particularly difficult for smaller companies with limited data science teams.
"The challenge is current technology and process are built for segmentation and they aren't built to take action on the insight in a scalable way," Hansen said.