For the better part of a decade, retail has looked to “personalization” as its primary marketing strategy in an effort to combat a rapidly deteriorating landscape. It’s time to admit it’s not working. True personalization, a 1:1 marketing experience between the brand and the consumer, is incredibly difficult, if not impossible with the current tools at a marketing team’s disposal. In fact, it would probably take upward of 10,000 marketing managers to pull such a strategy off. Artificial intelligence and machine learning, on the other hand, could handle it in mere minutes.

With a rising number of store closures this year, already up to at least 3,000, retailers can’t deny they’re struggling to find a strategy that works across the board to bring shoppers back into the fold and generate the loyalty they desperately need. AI, particularly as a marketing function, has the transformative power to cure some of these ills, but as it stands, only major companies like Amazon or Google are harnessing the technology in a way that impacts the bottom line. The challenge many brands face is how to catch up or even begin to compete.

AI and machine learning are still intimidating concepts for retailers who aren’t sold on technology as their savior. It’s understandable, the technology is still in its infancy with industries across the board unsettled by its potentially all-encompassing reach across an organization. However, the premise behind machine learning is simple, and much like humans, it learns from experience. The benefits are worth the process of figuring out how to make it work.

When marketers talk about personalization, currently, they’re mostly talking about personalization through segmentation — repurposing and repackaging the same message for anywhere from 10 to 50 specific audiences. They look at the entirety of the information available to them about a shopper or group of shoppers, make an analysis and create a message that would appeal to the user or move them along in their purchasing journey. Then, the marketer judges the users reaction — did they click through, did they make a purchase — and determines the message’s effectiveness.

Through machine learning, AI can act similarly and apply its experience to each individual shopper’s profile, browsing history, in-store visits, abandoned carts, purchase and returns — all to determine the perfect marketing message at the perfect time on the perfect channel. However, what would take a human at least 20 minutes to do for one shopper, AI can do it for 10,000 users per second.

Ultimately, retailers that realize betting on AI over tens of thousands of marketers is the smart strategy will also realize the role of the marketing team is destined to change. Retailers are attempting 1:1 personalization with the tools they currently have, including: CRM, loyalty programs, e-mail marketing and social media targeting. In the future, that technology will become wrapped up in the machine learning process and the marketer’s job has to evolve to directly engage with artificial intelligence. The job will evolve from analyzing a shopping behavior of groups or segments to coaching the AI to meet the company’s strategic objectives.

Marketing companies are getting closer to making this reality a nearer and nearer future. As it stands, the AI landscape is incredibly fragmented with young start-ups displaying elements of the new technology that are almost ready for the big time and large established loyalty players beginning to experiment. Not to mention, there are sociological and technological barriers to using this technology that are being worked out across all industries.

On the technology front, cmo’s are hesitant to take risks on creating an AI layer between them and the consumers and allowing for the deep integration with many different systems needed for success. Nothing would be more embarrassing than sending the perfect message about “product X” to a shopper only to find out it was so perfect the customer already purchased it and AI didn’t know because the in-store systems aren’t integrated with the e-commerce system. That is not an effective use of AI and a wasted opportunity to engage a consumer. The marketing team will never be obsolete, it will just change so that they’re the ones to make sure the AI is never at risk of becoming “stupid.”

Companies considering taking the leap into AI should evaluate via a few key criteria. It’s not easy and there is a reason data scientists are in high demand across all industries. Inbound data must be solid with a clear 360 view of the consumer across all channels. The easiest way to check is to look at the consumers in loyalty programs and see if the full data set is there — in-store behavior, online, app. Marketing automation tools also need to be in place. CRM systems are increasingly taking positive steps to tackle this, but any retailer considering AI for the marketing team needs to ensure there are programs to create, schedule and send marketing communications across all major channels. Basically, the inbound and the outbound pieces have to be in place, so artificial intelligence can run between them on its never-ending fact-finding and learning mission.

The truth is, no one in retail can afford to hire a 10,000-strong marketing team and artificial intelligence is becoming a less scary alternative nearly every day. AI is here to learn from the marketing team, accelerate their reach and ultimately bring customers back into stores, onto apps and boost loyalty across the board.

Alex Muller is cofounder of GPShopper.

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