Is Now the Time to Transition to Personalized Marketing?
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Marketing Jul 2, 2020

Is Now the Time to Transition to Personalized Marketing?

Budgets are strapped. There are a million other things to do. But the risk of ignoring AI-powered modern marketing is dire.

Leader using AI for personalized marketing.

Lisa Röper

Based on insights from

Jim Lecinski

Editor’s note: This is part of a series of articles based on Kellogg Executive Education webinars focused on COVID-19.

Months into the COVID-19 crisis, businesses are finally starting to come up for air. And as they survey a radically altered business landscape, many are realizing that their own business will need to adjust to meet the new conditions.

Each Thursday, Kellogg faculty are offering free webinars on how COVID-19 is impacting businesses, markets, and careers. You can sign up for upcoming sessions, hosted by Kellogg Executive Education, here.

“As leaders, our minds necessarily turn to starting to think about, ‘What do we do next? Where do we go from here? Not just survival, but perhaps, what [growth and transformation] opportunities are out there?’” says Jim Lecinski, a clinical associate professor of marketing at Kellogg, and also at Northwestern’s Medill School

In a recent webinar from Kellogg Executive Education, Lecinski discussed how leaders can use the current moment to revamp their marketing strategy to make personalized marketing a focus.

Personalized marketing involves understanding who your customers are as people and, in real time, delivering them an experience that is relevant to them at that moment. While personalization continues the trend of increased segmentation that marketers have been doing for years, it takes the idea further. So, instead of segmenting customers into, say, a dozen or a hundred segments, a company with 20 million customers can now create 20 million segmented messages.

That specificity requires some special tools—including sufficient data, plus AI and machine learning—to pull off.

“What’s different is the use and implementation of technology that allows us to cross this chasm and go from a hundred segments to 20 million segments,” says Lecinski. “Delivering this deeply personalized experience requires a significant shift for companies.”

It’s a shift that only about half of marketers have actually made. Why? Many write off personalization as hype or a fad—an idea with which he vehemently disagrees.

Many other marketers simply don’t know where to start. So Lecinski lays out a five-step roadmap for companies looking to make the transition. He calls it the AI Marketing Canvas.

First, companies need to gather enough quality customer data to train machine-learning models. There are some clever ways to do this: for instance, in Thailand, Unilever’s Knorr brand created cute emojis for customers to use on a popular social-messaging app in exchange for sharing information about themselves and their favorite Knorr products.

Second, companies should seek out quick wins at individual moments in the customer journey. This often means plugging the data you’ve collected into third-party tools and using them to make predictions about customer behavior. For instance, JPMorgan Chase Bank worked with an AI partner to serve up personalized display ads to its customers—and found that they increased click-through rates more than ads created by actual human marketers.

Next, companies should expand their efforts by beginning to develop an in-house AI competency. For instance, Coca-Cola began manufacturing its own “Freestyle” vending machines, which let customers create their own blend of soda flavors via an app and share their “recipe” with others and receive personalized messages and offers.

Then, it is time to bet big on machine learning. By now, marketers should have enough success under their belts to convince their executive teams to invest heavily in AI as a new way of doing business. Starbucks recently made the leap with its in-house Deep Brew platform, AI that allows it to personalize the customer experience while also freeing up employees.

In its final state of maturity, marketing AI doesn’t just solve a critical business problem—it becomes its own full-fledged revenue stream. For instance, The Washington Post’s automated storytelling engine, Heliograf, is now licensed to other newspapers and publishers.

Developing the AI necessary to transform your marketing may sound daunting, particularly at a time when resources are tough to come by. But the costs of not investing could be far worse, says Lecinski.

Rather than thinking about AI’s ROI in the more traditional sense of “return on investment,” he says, consider the “risk of ignoring” it and doing nothing. It may well be that the world moves on during this transformational COVID moment and your brand will no longer be able to compete against AI-powered modern marketing.

You can watch the full webinar here and read other articles in the series here.

Featured Faculty

Clincal Associate Professor of Marketing

About the Writer
Jessica Love is editor in chief of Kellogg Insight.
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