The Role Of AI In Closing The Personalization Gap In Retail Marketing

By Wes Hunt, Alliance Data

With consumer expectations constantly being re-shaped by new developments in the market, brand marketers today face the formidable challenge of trying to hit a moving target.

How well they are accomplishing that is the subject of a new report, “The Great Divide: Connecting brands to the real needs of today’s consumer,” based on research from Alliance Data’s Analytics & Insights Institute.

Using a combination of quantitative and qualitative research across more than 2,000 consumers and 40 brands, the research identified 31 consumer needs, measured how brands and consumers prioritize those needs, and asked consumers to rate how well those needs are being met.

One of the issues the research identified is the difficulty brand marketers face in prioritizing consumer needs. Marketers gave the 31 needs studied an average importance score of 92 percent—everything is equally important.

Consumers, on the other hand, gave average importance scores ranging from 28 percent to 88 percent. They know what’s important to them, but it isn’t always what brand marketers think is important.

Getting Personal

This disparity was particularly acute in the area of personalization. Brands valued many personalization needs much higher than consumers, who tended to see efforts—such as delivering recommendations based on past browsing history—as irrelevant.

This doesn’t mean consumers don’t value personalization. They absolutely do. What they don’t appreciate is tactics that create the appearance of being tailored without actually feeling personal.

This indicates the “divide” in personalization may be as much about definition as execution.

Brand marketers are operating on an internal definition of personalization and consumers aren’t buying it. They want personalization based on an understanding of their specific needs at a given moment in time.

The Role Of Artificial Intelligence

Meeting these high expectations for personalization isn’t easy, but it’s worth the effort. Experiences that meet a consumer’s needs in a particular moment of truth do more to build brand loyalty than almost anything else.

The first step is a shift in mindset from a product- or brand-centric definition of personalization to a customer-centric one.

Marketers today, more than ever, are justifiably evaluated based on their ability to drive sales. But when order volumes and sales become the driving metrics for connecting with consumers, it’s easy to lose sight of what is truly important to the consumer and to miss opportunities to build loyalty.

When this shift occurs, it will create a new lens through which the organization views its data. This is where AI becomes essential: It is the only way to achieve true personalization at scale.

This, too, may require a shift in thinking. Many retailers today are using AI primarily to achieve greater operational efficiency in the supply chain. It is certainly valuable in that regard, but when integrated into what are commonly called modern marketing platforms, it provides the insights required to deliver loyalty-building benefits to customers and to measure how well personalization strategies are working.

Of course, AI is only as good as the data that fuels it, so the customer data set will likely need to be expanded beyond transactional data to gain a more holistic view of behavior. This requires bringing data together across different channels as well as supplementing internal data with behavioral data from outside sources.

From a marketer’s perspective, AI-powered modern marketing platforms enable moving from “personalization” based simply on demographic or purchase history, to marketing that is truly personalized in the way consumers define it. Imagine moving from simple A-B testing to being able to deliver hundreds of variations on a communication based on deep customer insights—and then being able to efficiently measure the effectiveness of each.

And the power of AI can run even deeper. When AI-enabled insights are made available when and where the customer interaction is taking place, brands can more consistently meet the needs of consumers in the moment, creating experiences that build long-term loyalty.

Confirm Before You Challenge

As you move forward with your AI journey, expect the typical challenges that come with technology adoption: initial excitement based on the capabilities of the platform, followed by some frustration at harnessing those capabilities.

Early in this process, it’s generally not a good idea to try and use AI to disprove deeply held organizational beliefs. Instead, focus initial efforts on using data to confirm strongly held intuitions that drive key decisions. Often, you’ll find those intuitions are, in fact, supported by data, giving the new platform increased credibility.

Eventually, as your organization becomes comfortable with, and ultimately dependent on, the insights a modern marketing platform can deliver, you’ll settle into a level of consistent productivity.

The divide between consumer expectations and a brand’s ability to deliver on those expectations may be widest in the area of personalization. Modern marketing platforms that leverage AI and machine learning against a holistic view of consumer behavior are the only way to meet consumer expectations for personalization at scale.

About The Author

Wes Hunt is Vice President, Enterprise Data Science and Analytics, at Alliance Data.