What Marketers Need to Know About AI-Based Segmentation
By Ericka Podesta McCoy | 4.8.20
The days of mass marketing as we once knew it are coming to a close, and the personalized, one-to-one connections that marketers have long craved are becoming a reality. For the most part, we have the recent dramatic advances in segmentation to thank for this monumental shift in marketing approach. And underpinning these improvements are the latest advances in artificial intelligence (AI).
Traditional segmentation using broad-based filters such as geography and demographics has given way to deeper, dynamic segmentation powered by AI. These days, the most effective segmentation strategies are dynamic and intelligent—and include micro-segments that go well beyond generic attributes, light insights, and other flat data to incorporate deep, person-level insights that expose why people buy certain products, shop at particular retailers, or abandon a brand.
That said, many organizations have yet to pivot their segmentation strategies to take advantage of new capabilities in the market. Let’s look at how approaches have transformed over the past few years and how AI-based segmentation can accelerate not only an organization’s marketing, but also its approaches to product development and customer experience.
How modern is your segmentation strategy?
Segmentation capabilities have evolved significantly in the past three years alone. Back in those long-ago days preceding 2016, segmentation strategies tended to be the product of lengthy studies. These strategies were updated biannually (at best) and spent most of their time sitting in a binder on the CMO’s desk.
Between 2016 and about a year ago, digital technology advances prompted improvements in segmentation. The segmentation strategies of this era tended to be more recent (though never fully up to date), were referred to regularly in marketing meetings, and used to a moderate extent—but typically only at the demographic level.
In the past year, however, AI has changed everything. These days, segmentation strategies can be updated frequently, are aspirationally actionable, are leveraged across the marketing organization, and are even beginning to inform functions beyond marketing. They go beyond static segmentation strategies that split consumers up demographically, geographically, and behaviorally to incorporate new deeper data points and insight layers that speak to a person’s motivations—not just what they do, but why they do it.
Of course, given the speed with which this capability has developed, plenty of organizations still find themselves back at the 2016 mark, working with basic demographic, generational, and geographic data, as well as uninspired behavioral segments, such as “Suburban Moms on the Move” and “Gen Z Social Addicts.” And while these customer views are important, they lack the depth needed to drive connections because they’re missing the “why” behind an individual’s everyday decision-making, which is essential in today’s hyper-competitive market.
How AI-based segmentation unlocks the “why”
When we talk about leveraging AI for segmentation purposes, we’re typically talking about two core applications. The first would be the use of natural language processing on text—such as social or blog data—to discern the values, emotions, and sentiments of a given customer and apply segmenting rules accordingly. The second application is machine learning that takes segments from smaller data sets and performs lookalike analysis to identify who, in a larger database of consumers, shares those attributes.
It is within this second application of AI that marketers and their teams can achieve true scale, even with their nuanced personas. Until recently, after all, the Achilles’ heel of personalization has been scale; once marketers sliced and diced their audiences according to multiple known attributes, their resulting audiences were too small to be actionable. With AI-based segmentation and audience projection, that’s no longer the case.
AI-based segmentation enables marketers to get more specific with their segmentation and, ultimately, target individuals as humans rather than mere cardboard cutouts. These days, marketers can zero-in on target segments in thousands of different ways. Starting with existing personas, they’re able to leverage AI-based segmentation to layer on (and build out audiences at scale) according to attributes including:
● Psychological drivers
● Personal values
● Media consumption
● Brand affinity
● Who values loyalty programs
● Their propensity to buy a given product in the next year
● Where they shop
● The amount of time they spend online and offline and where they spend it
● Their preferred method of civic expression
● Product attributes they value, including everything from apparel to household items
Get granular by using AI-powered data to build more robust micro-segments. This helps draw out important nuances that drive decision-making improvements across the organization and throughout the lifecycle. Developing more tailored products, services, and experiences, personalizing offers, and ensuring more compelling creative and messaging—all these things increase consumers’ likelihood to buy, spend more, and stay, ultimately, leading to brand love.
Think about it in terms of a relationship: The more you understand someone by learning about their interests and what they value, the more likely you are to establish a lasting connection with them. The same is true of the brand and consumer relationship. When brands present consumers with personalized value propositions, they connect, engage, convert, upsell, and retain them more effectively and more efficiently, driving growth.
AI-based segmentation provides multiple payoffs across the customer lifecycle, from acquisition to retention. When done correctly, this type of segmentation will drive your marketing strategy, guide your messaging and product development, and increase the overall success rates of your campaigns.
About the Author
Ericka Podesta McCoy is CMO at Resonate. A global marketing executive, Ericka is focused on enterprise growth and revenue management in the high-tech, telecom, manufacturing, energy, and hospitality sectors across North America, Europe and Asia. Her ability to envision and operationalize strategy is a hallmark of her leadership.