Technology has finally advanced to the point where marketers can use real-time data in a way that is both meaningful to customers and profitable for companies.
We’ve come a long way from “People who bought this, also bought that.”
Consider the experience of a representative customer we’ll call Jane. An affluent, married mom and homeowner, Jane shops at a national clothing retailer online, in the store, and occasionally via the app. When visiting the retailer’s website in search of yoga pants, she finds style choices based on previous purchases, the purchases of customers with profiles similar to hers, and the styles of yoga pants most frequently purchased on weekends. She adds one of the offered yoga pants to her shopping cart and checks out.
With the exception of a follow-up email, most interactions with the customer stop there. But here’s what this example looks like when we activate Jane’s data: Three days after her online purchase, the retailer sends Jane a health-themed email. Intrigued, she clicks the link and watches a video about raising healthy kids. One week later, she receives an iPhone message nudging her to use the store’s mobile app to unlock a 15 percent one-day discount on workout equipment. Though she has never bought such items at this retailer, Jane takes advantage of the offer and purchases a new sports bag. What began as a simple task of buying yoga pants ended up being a much more engaged experience.
Such data-activated marketing based on a person’s real-time needs, interests, and behaviors represents an important part of the new horizon of growth. It can boost total sales by 15 to 20 percent, and digital sales even more while significantly improving the ROI on marketing spend across marketing channels: from websites and mobile apps to—in the not-too-distant future—VR headsets and connected cars.
Companies regularly experiment with testing the impact of varied customer experiences, but they do it in isolation. When they do try to scale, they smack against the challenge of understanding what to prioritize. Going back to Jane, do marketers target her as a mom, a yoga enthusiast, or a homeowner? What happens when tests are running against all three segments? Is she part of a new microsegment that combines attributes and signals across all three segments?
This is a challenge that has continued to plague marketers, despite the promise of solutions such as customer-relationship management (CRM), master-data management (MDM), and marketing-resource management (MRM). These solutions can help companies consolidate and streamline data, manage segmentation, organize workflow, and improve customer relationships. But they don’t take full advantage of digital signals customers provide. Instead, relying on antiquated “list pulls,” basic segmentation, and campaigns, all lack the automated decision making, adaptive modeling, and nimble data utilization to scale personalized interactions.
Enter the Customer Data Platform (CDP)—a data discovery and “decisioning” (i.e. automated decision making) platform. The CDP makes it possible for marketers to scale data-driven customer interactions in real time. And while CDP hasn’t really broken into the Gartner Magic Quadrant or Forrester Wave, it is gradually becoming an industry-standard concept, with a small but growing cadre of third-party platforms emerging that will soon shape the category.
Incorporating a CDP into your organization—whether piggybacking on an existing master data-management or customer-relationship-management system or starting from scratch—requires mastery of four areas (exhibit):