When we first met with Dunkin’ Donuts, the brands was struggling with the idea of matching known customer data sets (like e-mail) with unknown, anonymous user data (like cookies). The Dunkin’ team was tasked with a long-term project of supercharging its approach to personalized marketing.
Just like Keurig Green Mountain, Dunkin’ was in a fierce battle for the hearts, minds, and wallets of the American coffee drinks. Its competition ranged from premium coffee chains such as Starbucks to quick-service restaurants, such as McDonald’s, which was stepping up its coffee game.
Dunkin’ coffee shops were places that people could depend on for a highly consistent, fast breakfast or afternoon coffee break. That customer archetype still rings true today: there are ‘Starbucks people” and “Dunkin’ people,” and not many in between. Although much of this sentiment is based on regional distribution of the chains, there is no doubt that there are core differences in brand adopters. Dunkin was confronting the paradox of our Data Driven Principle #2: the company had a ton of consumer data, but not as much as it needed to get the job done. With rich access to stores of mobile and e-mail data, how could Dunkin move from a CRM-first data strategy and amplify what it knew about consumers to deliver consistent, fast experiences at massive scale?
Was it possible to use its CRM data as a force multiplier to support a broader expansion strategy for customer engagement?
Since 2006, Dunkin’s digital team had been re-architecting brand experiences to keep up with fast-moving consumer trends, and a large part of the initiative centered on creating great customer experiences. Dunkin’ launched an effort to build a first-party relationship with its customers, including the introduction of a new loyalty program, DD Perks.
When we met the Dunkin’ team, DD Perks had millions of active users, and the company was generating results from its mobile loyalty strategy. Customers who ordered via mobile app could avoid the lines by paying directly through their phones. They could earn free coffee frequently and get special discounts on menu items. Dunkin’ connected the app with its in-store point-of-sale systems and tied mobile app usage and in-store sales together. It combines these data sets to get a better understanding of attribution and to build deep profiles of its customers.
Now, Dunkin’ could present customized menus to its customers based on their frequent choices, and the company could better understand the differences and buying habits between coffee and tea drinkers, and test messages for new menu items.
This CRM and purchase data asset formed the foundation for Dunkin’s segmentation strategy for anonymous web users: real customer data was telling the company the difference between frozen Coolata drinkers and classic coffee fans, between early-morning grab-and-go commuters and late-morning in-store coffee drinkers.
Connecting these real-life customer profiles with the anonymous world of the Internet would help Dunkin’ grow and scale beyond its 5 million known app users, enabling it to intelligently reach tens of millions of new customers.