Have you ever wondered how it can be economically viable for there to be multiple Starbucks coffee shops clustered in very close proximity to each other? Unbeknown to many, Starbucks has invested significantly in big data and analytics capabilities in order to determine the potential success of its stores and products, and grow sales. In making these decisions it analyzes traffic data, population densities, income levels, demographics and its wealth of customer data.
Where it all started: the concentration of Starbucks stores in downtown Seattle
So where does Starbucks get all of this data? For geospatial, demographic, traffic and location data, Starbucks get data from its in-house Atlas tool which was built by Integral GIS and Esri. For customer data, Starbucks has it’s Starbucks Rewards loyalty program, which had 12.9 million members at the end of Q1 FY17 (up 16% YoY) and can be accessed by physical card or mobile app. Besides redeeming loyalty points for free food and drinks, the mobile app also enables members to place orders ahead of time and pay using their mobile phone.
Through Starbucks Rewards, Starbucks is able to collect a mountain of data about its customers such as their favorite drinks, beverage customizations, visit frequency, the stores they visit, the time of day etc. Beyond store locations, Starbucks has been able to combine its data from Atlas and its loyalty program to push targeted promotions directly to customers’ mobile devices, improve the customer experience, tailor store offerings and design new products. Some examples include:
- making alcohol available at stores in areas of high alcohol consumption under a special menu called Starbucks Evenings (in early 2017 Starbucks announced they would be integrating this into their premium “Roastery” stores);
- pushing local promotions for Frappucinos in Memphis ahead of heat waves, essentially predicting (or “incepting”) what a consumer will want before they even know it themselves;
- launching new unsweetened tea K-cup and bottled products in grocery stores based on in-store data that 43% of tea drinking customers don’t add sugar; and
- announcing a new express store design that eliminates both the cashier counter and seating.
In the words of Starbucks CTO Gerri Martin-Flickinger, “With about 90 million transactions per week we know a lot about what people are buying, where they’re buying, and how they’re buying. If we combine that information with other data like weather, promotions, inventory, insights into local events, we can actually provide better, personalized service for customers.” This ultimately creates a positive feedback loop. Starbucks uses data to improve its product offerings and customer experience, leading to increased sales from both existing and new customers, as a result more customers sign up to become Starbucks Rewards memberships, Starbucks captures more data from its large user base and the cycle repeats itself.
With mobile payments now making up over 16% of Starbucks’ US transactions, a record annual revenue in 2016 of $21.3B and same-store-sales having grown have 5-7% YoY over the past five years, clearly Starbucks’ digital and data strategy is paying off. And so, whether you’re a Starbucks coffee fanatic or you think its products overpriced and nothing special, (as an Australian I admittedly fall in the latter category) you can’t argue with the results.