Every day, Starbucks grinds through mounds of coffee beans to serve its customers. In doing so, the company has also been collecting mounds of data that could be used to improve customer experience and business performance. In this article, we will investigate how this 26 Billion USD company captures value through data analytics.
An opportunity brewing
Through its network of over 30,000 stores worldwide, Starbucks has been gathering over 100 million transactions a week. To make use of these data, the company set up a dedicated team of data scientists, led by Jon Francis, Starbucks’ Senior Vice President of enterprise analytics, data science, research data, and analytics. Since then, the team and the company have been able to tap into the power of data analytics to enhance its business performance.
Using data collected at the store and through its mobile apps with over 17M members, the company has been able to drive business improvements in 3 significant ways.
(1) Personalized experiences and promotions
When Starbucks launched its rewards program and mobile app, they dramatically increased the data they collected and could use to get to know their customers and extract info about purchasing habits. Through its mobile app, Starbucks has been collecting data about what, where, and when members buy coffee. To do so, Starbucks leverages the Digital flywheel program, a cloud-based artificial intelligence engine that’s able to recommend food and drink items in a precise manner. As such, even when people visit a new Starbucks location, the store’s point-of-sale system can identify the customer through their phone and give the barista their preferred order.
Based on customers’ purchase history, Starbucks could also suggest new products a consumer might enjoy and provides unique discounts and rewards on certain items based on customers’ unique preferences. Taking it a step further, Starbucks has also been collecting data on weather patterns and their relationship with customer order patterns. Doing so allows the company to provide even more personalized experiences and promotions such as targeting a customer with cold drinks on hot days.
(2) New product introduction
When launching new products, Starbucks also turned to the data collected to determine what products they should offer. Specifically, when expanding its product lines into grocery stores, the company relies heavily on the data collected. For example, data collected has shown that 43 percent of tea-drinking customers tend to skip the sugar. To cater to this segment, Starbucks created its lines of unsweetened ice tea. When data has shown that 25 percent of consumers don’t add milk to their coffee, the company launched a new line of black iced coffee without milk.
(3) Location selection
Selecting the right location is critical to winning in retail. Using location-based analytics powered by Atlas, a mapping and business intelligence tool developed by Esri, the company can select the most strategic location to open up its new stores. The tool enables Starbucks to evaluate massive amounts of data including variables such as population, income levels, traffic, competitor presence, and proximity to other Starbucks locations before recommending a new store location. Using these data, the company can also predict revenues, profits, and other aspects of economic performance associated with that location.
While Starbucks was not born in the digital era, it has successfully integrated new technologies into its core business like a digitally native company. Over the years, data analytics has undeniably become the backbone of Starbucks’s continuous improvement. Looking ahead, I have no doubt that Starbucks will continue to gather more data and make even more innovative use of those data to create an even more personalized customer experience and achieve business excellence.
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“Starbucks knows how you like your coffee,”CNBC, Apr 6, 2016