|A Starbucks Coffee shop in Hong Kong. / Photo by: Goarorelooam via Wikimedia Commons|
With 25,000 stores worldwide generating as many as 90 million transactions weekly, it is no wonder that that Starbucks generates a big amount of data. That amount of data increased further when the coffee chain rolled out its rewards program and mobile app, which enabled the company to know a lot about their customers’ spending habits.
The mobile app is now being used by 17 million users while the rewards program has 13 million active members. Such a large membership generates a huge amount of data that show what, where, and when their customers will buy coffee. That massive data can be paired with other data on weather, holidays, and special promotions.
Some of the ways that Starbucks uses the data that it collects from its customers have been expounded by Bernard Marr, writing for Forbes magazine.
Personalizing the Starbucks Experience
The coffee retail giant is authorized by the members of the rewards program and the mobile app to collect data from their preferred drinks to what time of the day they make their orders. Even if these people purchase from a store that they have never been to before, the store’s point-of-sale system can identify these customers through their smartphones and their preferred orders.
Targeted and Personalized Marketing
Starbucks also relies on that enormous amount of data to send personalized offers and discounts that are different from the usual birthday discount. It will also send customized e-mail to customers who have not visited Starbucks recently with enticing promotions from their purchase history to re-engage them.
The My Starbucks Barista feature on the Starbucks mobile app allows customers to place an order through voice commands or messages to a virtual barista that uses artificial intelligence algorithms.
Determine New Store Locations
Starbucks uses Atlas, a mapping and business intelligence app developed by Esri to determine where new stores should be located. Atlas evaluates immense amounts of data, such as proximity to other Starbucks locations, demographics, and traffic patterns when recommending where a new store should be located.