Starbucks is a leading roaster, marketer, and retailer of coffee in the US and globally. The company’s more than 27,000 owned and licensed stores sell a variety of coffee, food, and other beverages (e.g., tea) to consumers around the world .
Starbucks launched its mobile ordering and payments application in 2014, raising the bar for using technology in the restaurant and retail space . 5 years later, the Starbucks App has become a competitive advantage for Starbucks, allowing it to tap into the increasingly important area of machine learning to drive sales and customer loyalty in the US and around the world. As the company looks to China as the core of its growth strategy for the foreseeable future , it will become only more important to leverage machine learning technology to stay ahead of the competition in many core functions such as customer engagement, product innovation, real estate strategy, and human resource management.
Machine Learning at Starbucks
Starbucks’s machine learning strategy today is enabled by the vast amounts of customer purchasing data collected through the mobile ordering and payment application over the past 5 years. Today, more than 50% of customer purchases are either ordered or paid for through the mobile application . Each time a customer makes a purchase using the mobile application, Starbucks records valuable data about the consumer’s food and beverage preferences, average order size, purchase location, and time of day. By stitching together this data with other data such as weather data and store-level product availability, Starbucks is able to offer each customer personalized offers and recommendations, as well as “games” to receive bonus rewards (e.g., buy 2 Frappuccinos over the next 3 days) .
Starbucks’s current approach to machine learning appears to be successful in part because it meets the three requirements of an effective machine learning implementation :
- Right problem – Starbucks is using the data and algorithms to predict which offers will delight customers, with no view towards trying to draw causal inferences from the relationships
- Right data – due to the success of the mobile Order and Pay application, the company has access to a wealth of relevant, proprietary data about past consumer behavior which can be used to predict preferences and future behavior
- High tolerance for error – unlike in other industries (e.g., medical, oil & gas) where the cost of error is very high, the cost associated with the algorithm making an incorrect or irrelevant recommendation is low; the consumer may just ignore that particular offer
The future is Chinese
As Starbucks turns its focus to China as the number 1 driver of growth in the foreseeable future, the company can continue to leverage machine learning to gain a competitive edge. However, the company has to be very careful in how it implements its US machine learning strategy in a new country with totally different preferences, behaviors, and culture. Mike Yeomans, a post-doctoral fellow at Harvard University, cautions about using existing machine learning algorithms for situations that are “out-of-context” . When translating the algorithm from the US to China, Starbucks may need to consider not only regularization (i.e., the relative weight of different variables) but also reevaluate the feature extraction phase of algorithm design (i.e., which variables, or “features,” the model will use in the first place).
In addition to using machine learning for customer engagement, the company can consider using the technology in other areas as it expands its presence in China:
- Product innovation – use the company’s rich transactions data to predict and test the success of new food and beverage products. For example, if the algorithm finds Starbucks consumers like cheese, the algorithm can present various innovative cheese-related food and drink products to the new product development team to design and test in certain markets.
- Store location – use data and analytics to predict the success of potential store locations, as the company continues to build out its retail footprint. For instance, if the average wait time for customers in a cluster of stores in Beijing is high, the company could consider building an additional Starbucks store close by.
- Hiring partners – use algorithms to predict success of potential candidates in the hiring process, as the company attempts to massively scale its workforce in China. The company could choose to develop the capability in-house or partner with third party providers such as Fountain  or OnboardIQ .
Please weigh in on the following questions as you consider Starbucks’s use of machine learning:
- What will be the most significant barrier to Starbucks’s China strategy, and how can they leverage machine learning to address it?
- What other ways can Starbucks use machine learning to impact parts of its business?
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[Featured Image] Starbucks Corporation. “kv-1,” 2018. [Online]. https://www.starbucks.com.cn/en/menu/. [Accessed: 12-Nov-2018]