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 [1].<\/p>\n
Starbucks launched its mobile ordering and payments application in 2014, raising the bar for using technology in the restaurant and retail space [2]. 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 [3], 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.<\/p>\n
Machine Learning at Starbucks<\/strong><\/p>\n 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 [4]. 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) [2].<\/p>\n 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 [5]:<\/p>\n The future is Chinese<\/strong><\/p>\n 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 性视界 University, cautions about using existing machine learning algorithms for situations that are “out-of-context” [6]. 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).<\/p>\n 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:<\/p>\n Please weigh in on the following questions as you consider Starbucks’s use of machine learning:<\/p>\n <\/p>\n (781 words)<\/p>\n [1] Starbucks Corporation. “Starbucks Fiscal 2017 Annual Report,” 2017. [Online].\u00a0https:\/\/s22.q4cdn.com\/869488222\/files\/doc_financials\/annual\/2017\/01\/FY17-Starbucks-Form-10-K.pdf. [Accessed: 12-Nov-2018]<\/p>\n [2] Martin-Flickinger. “Starbucks Corporation 2017 Annual Meeting of Shareholders,” 2017. [Online].\u00a0https:\/\/seekingalpha.com\/article\/4057287-starbucks-corporations-sbux-ceo-howard-schultz-hosts-2017-annual-meeting-shareholders. [Accessed: 12-Nov-2018]<\/p>\n [3] Johnson. “FY17 Annual Letter to Shareholders,” 2017. [Online].\u00a0https:\/\/s22.q4cdn.com\/869488222\/files\/doc_financials\/annual\/2017\/01\/FY17-Annual-Letter-to-Shareholders.pdf. [Accessed: 12-Nov-2018]<\/p>\n [4] Brewer. “Starbucks (SBUX) Q4 2018 Results – Earnings Call Transcript,” 2018. [Online].\u00a0https:\/\/seekingalpha.com\/article\/4217413-starbucks-sbux-q4-2018-results-earnings-call-transcript. [Accessed: 12-Nov-2018]<\/p>\n [5]\u00a0Fedyk. “How to tell if machine learning can solve your business problem,” 2016.\u00a0性视界 Business Review Digital Articles<\/em><\/p>\n [6]\u00a0Yeomans. “What every manager should know about machine learning,” 2015. 性视界 Business Review Digital Articles<\/em><\/p>\n [7] PRWeb. “Fountain Breaks into Franchise Market with Free Hiring and Onboarding Service,” 2018. PRWeb.\u00a0<\/em>[Online].\u00a0https:\/\/www.prweb.com\/releases\/fountain_breaks_into_franchise_market_with_free_hiring_and_onboarding_service\/prweb15903234.htm. [Accessed: 12-Nov-2018]<\/p>\n [8] Lawler. “OnboardIQ raises $9.1 million to automate hiring for hourly workers,” 2017. Techcrunch<\/em>. [Online].\u00a0https:\/\/techcrunch.com\/2017\/07\/06\/onboardiq-9-1m\/. [Accessed: 12-Nov-2018]<\/p>\n [Featured Image] Starbucks Corporation. “kv-1,” 2018. [Online].\u00a0https:\/\/www.starbucks.com.cn\/en\/menu\/. [Accessed: 12-Nov-2018]<\/p>\n","protected":false},"excerpt":{"rendered":" Starbucks has been an innovator in big data and machine learning through its highly successful mobile order and pay application. How can that translate to China, its next leg of growth?<\/p>\n","protected":false},"author":11084,"featured_media":28797,"comment_status":"open","ping_status":"closed","template":"","categories":[1869,43,298,154,614,18,60,67,346,4353,91,2671,612],"class_list":["post-28784","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-ai","category-analytics","category-big-data","category-china","category-coffee","category-consumer","category-data","category-growth","category-machine-learning","category-ml","category-mobile","category-mobile-app","category-starbucks","hck-taxonomy-organization-starbucks","hck-taxonomy-industry-retail","hck-taxonomy-country-china"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n\n
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