{"id":15200,"date":"2016-11-16T20:31:37","date_gmt":"2016-11-17T01:31:37","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/starbucks-grinding-beans-and-data\/"},"modified":"2016-11-16T20:31:37","modified_gmt":"2016-11-17T01:31:37","slug":"starbucks-grinding-beans-and-data","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/starbucks-grinding-beans-and-data\/","title":{"rendered":"Starbucks: Grinding Beans and Data"},"content":{"rendered":"

Have you ever wondered why Starbucks places its locations in such close proximity? Have you ever seen something like the picture below in which two Starbucks locations are a mere hundred feet apart?<\/p>\n

\"starbucks-locations\"<\/a><\/p>\n

Although location strategy such as this may seem insane, Starbucks has been using Atlas, a geographic information system created by Esri, to determine their future store locations and overall growth strategy1<\/sup>. Using this application, Starbucks can map out local trade areas, retail clusters, area demographics, traffic patterns, transportation nodes, and where new offices are being built (generally leading to a possible increase in customer base). Atlas can also be used to analyze where customers tend to spend a higher average amount of money for products, allowing Starbucks to target more expensive products such as their Clover Brewing System\u2014a smoother premium coffee that demands a higher price2<\/sup>.<\/p>\n

Starbucks also uses data to understand their customer product needs. Using in-store sales data and interviews with baristas, Starbucks has been able launch their K-Cup products in groceries stores. The data allows them to see which in-store products are the most popular in given areas to stock grocery stores appropriately with matching K-Cup products. They can also hone in on specific details for each product; for example, Starbucks determined that 43% of tea drinkers do not add sugar to their order, so they created a line of unsweetened tea K-Cups. This recent move into K-Cups has been a great marketing tool; allowing Starbucks to \u201ccome home\u201d with the consumer by deterring customers from purchasing other grocery brands. Starbucks is not scared of cannibalizing their in-store revenues as most customers will still need to go to the store to get specialty drinks3<\/sup>.<\/p>\n

\"16ct_group_cluster_large\"<\/a><\/p>\n

Starbucks uses its loyalty cards to track specific individual buying data. The company has approximately 6 million people in their loyalty card program; of which, they have currently made individual profiles for half. These profiles allow Starbucks to see which products are selling to which individual customer demographics. It also allows the company to analyze purchasing trends and timelines to send mobile coupons and promotions when needed; generally targeting discounts towards customers who are showing tendencies of not repurchasing at Starbucks4<\/sup>.<\/p>\n

Starbucks is also generating interesting regional and time based promotions. They have been able to analyze areas with high concentrations of smart phones to release mass-quantity app-based discounts. They have also used weather pattern data to create Frappuccino promotions that coincide with oncoming heatwaves5<\/sup>.<\/p>\n

Overall, Starbucks main objective is to deliver quality products to the most consumers possible. Using \u201cbig data\u201d has allowed them to strategically grow their brick-and-mortar and product offerings to satisfy their caffeine craving customers. Although they have done some interesting work, I think there is a lot more data that can be analyzed in the future. Patrick O\u2019Hagan, director of market planning at Starbucks, has stated that the company is in process of changing their culture from a retail brand to a data-analysis brand. They have been trying to build this from the ground up by changing their hiring patterns to focus on engineers and scientists that know a variety of data analysis tools and can describe the outputs of analysis in an actionable and \u201chuman\u201d way6<\/sup>. I think the key next step for Starbucks will be to greater enhance the \u201cpersonal experience\u201d for their customers. If they can continue to make Starbucks more of an experience and destination where the customer feels special and welcomed vs. just a simple coffee purchase, then they will continue to grow and retain customers. While they are currently using data to send coupons to customers with low retention probabilities, I believe they can start to also focus on their loyal customers to increase their overall sales. GPS technology could possibly allow Starbucks to recognize when a customer is near a Starbucks location and send them a coupon or notification. If a customer continually orders the same item every morning, their GPS may be able to notify a store location to get an order ready before the customer even asks for it. \u00a0Starbucks could also use sales information to send out advertisements\/coupons to customers for their grocery retail offerings vs. just using the data to stock the stores.<\/p>\n

Word Count: 700<\/strong><\/p>\n

Resources<\/strong><\/p>\n

    \n
  1. Barbara Thau, \u201cHow Big Data Helps Retailers Pick Store Locations\u2014an Unsung Key to Retail Success,\u201d Forbes, April 24, 2015.<http:\/\/www.forbes.com\/sites\/barbarathau\/2014\/04\/24\/how-big-data-helps-retailers-like-starbucks-pick-store-locations-an-unsung-key-to-retail-success\/2\/#487487d2340b>, accessed: November 16, 2016<\/li>\n
  2. Carla Wheeler, \u201cGoing Big with GIS,\u201d Esri, August 2014. <http:\/\/www.esri.com\/esri-news\/arcwatch\/0814\/going-big-with-gis>, accessed November 16, 2016<\/li>\n
  3. Sarah Whitten, \u201cStarbucks Knows How You Like Your Coffee,\u201d CNBC, April 6, 2016. <http:\/\/www.cnbc.com\/2016\/04\/06\/big-data-starbucks-knows-how-you-like-your-coffee.html>, accessed November 16, 2016<\/li>\n
  4. Mark van Rijmenam, \u201cStarbucks, the Worlds Largest Coffee Shop, Grinds a lot of Data,\u201d Datafloq, June 20, 2014. <https:\/\/datafloq.com\/read\/world-largest-coffee-shop-starbucks-grinds-lot-dat\/440>, accessed: November 16, 2016<\/li>\n
  5. Neil Ungerleider, \u201cHow Fast Food Chains Pick Their Next Location,\u201d August 25, 2014. <https:\/\/www.fastcompany.com\/3034792\/how-fast-food-chains-pick-their-next-location>, accessed: November 16, 2016<\/li>\n
  6. Mikhal Khoso, \u201cData Analytics in the Real World: Starbucks,\u201d March 4, 2016. <http:\/\/www.northeastern.edu\/levelblog\/2016\/03\/04\/data-analytics-in-the-real-world-starbucks\/>, accessed: November 16, 2016<\/li>\n<\/ol>\n

    Pictures<\/strong><\/p>\n

    Main: http:\/\/www.dananicolefitness.com\/wp-content\/uploads\/sites\/4\/2015\/05\/SB7.jpg<\/p>\n

    Stores: https:\/\/www.quora.com\/Where-are-the-two-closest-Starbucks-in-the-world<\/p>\n

    K-cups:\u00a0https:\/\/g.foolcdn.com\/editorial\/images\/192755\/16ct_group_cluster_large<\/p>\n","protected":false},"excerpt":{"rendered":"

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