Transforming Starbucks using Data Analytics | Big Data | AI | MBA Case study examples with solutions

Big‌ ‌Data:‌ ‌The‌ ‌Secret‌ ‌to‌ ‌Starbucks’‌ ‌Success‌

The success of Starbucks cafés has been good for coffee distributors all over the United States. Why? Because Starbucks has given coffee a new cachet. All coffee. Not just the product sold by Starbucks but also the 80% sold in supermarkets—and all the rest.

Starbucks is one of the largest and best-known companies in the world, with over 27,000 stores and $22 billion in revenue last year. Data is key to Starbucks, which includes a head of Global Strategy, Insights and Analytics as part of its executive leadership team. According to Starbucks, this function uses “methodologies ranging from ethnography to big data analytics… that helps support Starbucks pricing strategy, real estate development planning, product development, trade promotion optimization and marketing strategy.”

Not only does Starbucks go through mounds of coffee beans to satiate its raving fans, but they also have mounds of data that they leverage in many ways to improve the customer experience and their business. With 90 million transactions a week in 25,000 stores worldwide the coffee giant is in many ways on the cutting edge of using big data and artificial intelligence to help direct marketing, sales and business decisions.

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. The mobile app has more than 17 million and the reward program has 13 million active users. These users alone create an overwhelming amount of data about what, where and when they buy coffee and complementary products that can be overlaid on other data including weather, holidays and special promotions. lets see some of the ways that Starbucks uses the data it collects.

0:00 Transforming Starbucks using Data Analytics
0:20 Starbucks and Data Analytics
1:17 Starbuck's Organizational Change
2:13 Store Location Analytics
3:31 New Product Management
4:33 Digital Transformation
5:50 Creating Dynamic Menus
7:05 Machine Maintenance Analytics
9:09 Conclusion

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