The right use of data changes everything. Disrupting Markets is your window into how companies have digitally transformed their businesses, shaken up their industries, and even changed the world through the use of data and analytics.

When Jerry Baldwin, Gordon Bowker, and Zev Siegl opened the first Starbucks store in 1971 near the historic Pike Place Market in Seattle, they couldn’t have known that their small, local coffee shop would turn into a worldwide empire. Following a localized expansion, stores started springing up outside North America and, by the late 1990s, Starbucks became the largest coffee-house chain in the world. 

Today the business boasts over 31,000 stores worldwide and over 400,000 employees, serving 100 million customers a week. And these aren’t the only figures that have grown over the years: in 2019, the firm recorded its highest-ever revenue, having generated over $26.5 billion.

Invest in data, invest in your company

It’s no coincidence that this recent growth has come alongside a huge investment in data analytics. Recently described as “not a coffee business, but a data tech company,” the firm now has a dedicated team of data scientists, led by Jon Francis, Starbucks’ senior vice president of enterprise analytics, data science, research data, and analytics. This team is committed to understanding the huge amount of data produced by the company on a daily basis.

“Becoming data-driven has always been about more than just convenience, and ‘how do we sell more product?’ It’s about using these digital tools to elevate the analog human experience.”

Jon Francis, SVP Data Analytics, Starbucks

“Every store has its own personality,” Francis said recently. “Every store has its own set of customers and its own set of characteristics, and artificial intelligence (AI) can help us understand those individual store characteristics better. If you try to run one algorithm for all stores, you’ll make progress. That’s kind of what we’re doing now. But to really breakthrough, you have to get down to the individual store level, and making sure we’re making it as easy as possible for each store manager to create the culture and the kind of human connection we aspire for where they are, because when we are able to do that — wow, we are at our best.”

Give users the data reports and insights that makes sense:

Learn how to deliver

Francis said this is about more than asking his data scientists to push the envelope to the bleeding edge. “It’s always going to be bigger than that. It’s always going to be about more than just convenience, and ‘how do we sell more product?’ It’s about using these digital tools to elevate the analog human experience.”

To this end, the firm now collects and processes information from customers, stores, and even its coffee machines using advanced technologies ranging from cloud computing to the Internet of Things (IoT), AI, and blockchain.

Delving deeper into the in-store experience

The firm’s internal AI platform, which is called Deep Brew, is at the crux of Starbucks’ current data strategy. The software’s capabilities include tracking the inventory that moves through its stores and automatically calculating replenishment orders. This frees up a huge number of hours for store staff, who previously counted everything by hand. Deep Brew is also used to predict staffing requirements so Starbucks can add workers where and when needed. This has massively improved scheduling processes.

Deep Brew will increasingly power our personalization engine, optimize store labor allocations, and drive inventory management in our stores.

Kevin Johnson, Starbucks CEO

Deep Brew was something that Starbucks CEO Kevin Johnson was keen to discuss during his presentation at the National Retail Federation’s annual show in January this year.

“Deep Brew will increasingly power our personalization engine, optimize store labor allocations, and drive inventory management in our stores,” said Johnson, a tech industry veteran who spent more than three decades at firms including IBM, Microsoft, and Juniper Networks. “In many ways, Deep Brew, and the focus on machine learning and AI, is all about finding ways to help humans have more time to be human. It’s not about robots replacing humans. It’s about technology that frees up our baristas to better connect with one another and connect with customers.”

To this end, Starbucks is also looking into the potential of using voice technology so that baristas can look their customers in the eye and take their order via a mic which transmits information to Deep Brew through natural language processing.

Making new connections with data 

Starbucks isn’t only using data in its stores. Thanks to the IoT, the coffee giant is able to securely connect all of its coffee machines using a guardian module which is connected to the cloud. By separating the device from the network, a guardian module can protect equipment from attack, ensure data is only transmitted between trusted cloud and device communications partners, and ensure the software of the module and the equipment remains intact and secured. Starbucks can use this technology in its new equipment and retrofit it into existing machines.

As a result of this, data is collected about every shot of espresso pulled, the type of beans used, the coffee’s temperature, and even about water quality. This can generate more than five megabytes of data in an eight-hour shift.

Starbucks can also use this connectivity to send new coffee recipes directly to machines in the click of a button—something that in the past could only happen via the manual delivery of USB sticks.

“Think about the complexity—we have to get to 30,000 stores in nearly 80 markets to update those recipes,” said Jeff Wile, senior vice president of retail and core technology services for Starbucks Technology in a recent interview. “That recipe push is a huge part of the cost savings and the justification for doing this.”

The connectivity also allows Starbucks to remotely diagnose potential problems, reduce maintenance costs, and achieve higher customer satisfaction by freeing up time to allow their partners to connect with their customers.

Improving traceability

As if this wasn’t enough, Starbucks has also put Big Data at the heart of its mission to connect its entire supply chain—a move which has helped to improve transparency and traceability. The firm’s ‘bean to cup’ pilot used blockchain technology to enable customers to use their phones to scan a code on a bag of coffee to see where the coffee beans were grown. According to another recent Starbucks story, it’s hoped that this will help to create a one-to-one connection between farmers across the globe and someone drinking coffee at, say, a Starbucks in Seattle or Shanghai.

“Coffee producers continue to embrace tools that equip them to be more sustainable in an ever-changing environment,” said Jean Nkunzimana of the MISOZI Coffee Cooperative in Rwanda. “From unpredictable climate changes to commodity price volatility, farmers continue to face multiple challenges in a global marketplace. With identity being the foundation of traceability, farmers have been able to leverage the value of being identified to create a credit history of the value of their production, as well as an acknowledgment of their self-worth.”

While all of these efforts are proving incredibly beneficial, there’s one more very important area that Starbucks is putting front and center of its data strategy: the need to build more personal connections with customers.

“In thinking about the two transformative elements of modern-day retail, it begins by creating unique and relevant experiences,” said Johnson. “If you can’t create a customer experience in your brick-and mortar store, an experience that goes beyond convenience, you’re just another node in the supply chain. And that in-store experience must then be extended to a digital mobile relationship.”

Going big by going mobile

The Starbucks mobile app is the perfect example of an extended digital relationship. The app, which is built and hosted in the cloud, uses reinforcement learning technology — a type of machine learning that uses insights from data to make decisions in complex, unpredictable environments based upon external feedback.

As a result of this technology, 18.9 million active Starbucks Rewards members now receive personalized recommendations from the app for food and drinks based on local store inventory, popular selections, weather, time of day, community preferences, and previous orders.

“We know from experience that when customers join our rewards program, their total spend with Starbucks increases meaningfully,” Johnson said in the firm’s Q1 2020 earnings call. Johnson also went on to reveal that the Starbucks app now drives 17% of sales. “Our industry-leading digital platform will further differentiate us from the competition over time.”

Building a better future with data

As the firm approaches its 50th anniversary next year, it wants to push even harder to deliver more for its customers, especially around sustainability. Its comprehensive data-driven strategy will be key to enabling this future.

“We are embarking on a journey,” Johnson recently told CNBC. “We know this journey will be hard. We are going to have to solve a lot of problems that nobody has solved yet today. It won’t be linear. But we’re going to do this. We are going to do it in partnership with others. And we’re going to be thoughtful and we’re going to bring the market along with us. But this is something that as we approach the 50th anniversary of Starbucks, will redefine the company.” 

Learn from the best 

Those organizations looking to become data-driven can learn from Starbucks’ success and apply data in three key ways: to empower frontline workers; to better connect all aspects of their business; and to build customer loyalty. 

The future of any business — any industry — will be determined by their use of data.

Carmen DeCouto, Product Marketing Manager, Sisense

“The future of any business — any industry — will be determined by their use of data,” says Carmen DeCouto, Product Marketing Manager at Sisense. “It’s not enough to simply collect an infinite number of data points. Every business must be investing in the tools and people to propel them forward in crowded marketplaces where customers may easily choose a competitor.”

By building data teams that can do all of this successfully, you are increase your chance to thrive in a changing world of data. Indeed, recent research from Harvard Business Review Analytic Services, has found that those businesses that have started down this road are already reaping the rewards: 72% say productivity has increased by empowering frontline workers, 69% say they’ve increased both customer and employee engagement/satisfaction, and 67% say they’ve increased the quality of their products and services.

Don’t know where to start? Choosing a platform like Sisense, designed to enable data teams to integrate analytics into everything their business does, makes the process of becoming data-driven simpler than ever.

Give users the data reports and insights that makes sense:

Learn how to deliver

Lindsay James is a journalist and copywriter with over 20 years’ experience writing for enterprise business audiences. She has had the privilege of creating all sorts of copy for some of the world’s biggest companies and is a regular contributor to The Record, Compass, and IT Pro.

Tags: | | | |