Big Data Drives Design at Big Chains

Data science is a critical tool in everything from site selection to customer retention. 

 

Starbucks is on the cutting edge of using big data and artificial intelligence to guide business decisions. When the company launched its rewards program (13+ million users to date) and mobile app (17+ million users to date), it dramatically increased both the quantity and richness of data flowing in, enabling it to better understand customers’ product preferences and purchasing behaviors — all in the name of continually upping its game and creating a more personalized, more relevant and, in the process, more profitable Starbucks. 

McDonald’s too, looks at multiple data points to enhance the customer experience. Under new CEO Steve Easterbrook, the company has ramped up efforts to collect and analyze data, turning to next-level trend analytics to better understand not just what’s happening at the individual unit level but also to identify patterns and best practices that it could act on to impact the McDonald’s experience more broadly.

Using sales data, sensors, video and other quantitative and qualitative data collection techniques, the company captures information on kitchen operations, customer purchase decisions with cashier attendants and at self-serve kiosks, and customer behavior at the drive-thru and in seating areas. It analyzes obvious data points, such as time spent ordering, as well as much more subtle details, such as customer eye movements as they scan menu boards for insights on highest areas of interest. Like Starbucks – and by now most competitors in the limited-service restaurant space — it has also rolled out a popular mobile app with loyalty program, which enables it to gather even more information on guests and their buying habits.

Capturing data is the relatively easy part. What companies aren’t prepared to do internally can be readily collected, sliced and diced for a price by a fast-growing cadre of third-party data analytics services. The toughest part about data is closing the loop and actually acting on what it tells you.

At McDonald’s, analytics has led to a variety of enhancements made to provide a better customer experience. Drive-thru data has been leveraged to tweak design and flow for faster service; in-store data has been mined to make new iterations in layout and design, including installation of more self-service kiosks (which become another big source of data); mobile-app data has been analyzed to send targeted discounts based on loyal customers; and geo-tracking data from the mobile app is used to locate customers as they approach a McDonald’s store, triggering the kitchen to start preparing their order so it’s ready faster.

For more on how data drives design, look for the full-length feature in our July/August issue.

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