Holiday Recommendations: Using Big Data to Keep Up with Consumer Preferences

With the holiday season approaching, consumers are looking to eat, drink, and be merry. To keep up with changing tastes during the festivities, restaurant and dining brands can cook up better insight into customer preferences through machine learning and Big Data analysis. 

Making Recommendations

Writing in Forbes, Bernard Marr reports that Starbucks uses the massive amounts of data generated by more than 17 million people using its smartphone app to work sales and marketing decisions. The app gives the chain the power to offer individualized project recommendations based on a customer’s fast history:  

In addition, based on ordering preferences, the app will suggest new products (and treats) customers might be interested in trying. This intel is driven by the company’s digital flywheel program, a cloud-based artificial intelligence engine that’s able to recommend food and drink items to customers who didn’t even know, yet, they wanted to try something new. It’s so sophisticated that the recommendations will change based on what makes the most sense according to the day’s weather, if it’s a holiday or a weekday, and what location you’re at.

This kind of personalized service builds customer loyalty and increases engagement, educating consumers about menu items they might not be familiar with while also offering a low-pressure way to market new offerings to customers. 

Importance of Quality Ingredients

Big Data analysis doesn’t just happen. It takes careful planning and development of a solid digital infrastructure before data can be put to use. 

According to Adam Rogers, writing in Wired, clean ingredients matter as much in data analytics as they do in food production:

If you don’t have a methodology designed to produce data ready-made for statistical analysis, you’re using “found” data, which is always messy, says Duncan Watts, a social scientist at Microsoft Research. “In data science there’s a trope about how 90 percent of the work involved is cleaning and organizing the data itself,” Watts says. “It’s true for email data, browser data, Twitter data, news media data, and even administrative data that’s supposed to be clean.”

Restaurant brands can benefit through the application of machine learning and Big Data analysis, but proper data management and hygiene can make the difference between successful data analysis and an expensive waste of time. 

Dealing with Data

Restaurant brands of any size can benefit from digital initiatives. LMS uses our institutional knowledge to design and implement the digital backbone necessary for Big Data analysis. To learn more, contact us today at 800-257-5902 or via email at [email protected].


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