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Winning 2020 A-Lab Team Gets Buy-in from Retailer

December 14, 2020

By Paula Klein

Can data analytics help spur healthier eating habits?  The answer is definitely yes, based on the work of the 2020 MIT Analytics Lab (A-Lab) winning team.  The four graduate students worked with Retail Business Services on a project analyzing the correlation between healthy foods and cost savings.

The MIT MBA students--Anjali Krishnamachar, Juliette Chevallier, Philipp Simons, Taylor Facen--guided by their mentor, MIT alumni Heather Fraser, set out to determine what incentives might drive supermarket shoppers to more nutritious food choices.  During the fall semester, they created different models  to see, for example,  if 5% price discounts would nudge shoppers to the healthier groups of foods promoted by RBS's Ahold Delhaize USA stores, which include Food Lion, Giant Food, and Stop and Shop.

The students analyzed buying patterns of two types of shoppers and tested 1,300 products to see which had the greatest price elasticity. The team crunched the data and concluded that a holistic approach using both discounts and customer targeting would yield the best outcomes. They also offered the retailer an alternative approach-- bundling certain products together, to attract customers to more nutritious foods.

Jumpstarting New Ideas

Eric Braun, Executive Innovation Strategist at RBS, fully embraced the internal/external collaboration. The project not only helped RBS program leaders toward their corporate goal, it “completely opened their eyes” to the applications of data science and analytics, he said. It also will help to jumpstart new ideas incorporating data science techniques throughout the business, he said. “I give the project an A. The students were phenomenal, pleasant, fun, and dedicated. It was a perfect, iterative, exploratory project.”

A panel of three MIT judges, Yael AvidanRenée Richardson Gosline, and Tod Loofbourrow, evaluated 23 team proposals from 83 students in this year's competition held virtually on December 11. Key criteria included: Technology and analytical advances; creativity; effort and overall business impact; problem-solving, and presentation. 

Announcing the winning team, IDE leader, Gosline noted that the judges were very impressed that the RBS team not only showed how to help people eat healthier, the students "overcame roadblocks, developed deep insights about the company, and humanized the data points for broader use." The team’s name will join previous year winners engraved in the A-Lab silver cup award (held by IDE Executive Director, David Verrill, in the above photo.)

This year's second place team worked with the children's app, LittleHoots, to develop a dashboard to aid marketing strategies. The third place team provided AB InBev a way to analyze the obsolescence and revenue optimization costs of beer brands.

Business Solutions

During the MIT A-Lab semester, student teams select and deliver a project using analytics, machine learning, or other digital technologies, to solve business problems. The IDE-led course runs each fall and is part of MIT Sloan School of Management’s suite of Action Learning offerings. It is led by IDE Director, Sinan Aral, and Abdullah Almaatouq. Braun said RBS initially proposed five ideas narrowed to three when the semester began.

Leaders of the RBS Guiding Stars program, in charge of the healthy food initiative, will meet in January to determine next steps and how to implement the A-Lab recommendations, Braun said.  He is also open to further research collaborations with academia to get “more diverse thinking” into the mix.

To date, A-Lab has attracted more than 400 students from a dozen MIT departments to work on over 90 projects spanning IoT, digital technology, platforms, finance, e-commerce, retail, manufacturing, medical supply chains, workplace safety, and global health. 

Some projects are tightly focused on dilemmas organizations currently face, which requires students to quickly understand particular business circumstances and domains before performing their descriptive, predictive, or causal analysis. Other projects are more open-ended, and students must think entrepreneurially about how to bring new value to existing data and suggest frontiers for future business opportunity.