From Data to Dining Delight

In the fast-paced world of quick-service restaurants, customer satisfaction can make or break a brand. But how does a restaurant executive measure something as intangible as a diner’s contentment?

Market research company Ipsos posed that challenge to a student team for the capstone project in Northwestern Engineering's Master of Science in Artificial Intelligence (MSAI) program and Northwestern's MBAi program — a joint-degree program offered between Northwestern's Kellogg School of Management and the McCormick School of Engineering.

For Nathan Adamson (MBAi '24), Victoria Lever (MBAi '24), Xin Li (MSAI '24), Yijie Li (MSAI '24), and Aaryansh Sahay (MSAI '24), this industry challenge tested their ability to blend cutting-edge AI with sharp business acumen, bridging the gap between the MSAI and MBAi programs.

Ipsos provided the students with a project exploring key drivers of satisfaction and strategies for retaining and acquiring customers for quick service restaurants.

The team dove into a dataset of answers to 40 questions about customer experiences at quick-service restaurants. Their challenge was to distill these answers into meaningful insights for Ipsos and its clients.

To do that, the team created six different clusters from the huge number of responses.

This data reduction allowed the team to perform causal analysis, identifying key factors influencing overall satisfaction.

But the real challenge lay in translating those statistical findings into actionable business insights.

“We would take these results and then transform them into a message for the client that was really valuable and really actionable,” Adamson said. “That’s where that intersection of business and artificial intelligence came into play.”

The project highlighted the unique strengths each program brings to business challenges. MSAI students provided more technical expertise, while MBAi students focused on business applications and client communication.

Students from each program said they benefited from seeing the perspective of their other team members.

“When you are in that technical bubble, you can lose the farsighted, long view of the entire project,” Sahay said. “The project was not about maximizing accuracy or using a state-of-the-art model. It was about getting more customers to stay with your brand.”

For Lever, the experience underscored the importance of interdisciplinary collaboration. She said it helped her relate better to more technically minded teammates.

This was the second time Ipsos partnered with the MSAI and MBAi programs for a capstone project. Sarah Logman, senior vice president for experience analytics and innovation at Ipsos, was impressed with the results.

“The students delivered a fantastic report that spurred ideas for how we can freshen up our approach at Ipsos,” Logman said. “It was very close to a client-ready presentation in that it focused on the business implementation of the results and what it meant for the client rather than just what the model produced.”

As the students wrapped up their presentation, they realized their respective programs taught them more than just technical skills. They now have the skills to narrow the divide between data science and business strategy, a crucial ability as industries become more AI-driven.

Sahay said the project taught him the importance of balance between technical excellence and business applicability.

“We learned to think beyond the code and consider the practical implications of our work,” he said. “This experience has fundamentally changed how I approach data science problems, making me a more well-rounded professional.”

Logman said the collaborative capstone makes graduates of both the MSAI and MBAi programs infinitely employable in today’s business world.

She said she saw in the MSAI and MBAi students something she pushes with her own team at Ipsos.

“It’s extremely valuable to learn the need to have business context feed the analytical work,” she said. “This is a critical skill I emphasize to my own data science teams and any up-and-coming data scientist that I come across.”