MSBA Students Deliver Fresh Insights for Mall of America

Winners of the predictive live case, from left: Imran Khan, Moumi Panja, Aparajitha Chappa, Katharine Krawczyk, Vineet Tanna, Zeyuan Li

Each year, Mall of America welcomes 40 million visitors. They come to shop, of course, but they also come to enjoy events and attractions, especially Nickelodeon Universe. Located at the center of the mall under acres of skylights, the park features 27 rides for kids, families, and thrill-seekers. The experience those guests have when they visit is a top priority for Mall of America, according to Janette Smrcka, director of information technology.

“In the age of easy online shopping, the in-person experience is what differentiates us,” says Smrcka. “We’re always looking for ways to improve the experience and give guests something unique and memorable. Nickelodeon Universe is a huge part of that.”

In search of new insights, Smrcka and colleague Phil MacDonald brought a pair of questions and a cache of data to students in the Carlson School Master of Science in Business Analytics (MSBA) Program.

“We have a lot of data about Nickelodeon Universe,” says MacDonald, referring to ticket sales and the data collected when park employees scan guest tickets at individual rides. “We were pretty sure we could learn some interesting things from that data, but we didn’t have the time or bandwidth to look into it.”

The Mall of America team seriously increased its data analytics bandwidth and quickly got a range of insights by sponsoring two live case competitions with the Carlson Analytics Lab and the MSBA Program. The two cases happened simultaneously during fall semester, as part of Assistant Professor Ed McFowland’s exploratory analytics course and Assistant Professor Yicheng Song’s predictive analytics course.

Over four weeks, 20 teams of four to six MSBA students tackled the wide-open exploratory case question: What can Mall of America learn from this data, and how can that be used to improve customer experience in Nickelodeon Universe? For the predictive analytics case, the same teams constructed various models to predict ridership levels at the park up to a year in advance.

“The atmosphere in the class was electrifying with respect to both the projects,” reports student Utkarsh Khandelwal. “There was a strong sense of competition and everybody wanted to do their best, not just to win, but because it was the first real project we were doing as a class.”

Winners of the exploratory analytics case with clients from Mall of America, from left: Utkarsh Khandelwal, Rachel Wolfe, Janette Smrcka and Phil MacDonald from Mall of America, Chelsea Hui Dong, John Arul Selvam, Bhuvan Oberoi, and Brian Spielman (MOA).

The professors evaluated the work of all the teams and selected finalists to present to Mall of America.

“Successful teams applied, combined, and extended current data science techniques in novel ways to detect patterns,” says McFowland, “These patterns needed to be supported by data, of course, but also translated into convincing, actionable insights to improve the business.”

In the end, Mall of America saw a range of creative solutions from the students. Teams used the data to explore everything from wait times at rides to crowd flow in the park, and from visitor behavior to the effect of promotions. Choosing the winners was tough.

“All the teams had strengths and did excellent work,” says MacDonald. “It felt like the Olympics, where the difference between first place and eighth comes down to fractions.”

Smrcka agrees. “We were really impressed with the work and got a ton of value out of this. Although only one team can be named winner of each case, we saw many solutions we’ll be able to implement at Mall of America.”

Khandelwal, whose team won top honors in the exploratory case, also sees the bigger picture: “The fact that the work you did can bring about tangible changes to the biggest mall in America and affect the experience of millions of visitors brings an immense feeling of fulfillment and justifies all the hard work we put in.”