A team of FIT students took third place in this year’s Adobe Analytics Challenge, an annual competition in which undergraduate and graduate students crunch real data from major corporations and make business recommendations. Past brand partners have included Condé Nast, T-Mobile, and Major League Baseball; this year, Nike sponsored the challenge.
Team Flash—Fashion Business Management seniors Muskaan Arora, Joyce Ishikawa, and Sofia Simoniello—stood out from more than 1,600 entrants from around the world. During a final presentation and judging on Nov. 17, the students won $6,000; the top prize, awarded to the Indian Institute of Technology, was $35,000.
“I was blown away by the imagination and innovative ideas you all came up with on a very short timeline,” Emily White, vice president of enterprise data and analytics at Nike, said at the final presentation.
The Adobe Analytics Challenge has taken place annually for select universities since 2005, but for the first time this year, it opened to colleges and universities worldwide. The other five finalist teams hailed from major research institutions such as the University of Chicago’s Booth School of Business and UCLA Anderson School of Management.
Students were given two weeks to analyze usage data from Nike’s e-commerce platform and mobile app, using the Adobe Analytics platform, an e-commerce analytics and business intelligence software, and make recommendations for improving sales.
FIT’s team made numerous discoveries. For example, customers were more likely to pay full price on the app than on the website, Android users were more engaged and loyal than iOS users, and the more time consumers spent on the checkout page, the more likely they were to abandon their shopping cart.
They created a consumer segmentation funnel to identify the most engaged consumers and drew up profiles of those customer types to make their analysis more concrete. Their recommendations included offering a limited-time promo code at checkout to give buyers a sense of urgency, and personalizing the app experience to encourage loyalty. For each recommendation, they appended a projected sales increase.
“We had to think from a blend of the customer’s and retailer’s experience,” Arora said. “We had to figure out the problems as a customer, but the solutions as a retailer.”
The five judges were analytics experts from Adobe, Nike, and elsewhere. One judge, Hila Dahan, COO and co-founder of consulting firm 33 Sticks, said she appreciated FIT students’ rooting in fashion, “looking at the mindset of the Nike guest and trying to analyze from the consumer’s perspective.”
Maria Hwang, the assistant professor of Computer Science who advised Team Flash, believes the students’ analysis stood out because it brought a narrative to the theoretical concepts. “Usually theoretical stuff is harder to understand,” she said, “but adding that practical flair fed right into the judges.”
She was also impressed that they substantiated their claims with numbers and cited their sources, a practice that is de rigueur in academia but less common in business.
All three students had taken Hwang’s class in machine learning, a field in which data is harnessed to train algorithms that predict future behaviors. They also participated in an informal course Hwang offered over the summer in data science and analytics, a related field in which data is mined for insights into the consumer. Machine learning and data science are becoming essential skills for the fashion industry, as sales forecasting becomes ever more precise to reduce inventory and maximize profits.
Traditionally, these computer science and data–focused fields have been dominated by men—as have past winning teams of the Adobe challenge—but this year, women comprised most of the top six teams.
Hwang is passionate about helping women enter computer science and math fields. She often hears students say, “I’m not a math person.” “That is not a concept that exists in this world!” she said. “There is no math gene. But that fear is really preventing a lot of talented people from entering this whole world.”
Simoniello admitted that she was nervous about entering a data analytics challenge. “I always thought, maybe I’m not capable of doing this because I don’t have a computer science degree,” she said. “And then I told myself, I’m a student, and we’re here to learn and fail and everything. But with dedication and with effort, there is nothing we cannot do.”
Watch the final presentation here: