Turning marketing campaigns into actual sales, restocking retail inventory in real time, determining freight shipping efficiency, and evaluating talent pipelines all have one requirement in common: optimal data analytics.
But successful implementation of business analytics requires leading-edge tools and sophisticated data modeling skills—exactly what MIT Analytics Lab (A-Lab) students know best. This year, 25 A-Lab student teams worked with their sponsor organizations on a wide range of data-set problems—from examining talent pipelines, to predicting real estate metrics, and analyzing stock replenishment at local grocers. Their analytical acumen yielded cost-saving results for the businesses—and awards for the student teams.
On December 3, the teams presented their semester-long projects to a panel of judges that chose a winner based on creativity, execution, and business value. The winning team—whose names will be etched on the silver winner’s cup—was Team “Lasso Ladies” representing Atlassian, a maker of software collaboration tools such as Jira, Confluence, Bitbucket, and AtlassianMarket. The team, Kim Adler, Tiana Cui, Grace Garbrecht, and Zijin Wang, found that the most effective way to convert leads to actual sales includes multiple intermediary touchpoints such as webinars, trials, and website resources.
Pictured above: Team “Lasso Ladies,” Kim Adler, Tiana Cui, Grace Garbrecht, and Zijin Wang
The second-place team “Data Divers,” representing Retail Business Services, offered data-based stock replenishment optimization schedules that can prevent millions of dollars in lost sales and product spoilage.
Also in the top three was a team representing Listrak Inc. which studied the impact of historical data on e-commerce purchasing models to reduce data storage costs.
MIT IDE Director and A-Lab Professor, Sinan Aral, noted that today’s labor market–where demand outstrips supply and analytics is becoming mandatory—makes A-Lab timelier than ever. “Data science and analytics can move the needle on how to redeploy the workforce,” he said, introducing the teams at the presentation event. “Learning by doing” is the only way to excel in business analytics, according to Aral, and these projects pave the way.
The judges included Yael Avidan (Senior Director, Product – Core Shopping Experience, Chewy), Renée Richardson Gosline (Senior Lecturer, MIT Sloan School of Management and Human/AI Interface Research Group Lead, MIT IDE), and Justin Fortier (Principal Data Scientist, ViralGains). They based their decisions on four key criteria: Use of technology and analytics; how much improvement the project delivered; the overall business impact and implications of the findings, and the clarity and delivery of the presentation itself.
“It’s inspiring to learn from students’ work, and the Analytics Lab is one of my favorite forums,” Tweeted Professor and A-Lab judge Gosline. It’s a “four-hour marathon of stellar data science pitches.”
Pictured above: Winning team poses with judges Justin Fortier, Renée Richardson Gosline, Yael Avidan, and Professor Sinan Aral
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.