I worked as a Summer Business Analyst at E15 Analytics, the consulting arm of Levy, a company that sells food and beverage at hundreds of stadiums and venues across the country.

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I consulted with the Artificial Intelligence practice by working with large stadiums and venues to reduce their labor cost while improving the fan experience. I redesigned our staffing system from the ground-up, using an entirely new approach that significantly reduced the number of temp workers at our stadium, while also reducing the average length of a line by 4 minutes at popular stands, improving the fan experience while saving over $1.15M in one year. A critical part of my work was taking complicated new technology—Artificial Intelligence—and successfully convincing the entire leadership team at our parent company to utilize our approach by explaining our process and results.

 

As a member of the beverage analytics team, I worked on two different projects during my summer working in Chicago.

 

The first focused on determining the optimal beer sales mix at stadiums, and how it should be optimized leveraging all of the data E15 had available. I developed an Artificial Neural Network in Python to understand and analyze the US alcoholic beverage market and provided individualized recommendations for clients on the optimal proportion of beer sales that should come from each segment of beer, such as National Craft, Latin import, and Domestic Light, to achieve financial targets. My recommendations are actively being used at over 100 venues to guide pricing, promotions, offerings, and other business decisions in the beverage space.

 

The second focused on automating the process by which data is cleaned and made ready for analysis from clients to E15. I used Python to automate this process, and was able to save more than 270 hours every week, and improve the accuracy and integrity of beverage data by 3.8%. In addition, I created an intelligent system that is able to get smarter the more it is used.

 

Through this process, I was able to both develop computer systems that will be used for years to come and gain a better understanding of the CPG sector.