During my time with the Clippers, I worked on various machine learning and analytics projects. Two of my most prevalent are listed below:
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Season Ticket Holder Objections: I analyzed call transcripts from season ticket holders to identify common objections raised by customers. By carefully reviewing these transcripts and using text analytics methods, I was able to uncover recurring themes and patterns. This allowed me to provide valuable insights to the company and make recommendations for improving customer satisfaction and retention.
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Attendance Predictions & Key Variables: In this project, I deployed machine learning models to analyze key variables that impact attendance at Clippers games. By utilizing data from various sources and employing advanced machine learning techniques, I was able to uncover important insights and patterns in attendance behavior. This information helped the company make data-driven decisions on how to improve attendance rates and optimize their operations.
Additionally, I worked on various ad-hoc requests and foundational analytics projects. The main analytics project I worked on during my first summer with the Clippers was in regards to developing a new ticketing model for the Intuit Dome - the Clippers' new stadium opening in Inglewood for the 2024-25 NBA Season. My team and I proposed a subscription-based ticketing model that would aim to improve the fan experience and reach a wider target market than we previously had been.
I'm proud to say that the subscription-based model will be implemented for the Clippers' first season in the Intuit Dome! By targeting a specific demographic and opening up this opportunity for fans to attend games, we hope to see an increase in Clipper spirit as well as a $1-2 million increase during the first year of implementation.