One of the biggest challenges in producing theater on any scale is to fill a house with people paying the "right" price. Producers want to maximize revenue. Audiences want to be entertained for a price that keeps the entertainment enjoyable.
Over the summer, I aim on developing the skillset to determine the perfect price point for shows so that houses are at 100% capacity with audiences that are paying prices they are most comfortable with. Revenue is maximized, producers can make more shows, actors + creative team have more jobs, and the industry grows!
Financial services already use these technologies. So do airlines, hotels, Facebook, Google, and Amazon. Let's use data science in Broadway!
After researching the tools that currently exist, I've decided to use LYNDA.com and DataScienceAcademy.com to learn SQL, R,and PYTHON. I am most enthused by Kevin R. Williams's study from Yale: "Dynamic Airline Pricing and Seat Availability." Similarly Ian Boneysteele, Konstantine Buhler, James Kernochan, Mike Mester, and Soren Sudhof study at Stanford: "Forecasting Broadway Show Gross Revenue."
Wish me luck as I begin this next stage of my journey!
Boneysteele I., Buhler K., Kernochan, J., Mester M., Sudhof S. (2016). Forecasting Broadway Show Gross Revenue. [online] Stanford School of Business. Available at http://zoo.cs.yale.edu/classes/cs458/lectures/old/Broadway/Final%20report%20vF.pdf [Accessed 18 Jun. 2018].
Steinmetz, J. (2016). Exploring Broadway Data in Tableau | InterWorks. [online] InterWorks. Available at: https://interworks.com/blog/jsteinmetz/2016/08/03/exploring-broadway-data-tableau/ [Accessed 18 Jun. 2018].
Williams, K. R. (2017). Dynamic airline pricing and seat availability. Yale School of Management; Yale University - Cowles Foundation