Abstract: Dynamic pricing is widely adopted by industries such as airlines, hospitality, and cloud to react to demand changes and boost revenue of fixed rent-by-time assets. The parking industry faces similar opportunities to greatly increase their $30 billion annual revenue in the US alone, with an additional $50 billion world-wide. At Smarking, we leveraged machine learning and data science to build the first industrial level software for online dynamic pricing in parking this year.
In this talk, we will present the context, constraints, and challenges of dynamic pricing in parking. We will show how we apply data science approaches to implement an online dynamic pricing software to automatically learn from and react to real-time demand changes. We worked with three of our industrial partners in Chicago, Boston, and New York respectively to launch trials of the software. We achieved more than 5% of revenue increase for each of them during the trial period. Such results demonstrated the power of data science in a traditional industry. Further, from the data we collected, we found great opportunities to achieve more than 10% of revenue increase, which could eventually contribute multi-billion dollars of additional annual revenue to the whole parking industry.
Bio: Dr. Maokai Lin is CTO and Co-Founder of Smarking, a data analytics Software as a Service (SaaS) startup for parking management based in San Francisco. Since launching Smarking in 2014, Maokai leads the company to provide real-time data visualization, predictive analytics, and dyanmic pricing for over 2000 parking locations in the US and Canada, including clients such as City of Miami, City of Santa Monica, Boston Logan Airport, San Diego Airport, MIT, and Brookfield Properties.
Maokai holds a PhD degree in Operations Research from MIT. Before Co-Founding Smarking, Maokai worked on pricing and modeling at Facebook and Morgan Stanley. Maokai also won “fittest baby” in his hometown at 9 months old.