Using Data Science to Optimize Pricing for the Individual
Sacramento Data Science Meetup
Gallagher Hall, Room 2310

Sacramento Data Science MEETUP

The ability to perform customer optimized pricing is in high demand. UC Davis GSM Assistant Professor Mike Palazzolo will speak on his recent work in this area. 

Networking: 6:30 p.m. – 7:00 p.m. (and post-talk)
Program: starts 7:00 p.m.

Location: UC Davis Gallagher Hall – Room 2310

Assistant Professor Mike Palazzolo’s Presentation:

Posted pricing is the dominant framework for mass market products and services. Firms announce prices and customers decide whether or not (or which version) to purchase. Only customers willing to pay the set price get to benefit from the product, and firms learn only how many consumers were willing to pay the set price. The modern technological environment enables richer two-way communication between firms and consumers, allowing for a more sophisticated approach to data collection and, in turn, pricing. Rather than giving consumers a take-it-or-leave-it price, firms can solicit price preferences from consumers, revealing the demand curve for their products and allowing for a greater degree of price discrimination.

In this talk, we discuss how analytics is helping us leverage data to design an adaptive “name your price” bidding mechanism for an arts and entertainment venue. The talk will cover descriptive analytics (use of historical data to understand consumers’ strategic purchase behavior), predictive analytics (predicting how consumers will respond to a “name your price” mechanism), and prescriptive analytics (deciphering optimal pricing policies). 

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