Analytics for online markets
Author(s)Johnson, Kris (Kris Dianne)
Massachusetts Institute of Technology. Operations Research Center.
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Online markets are becoming increasingly important in today's world as more people gain access to the internet. Furthermore, the explosion of data that is collected via these online markets provides us with new opportunities to use analytics techniques to design markets and optimize tactical decisions. In this thesis, we focus on two types of online markets -- peer-to-peer networks and online retail markets -- to show how using analytics can make a valuable impact. We first study scrip systems which provide a non-monetary trade economy for exchange of resources; their most common application is in governing online peer-to-peer networks. We model a scrip system as a stochastic game and study system design issues on selection rules to match trade partners over time. We show the optimality of one particular rule in terms of maximizing social welfare for a given scrip system that guarantees players' incentives to participate, and we investigate the optimal number of scrips to issue under this rule. In the second part, we partner with Rue La La, an online retailer in the online flash sales industry where they offer extremely limited-time discounts on designer apparel and accessories. One of Rue La La's main challenges is pricing and predicting demand for products that it has never sold before. To tackle this challenge, we use machine learning techniques to predict demand of new products and develop an algorithm to efficiently solve the subsequent multi-product price optimization. We then create and implement this algorithm into a pricing decision support tool for Rue La La's daily use. We conduct a controlled field experiment which estimates an increase in revenue of the test group by approximately 10%. Finally, we extend our work with Rue La La to address a more dynamic setting where a retailer may choose to change the price of a product throughout the course of the selling season. We have developed an algorithm that extends the well-known multi-armed bandit algorithm called Thompson Sampling to consider a retailer's limited inventory constraints. Our algorithm has promising numerical performance results when compared to other algorithms developed for the same setting.
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 147-153).
DepartmentMassachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology
Operations Research Center.