My research is largely inter-disciplinary- I love working on problems that require a mixture of techniques. I am also interested in applying statistical and econometric modeling methods to large data sets and analyzing and interpreting patterns in consumer behavior that appear in these data sets.
In the following sections, I summarize my current research and future research interests in these three aspects of online interactions.
My research in this area studies channel interactions in the context of digital entertainment goods. The profusion of digital channels has forced sellers to think about how these channels interact with each other and with established physical channels. I study this phenomenon by working closely with several major industry partners from television and motion picture industries.
Estimating the Impact of Free Streaming Availability on EST Sales
The rise of online digital platforms has caused a massive increase in the number of viewers who consume entertainment via streaming. To cater to this audience, television networks have started making their content instantly available on both paid and free online streaming platforms after broadcast. However, little is known on the impact that these free streaming channels have on consumption in paid channels. In this paper with Prof. Michael D. Smith & Prof. Rahul Telang, I empirically analyze if free streaming on a major television network’s online platform cannibalizes the sales on paid channels. I exploit the natural variation in the online streaming schedules of a prominent television show in our identification strategy. Using a difference-in-difference approach we find that free streaming cannibalizes EST sales by about 8.4%.
This paper won the Best Student Paper award – Runner up, at the Conference on Information Systems and Technology at Nashville, TN, 2016.
Effect of Subscription based Video on Demand Platforms on Movie Sales
In this ongoing work with Prof. Rahul Telang & Dr. Liron Sivan, I study the latest, major form of disruption in the entertainment industry: subscription-based streaming platforms (SVOD). Using a novel and detailed dataset of movie sales, I measure the impact of SVOD platforms on the viewership and sales on other traditional online and offline platforms.
I exploit the detailed nature of the dataset and construct control and a treatment group using information on when movies enter and exit many different SVOD platforms. I show how my model provides a differential rate of cannibalization on different streaming platforms depending on the nature of promotion and content discovery on each of these platforms. I also demonstrate how the effect of cannibalization persists even when movies exit a streaming platform and how this effect varies with the nature of the promotion. I develop a model of promotion and subscription to explain the loss of revenue even after a movie exits the streaming platform. Using this model I estimate the promotional effect and the subscription effect on cannibalization.
This paper was presented at the Workshop on Information Systems and Economics, Seoul, South Korea, 2017
During the course of my research, I have acquired an array of skills from different disciplines. I have had the opportunity to conduct randomized experiments, collect, analyze and visualize data, process large-scale data. I have worked successfully with different industry partners and I have been able to identify questions that are both managerially important and also address gaps in the academic literature. I hope to continue working on exciting problems and contribute to the discussions around the impact of technology on online interactions.
My research in this area analyzes the influences of user-generated content on review portals and sharing economy platforms. In both of the papers described below, I conduct my empirical analyses using data obtained using large-scale data collection techniques from platforms like Yelp, Airbnb and TripAdvisor.
The Impact of Airbnb on Hotel Entry
In this paper with Prof. Beibei Li, Prof. Davide Proserpio & Prof. Rahul Telang, I study how the rise of sharing economy platforms impacts the entry of hotels to new markets in the context of Airbnb. To understand this, I exploit the temporal and geographical variability in Airbnb’s entry into a market and employ a difference-in-differences specification. I then build a structural entry model to understand the underlying mechanism of the business stealing effect of Airbnb. I find that an increase in of the number of Airbnb listings by 10\% of the total number of hotel rooms in a market causes a decline of 17.40\% in the entry of hotels belonging to the low-end category and a decline of 27\% in the entry of hotels belonging to the mid-range category. Based on this, I perform policy simulations to understand the heterogeneous impact of Airbnb entry in different geographical locations.
A version of this paper was presented at Workshop on Information Systems and Economics at Dallas, TX in 2015 and I was presented with the Best Student Paper – Runner Up award.