The Secret to Getting Consumers to Trust Personalized Recommendations
Columbia Business School researchers discover that the amount of variety in a consumer’s past purchases predicts their openness to algorithm-based recommendations.
Columbia Business School researchers discover that the amount of variety in a consumer’s past purchases predicts their openness to algorithm-based recommendations.
Research from Columbia Business School Reveals How Consumers Perceive Pricing Set by Algorithms
A field experiment by Professor Kinshuk Jerath and his co-researchers shows that an optimal level of ‘retail media’ benefits both marketplaces and consumers.
Columbia Business School Research Finds Political Campaigns Successfully Locked People into Weekly Recurring Donations By Creating Hidden Pre-Checked Boxes on Campaign Websites
LinkedIn Co-founder Reid Hoffman shares parallels between his own career and AI’s boom during a conversation with CBS Dean Costis Maglaras.
Columbia Business School Research Suggests Companies Can Reduce Consumer Regret by Promoting Both Highly Rated Products and Newer Products
Five Studies by Columbia Business School Faculty Present Insights into Pivotal Delivery Times, Emerging Fashion Trends, Advertising Strategies, and Customer Behavior
From dating to organ donation, this year's Marketplace Innovation Workshop explores how advances in technology can drive social good.
Hongyao Ma is an Assistant Professor of Business in the Decision, Risk, and Operations division at Columbia Business School. Her research is situated at the interface of computer science, economics and operations, with a particular focus on market design. Hongyao completed her Ph.D. in Computer Science at Harvard University in 2019, and worked as a postdoctoral researcher at Uber and then Caltech during 2019-2020. She obtained her M.S. in 2014 at Harvard, and B.E. in 2012 at Xi'an Jiaotong University, both in Electrical Engineering.
Miklos Sarvary is the Carson Family Professor of Business and the faculty lead for the Media and Technology Program at Columbia Business School. Miklos' broad research agenda focuses on media and information marketing. His most recent papers study ad blocking, online marketplace design and content bundling on social media. Previously, he worked on user-generated content, online/mobile advertising and media and telecommunications competition.
Omid Malekan is the the author of several books, including Re-Architecting Trust: the Curse of History and the Crypto Cure for Money, Markets and Platforms as well as The Story of the Blockchain: A Beginner’s Guide to the Technology That Nobody Understands. An eight-year veteran of the crypto industry, his writing on this and related topics has appeared in the New York Times, Wall Street Journal, Financial Times, Spectator Magazine, and his own blog on Medium.
Laura Doval is the Chong Khoon Lin Professor of Business in the Economics Division at Columbia Business School. She is a microeconomic theorist working in the areas of mechanism design, market design, and information economics. Her work has been published in Econometrica and the Journal of Political Economy.
Professor Doval is also a Research Affiliate at the Centre for Economic Policy Research (CEPR) (Industrial Organization), and an Associate Editor at Theoretical Economics, the Journal of the European Economic Association, and Economic Theory.
Hannah Li is an Assistant Professor in the Decision, Risk, and Operations division at Columbia Business School. Her research focuses on developing data science methods for social systems--marketplaces, education systems, and online platforms. Her research combines techniques from operations research, statistics, and economics to develop theoretical insights for practically motivated problems. She informs her work with industry experience, working for and collaborating with large online platforms.
Bo Cowgill is an Assistant Professor at Columbia Business School, a research affiliate at CESifo, and a Term Member of the Council on Foreign Relations. His elective, People Analytics and Strategy, won The Aspen Institute's 2019 Ideas Worth Teaching Award. He was also named to Poets and Quants’ 2020 list of Best 40 Business School Professors Under 40.
Kinshuk Jerath is the Arthur F. Burns Chair of Free and Competitive Enterprise, Professor of Business in the Marketing division at Columbia Business School. He is also the Chair of the Marketing Division. His research is in technology-enabled marketing, primarily in online advertising, online and offline retailing, sales force management and customer management. His research has appeared in top-tier marketing and operations management journals, such as Marketing Science, Journal of Marketing Research, Management Science and Operations Research.
Santiago R. Balseiro is an Associate Professor of Business at the Graduate School of Business, Columbia University. He is the Research Director of the Deming Center and a part-time research scientist at Google Research. He teaches the core MBA classes Business Analytics and Operations Management, and the core Ph.D. class Foundations of Optimization.
Professor Jian Li joined Columbia Business School in 2021. She graduated with a PhD from the Joint Program of Financial Economics at the University of Chicago. Her research interest lies at the intersection of macroeconomics and finance. She is particularly interested in how financial intermediaries affect the real economy and how different types of financial institutions can contribute to financial instability.
Malek Ben Sliman is an Adjunct Associate Professor of Business at Columbia Business School in the Marketing Department. Malek’s research interests lie in the application of machine learning, computer vision and NLP tools in the context of art valuation, social networks, marketing analytics and online retailing. As a practitioner, he had previously worked at Sotheby’s where he built models to track the evolution of artists’ prestige over time and to automatically predict the price of art during auctions.
Omar Besbes's primary research interests are in the area of data-driven decision-making with a focus on applications in e-commerce, pricing and revenue management, online advertising, operations management and general service systems. His research has been recognized by multiple prizes, including the 2019 Frederick W. Lanchester Prize, the 2017 M&SOM society Young Scholar Prize, the 2013 M&SOM best paper award and the 2012 INFORMS Revenue Management and Pricing Section prize. He serves on the editorial boards of Management Science and Operations Research.
One of the most crucial aspects and significant levers that gaming companies possess in designing digital games is setting the level of difficulty, which essentially regulates the user’s ability to progress within the game. This aspect is particularly significant in free-to-play (F2P) games, where the paid version often aims to enhance the player’s experience and to facilitate faster progression.
In the months before the 2020 U.S. election, several political campaign websites added prechecked boxes (defaults), automatically making all donations into recurring weekly contributions unless donors unchecked them. Since these changes occurred at different times for different campaigns, we use a staggered difference-in-differences design to measure the causal effects of defaults on donors’ behavior. We estimate that defaults increased campaign donations by over $43 million while increasing requested refunds by almost $3 million.
Housing affordability has become the main policy challenge for most large cities in the world. Zoning, rent control, housing vouchers, and tax credits are the main levers employed by policy makers. We build a new dynamic stochastic spatial equilibrium model to evaluate the effect of these policies on house prices, rents, residential construction, labor supply, output, income and wealth inequality, as well as the location decision of households within the city. The analysis incorporates risk, wealth effects, and resident landlords.
When an inventory manager attempts to construct probabilistic models of demand based on past data, demand samples are almost never available: only sales data can be used. This limitation, referred to as demand censoring, introduces an exploration-exploitation trade-off as the ordering decisions impact the information collected. Much of the literature has sought to understand how operational decisions should be modified to incorporate this trade-off. We ask an even more basic question: when does the exploration-exploitation trade-off matter?
We propose a new valuation method for private equity investments. First, we construct a cash-flow replicating portfolio for the private investment, using cash-flows on various listed equity and fixed income instruments. The second step values the replicating portfolio using a flexible asset pricing model that accurately prices the systematic risk in listed equity and fixed income instruments of different horizons.
The initial phase of the COVID-19 pandemic was characterized by voluminous, highly negative news coverage. Markets overreacted to uninformative news, and reacted more to news during high volatility periods. News coverage responded to lagged market activity, and causally impacted contemporaneous returns. The early part of the pandemic was characterized by pronounced feedback between news and markets. I propose a structural break test to identify the presence and end of such feedback episodes. This one ended in March 2020, which was knowable by the end of April.
Pricing is central to many industries and academic disciplines ranging from Operations Research to Computer Science and Economics. In the present paper, we study data-driven optimal pricing in low informational environments. We analyze the following fundamental problem: how should a decision-maker optimally price based on a single sample of the willingness-to-pay (WTP) of customers. The decision-maker's objective is to select a general pricing policy with maximum competitive ratio when the WTP distribution is only known to belong to some broad set.
A buyer wishes to purchase a durable good from a seller who in each period chooses a mechanism under limited commitment. The buyer's valuation is binary and fully persistent. We show that posted prices implement all equilibrium outcomes of an infinite-horizon, mechanism selection game. Despite being able to choose mechanisms, the seller can do no better and no worse than if he chose prices in each period, so that he is subject to Coase's conjecture. Our analysis marries insights from information and mechanism design with those from the literature on durable goods.