Assistant Professor of Operations Management, HEC Paris
Research Interests: consumer privacy; operations management in interconnected information-based economies; social networks, platforms & marketplaces;
Teaching Interests: operations & supply chain management; platforms, networks, & data analytics; business model innovation.
Published & Forthcoming Papers
 The Use and Value of Social Network Information in Selective Selling of Exclusive Products - [Read full abstract] - [SSRN Link]
We consider the use and value of social network information in selectively selling goods and services whose value derives from exclusive ownership among network connections or friends. Our stylized model accommodates customers who are heterogeneous in their number of friends (degree)
and their proclivity for social comparisons (conspicuity). Firms with information on either (or both) of these characteristics can use it to make a product selectively available and increase their profits by better managing the exclusivity-sales trade-off. We find that the firm’s best
targets are high-conspicuity customers within intermediate-degree segments – in contrast with the practice of targeting high degree customers. We also find that information about degree is more valuable than information about conspicuity. Surprisingly, strategies informed only by degree
perform no worse than those informed by degree and conspicuity both, yet the opposite is not true. Customer self-selection is a perfect substitute for unavailable information on conspicuity, but there is no such recourse when degree information is unavailable. Examining alternate settings
(conformance social comparisons, functional value heterogeneity) suggests that there are two canonical categories of social information– less valuable information on characteristics where the firm’s preferred customers are also the most interested customers and more valuable information on
characteristics where they are not.
Network Science, 5.1 (2017): 108-139.
with Matt Leduc (Paris School of Economics)
What are incentives of the individuals (or firms) in a generic networked environment (e.g. social platform, supply chain) to invest in costly protection against both intrinsic (e.g. direct hacker attack, natural disaster) and networked (e.g. leakage of one’s sensitive information that exposes his/her social connections, cascading disruptions) risk?
We study the incentives that agents have to invest in costly protection against cascading failures in networked systems. Applications include vaccination, computer security and airport security. Agents are connected through a network and can fail either intrinsically or as a result of the failure of a subset of their neighbors. We characterize the equilibrium based on an agent’s failure probability and derive conditions under which equilibrium strategies are monotone in degree (i.e. in how connected an agent is on the network). We show that different kinds of applications (e.g. vaccination, airport security) lead to very different equilibrium patterns of investments in protection, with important welfare and risk implications. Our equilibrium concept is flexible enough to allow for comparative statics in terms of network properties and we show that it is also robust to the introduction of global externalities (e.g. price feedback, congestion).
Working Papers & Papers under Review
We study the incentives of a digital business to collect and protect users’ information. The information the business collects improves the service it provides to consumers, but it may also be accessed, at a cost, by third strategic parties in a way that harms users, imposing privacy costs. We characterize how the revenue model of the business shapes the equilibrium data policy. We compare the equilibrium data policy with the social optimum and show that a two-pronged policy, which combines a minimal data protection requirement with a tax proportional to the amount of data collected, restores efficiency.
 NEW Privacy-Preserving Personalized Revenue Management - [Read full abstract] - [SSRN Link]
with Yanzhe (Murray) Lei (Queen's University) and Sentao Miao (McGill University), under review, 2020
How privacy-preserving data-driven personalized decisions can be made in the context of operations and revenue management? What such privacy preservation entails for the decision makers?
This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend this classical framework of personalized pricing by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private -- an industry standard for privacy preservation. The two settings we consider are theoretically and practically relevant: central and local models of differential privacy, which differ in the strength of the privacy guarantees they provide. For both models, we develop privacy-preserving personalized pricing algorithms and derive the theoretical bounds on their performance as measured by the firm's revenue. Our analyses suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve central differential privacy at a cost of the same order as the "classical" loss in revenue due to estimation error. Comparing the two models, we conclude that local differentially private personalized pricing yields better privacy guarantees but leads to much greater revenue loss by the firm. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and real-world On-line Auto Lending (CPRM-12-001) data sets. Finally, we also apply our theoretical framework to the setting of personalized assortment optimization.
with Ming Hu (University of Toronto) and Jianfu Wang (City University of Hong Kong), under review, 2020
What are the consequences of providing control over their personal information to consumers in service settings?
We study customer-centric privacy management in service systems and explore the consequences of extended control over personal information by customers in such systems. Our stylized model of a service environment features a service provider and customers who are strategic in deciding whether to disclose personal information to the service provider – that is, customers' privacy or information disclosure strategy. A customer's service request can be one of two types, which affects service time but is unknown when customers commit to a privacy strategy. The service provider can discriminate among customers, based on their disclosed information, by offering different priorities. Our analysis yields three sets of main insights. First, when given control over their personal data, strategic customers do not always choose to withhold it. We find that control over information gives customers a tool they can use to hedge against the service provider's incentives, which might not be aligned with the interests of customers. Second, a customer's self-centered strategic decision may or may not be aligned with what is best for customers themselves. In fact, giving customers full control over information might backfire by leading to inferior system performance (i.e., longer average wait time) and hurting customers themselves. We demonstrate how a regulator can correct information disclosure inefficiencies through monetary incentives to customers and show that providing such incentives makes economic sense in some scenarios. Finally, the service provider itself can benefit from customers being in control of their personal information by enticing more customers joining the service. Our findings shed light on the market for pricing personal information in the service industry.
 Impact of Workforce Flexibility on Customer Satisfaction: Empirical Framework & Evidence from a Cleaning Services Platform - [Read full abstract] - [SSRN Link]
with Ekaterina Astashkina (University of Michigan) and Marat Salikhov (Yale, INSEAD), in preparation for re-submission, 2020
Should on-demand platforms allow their workers to choose tasks on their own?
Problem definition: Contrary to classic applications of matching theory, in most contemporary on-demand service platforms, matches can not be enforced because workers are flexible – they choose their tasks. Such flexibility makes it difficult to manage workers while keeping customers satisfied. We build a framework to compare platform matching policies with less flexible and more flexible workers, and empirically quantify by how much worker flexibility hurts customer satisfaction and customer equity.
Academic/Practical relevance: In academic literature, there is no established framework that allows for the comparison of matching policies in on-demand platforms. Further, the link between worker flexibility and customer satisfaction is understudied.
Methodology: We propose a tripartite framework for empirical evaluation and comparison of the operational policies with different degrees of worker flexibility. Step 1: Predictive modeling of customer satisfaction based on estimation of individual unobservable characteristics: customer difficulty and worker ability (item-response theory model). Step 2: Evaluation of the effect of matching policy (under a given level of flexibility) on customer satisfaction (bipartite matching). Step 3: Quantification of the associated monetary impact (customer lifetime value model).
Results: We apply our framework to the dataset of one of the world's largest on-demand platforms for residential cleanings. We find that customer difficulty and cleaner ability are good predictors of customer satisfaction. Granting full flexibility to workers reduces customer satisfaction by 3% and customer lifetime revenue by 0.2%. We propose a family of matching policies that provide sufficient flexibility to workers, while alleviating 75% of the detrimental effect of worker flexibility on customer satisfaction.
Managerial implications: Our results suggest that, in platforms with flexible workforce, the presence of worker and customer heterogeneity translates into matching inefficiency – the drop in customer satisfaction. Our empirical framework helps practitioners to decide on the right level of worker flexibility and the means for achieving it.
 Inventory Management for 1% Products - [Read full abstract]
with Elena Belavina (Cornell University), preliminary draft is available upon request, 2018
What is optimal long-term inventory policy when selling products to customers engaged in social comparisons?
Sociological trends have led to consumer's increasing willingness to purchase goods whose value lies partly in the exclusivity of their ownership. Firms selling these goods face a trade-off--while producing more allows for extracting more revenues, it also may compromise the product's reputation for exclusivity. In this study, we develop a dynamic game-theoretic model of reputations--a repeated game with long-lived, short-lived players, incomplete information, adverse selection, Bayesian updating of reputations and strategic memory. Our findings suggest a radical departure from the recommendations of the existing literature. Unlike the over-production equilibrium that is widely touted in the existing literature, our more realistic dynamic model suggests that in equilibrium the firm follows a non-stationary cyclic strategy that alternates scarcity phases with the overproduction phases. While the former is used as an exclusivity reputation building mechanism, the latter represents a reputation exploitation phase.
Work in Progress
 Validating a Business Idea in a Social Network: Who to Ask and What to Ask For?
with Evgeny Kagan (Johns Hopkins University), preparation of the experiment at the incubator for startups Station F (Paris), 2019
- Recipient of The HEC Foundation Research Grant: EUR 22,000
 The Value of Information in Online Ad Auctions
with Alex Nikulkov (Facebook Research), preliminary results are available upon request, 2019