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  • 1
    Online Resource
    Online Resource
    The Value of Personalized Pricing
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2021
    In:  Management Science Vol. 67, No. 10 ( 2021-10), p. 6055-6070
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 67, No. 10 ( 2021-10), p. 6055-6070
    Abstract: Increased availability of high-quality customer information has fueled interest in personalized pricing strategies, that is, strategies that predict an individual customer’s valuation for a product and then offer a price tailored to that customer. Although the appeal of personalized pricing is clear, it may also incur large costs in the forms of market research, investment in information technology and analytics expertise, and branding risks. In light of these trade-offs, our work studies the value of personalized pricing strategies over a simple single-price strategy. We first provide closed-form lower and upper bounds on the ratio between the profits of an idealized personalized pricing strategy (first-degree price discrimination) and a single-price strategy. Our bounds depend on simple statistics of the valuation distribution and shed light on the types of markets for which personalized pricing has little or significant potential value. Second, we consider a feature-based pricing model where customer valuations can be estimated from observed features. We show how to transform our aforementioned bounds into lower and upper bounds on the value of feature-based pricing over single pricing depending on the degree to which the features are informative for the valuation. Finally, we demonstrate how to obtain sharper bounds by incorporating additional information about the valuation distribution (moments or shape constraints) by solving tractable linear optimization problems. This paper was accepted by David Simchi-Levi, revenue management and market analytics.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2020.3821
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2021
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 2
    Online Resource
    Online Resource
    Price Discrimination with Fairness Constraints
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2022
    In:  Management Science Vol. 68, No. 12 ( 2022-12), p. 8536-8552
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 68, No. 12 ( 2022-12), p. 8536-8552
    Abstract: Price discrimination strategies, which offer different prices to customers based on differences in their valuations, have become common practice. Although it allows sellers to increase their profits, it also raises several concerns in terms of fairness (e.g., by charging higher prices (or denying access) to protected minorities in case they have higher (or lower) valuations than the general population). This topic has received extensive attention from media, industry, and regulatory agencies. In this paper, we consider the problem of setting prices for different groups under fairness constraints. We first propose four definitions: fairness in price, demand, consumer surplus, and no-purchase valuation. We prove that satisfying more than one of these fairness constraints is impossible even under simple settings. We then analyze the pricing strategy of a profit-maximizing seller and the impact of imposing fairness on the seller’s profit, consumer surplus, and social welfare. Under a linear demand model, we find that imposing a small amount of price fairness increases social welfare, whereas too much price fairness may result in a lower welfare relative to imposing no fairness. On the other hand, imposing fairness in demand or consumer surplus always decreases social welfare. Finally, no-purchase valuation fairness always increases social welfare. We observe similar patterns under several extensions and for other common demand models numerically. Our results and insights provide a first step in understanding the impact of imposing fairness in the context of discriminatory pricing. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: A. N. Elmachtoub was supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grants 1763000 and 1944428]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4317 .
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2022.4317
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2022
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 3
    Online Resource
    Online Resource
    Retailing with Opaque Products
    Elsevier BV ; 2015
    In:  SSRN Electronic Journal
    Details
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    URL: Article
    DOI: 10.2139/ssrn.2659211
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
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  • 4
    Online Resource
    Online Resource
    Queuing safely for elevator systems amidst a pandemic
    SAGE Publications ; 2022
    In:  Production and Operations Management Vol. 31, No. 5 ( 2022-05), p. 2306-2323
    Details
    In: Production and Operations Management, SAGE Publications, Vol. 31, No. 5 ( 2022-05), p. 2306-2323
    Abstract: The requirement of social distancing during the COVID‐19 pandemic has presented significant challenges for high‐rise buildings, which heavily rely on elevators for vertical transportation. In particular, the need for social distancing has reduced elevator capacity typically by at least two‐thirds or as much as over [Formula: see text] of the normal amount. This reduction is a serious concern, as reduced elevator capacities cause large queues to build up in lobbies, which makes social distancing difficult and results in large wait times. The objective of this study is to safely manage the elevator queues by proposing simple, technology‐free interventions that drastically reduce the waiting time and length of lobby queues. We use mathematical modeling, epidemiological expertise, and simulation to design and evaluate our interventions. The key idea is to explicitly or implicitly group passengers that are going to the same floor into the same elevator as much as possible. In the Cohorting intervention, we attempt to find passengers going to the same floor as the first person in the queue. In the Queue Splitting intervention, we create a different queue for different groups of floors. Based on simulation and analytical findings, Cohorting and Queue Splitting can significantly reduce queue length and wait time, while also maintaining safety from viral transmission in otherwise crowded elevators, building lobbies, and entrances. These interventions are generally accessible for many buildings since they do not require programming the elevators, and rely on only using signage and/or a queue manager to guide passengers.
    Type of Medium: Online Resource
    ISSN: 1059-1478 , 1937-5956
    URL: Article
    DOI: 10.1111/poms.13686
    RVK:
    QA 10000
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2151364-8
    detail.hit.zdb_id: 1108460-1
    SSG: 3,2
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  • 5
    Online Resource
    Online Resource
    The Value of Personalized Pricing
    Elsevier BV ; 2018
    In:  SSRN Electronic Journal
    Details
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    URL: Article
    DOI: 10.2139/ssrn.3127719
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
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  • 6
    Online Resource
    Online Resource
    The submodular joint replenishment problem
    Springer Science and Business Media LLC ; 2016
    In:  Mathematical Programming Vol. 158, No. 1-2 ( 2016-7), p. 207-233
    Details
    In: Mathematical Programming, Springer Science and Business Media LLC, Vol. 158, No. 1-2 ( 2016-7), p. 207-233
    Type of Medium: Online Resource
    ISSN: 0025-5610 , 1436-4646
    URL: Article
    DOI: 10.1007/s10107-015-0920-3
    RVK:
    SA 6810
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
    detail.hit.zdb_id: 1463397-8
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  • 7
    Online Resource
    Online Resource
    From Cost Sharing Mechanisms to Online Selection Problems
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2015
    In:  Mathematics of Operations Research Vol. 40, No. 3 ( 2015-08), p. 542-557
    Details
    In: Mathematics of Operations Research, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 40, No. 3 ( 2015-08), p. 542-557
    Abstract: We consider a general class of online optimization problems, called online selection problems, where customers arrive sequentially, and one has to decide upon arrival whether to accept or reject each customer. If a customer is rejected, then a rejection cost is incurred. The accepted customers are served with minimum possible cost, either online or after all customers have arrived. The goal is to minimize the total production costs for the accepted customers plus the rejection costs for the rejected customers. These selection problems are related to online variants of offline prize collecting combinatorial optimization problems that have been widely studied in the computer science literature. In this paper, we provide a general framework to develop online algorithms for this class of selection problems. In essence, the algorithmic framework leverages any cost sharing mechanism with certain properties into a poly-logarithmic competitive online algorithm for the respective problem; the competitive ratios are shown to be near-optimal. We believe that the general and transparent connection we establish between cost sharing mechanisms and online algorithms could lead to additional online algorithms for problems beyond the ones studied in this paper.
    Type of Medium: Online Resource
    ISSN: 0364-765X , 1526-5471
    URL: Article
    DOI: 10.1287/moor.2014.0684
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2015
    detail.hit.zdb_id: 2004273-5
    detail.hit.zdb_id: 195683-8
    SSG: 3,2
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  • 8
    Online Resource
    Online Resource
    Loot Box Pricing and Design
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2021
    In:  Management Science Vol. 67, No. 8 ( 2021-08), p. 4809-4825
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 67, No. 8 ( 2021-08), p. 4809-4825
    Abstract: In the online video game industry, a significant portion of the revenue is generated from microtransactions, where a small amount of real-world currency is exchanged for virtual items to be used in the game. One popular way to conduct microtransactions is via a loot box, which is a random allocation of virtual items whose contents are not revealed until after purchase. In this work, we consider how to optimally price and design loot boxes from the perspective of a revenue-maximizing video game company and analyze customer surplus under such selling strategies. Our paper provides the first formal treatment of loot boxes, with the aim to provide customers, companies, and regulatory bodies with insights into this popular selling strategy. We consider two types of loot boxes: a traditional one where customers can receive (unwanted) duplicates and a unique one where customers are guaranteed to never receive duplicates. We show that as the number of virtual items grows large, the unique box strategy is asymptotically optimal among all possible strategies, whereas the traditional box strategy only garners 36.7% of the optimal revenue. On the other hand, the unique box strategy leaves almost zero customer surplus, whereas the traditional box strategy leaves positive surplus. Further, when designing traditional and unique loot boxes, we show it is asymptotically optimal to allocate the items uniformly, even when the item valuation distributions are heterogeneous. We also show that, when the seller purposely misrepresents the allocation probabilities, their revenue may increase significantly, and thus, strict regulation is needed. Finally, we show that, even if the seller allows customers to salvage unwanted items, then the customer surplus can only increase by at most 1.4%. This paper was accepted by Victor Martinez-de-Albeniz, operations management.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2020.3748
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2021
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 9
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    Online Resource
    Smart “Predict, then Optimize”
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2022
    In:  Management Science Vol. 68, No. 1 ( 2022-01), p. 9-26
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 68, No. 1 ( 2022-01), p. 9-26
    Abstract: Many real-world analytics problems involve two significant challenges: prediction and optimization. Because of the typically complex nature of each challenge, the standard paradigm is predict-then-optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in the downstream optimization problem. In contrast, we propose a new and very general framework, called Smart “Predict, then Optimize” (SPO), which directly leverages the optimization problem structure—that is, its objective and constraints—for designing better prediction models. A key component of our framework is the SPO loss function, which measures the decision error induced by a prediction. Training a prediction model with respect to the SPO loss is computationally challenging, and, thus, we derive, using duality theory, a convex surrogate loss function, which we call the SPO+ loss. Most importantly, we prove that the SPO+ loss is statistically consistent with respect to the SPO loss under mild conditions. Our SPO+ loss function can tractably handle any polyhedral, convex, or even mixed-integer optimization problem with a linear objective. Numerical experiments on shortest-path and portfolio-optimization problems show that the SPO framework can lead to significant improvement under the predict-then-optimize paradigm, in particular, when the prediction model being trained is misspecified. We find that linear models trained using SPO+ loss tend to dominate random-forest algorithms, even when the ground truth is highly nonlinear. This paper was accepted by Yinyu Ye, optimization. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2020.3922
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2020.3922
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2022
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 10
    Online Resource
    Online Resource
    The Power of Opaque Products in Pricing
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2021
    In:  Management Science Vol. 67, No. 8 ( 2021-08), p. 4686-4702
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 67, No. 8 ( 2021-08), p. 4686-4702
    Abstract: We study the power of selling opaque products, that is, products where a feature (such as color) is hidden from the customer until after purchase. Opaque products, which are sold with a price discount, have emerged as a powerful vehicle to increase revenue for many online retailers and service providers that offer horizontally differentiated items. In the opaque selling models we consider, all of the items are sold at a single common price alongside opaque products that may correspond to various subsets of the items. We consider two types of customers, risk-neutral ones, who assume they will receive a truly random item of the opaque product, and pessimistic ones, who assume they will receive their least favorite item of the opaque product. We benchmark opaque selling against two common selling strategies: discriminatory pricing, where one explicitly charges different prices for each item, and single pricing, where a single price is charged for all the items. We give a sharp characterization of when opaque selling outperforms discriminatory pricing; namely, this result holds for situations where all customers are pessimistic or the item valuations are supported on two points. In the latter case, we also show that opaque selling with just one opaque product guarantees at least 71.9% of the revenue from discriminatory pricing. We then provide upper bounds on the potential revenue increase from opaque selling strategies over single pricing and describe cases where the increase can be significantly more than that of discriminatory pricing. Finally, we provide pricing algorithms and conduct an extensive numerical study to assess the power of opaque selling for a variety valuation distributions and model extensions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2020.3750
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2021
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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