Data-Driven Sports Ticket Pricing for Multiple Sales Channels with Heterogeneous Customers

2021 ◽  
Author(s):  
Hayri A. Arslan ◽  
Robert F. Easley ◽  
Ruxian Wang ◽  
Övünç Yılmaz
Keyword(s):  
Multinomial Logit ◽  
Problem Definition ◽  
Data Driven ◽  
Purchase Behavior ◽  
Sports Teams ◽  
Ticket Sales ◽  
Sales Channels ◽  
Price Optimization ◽  
Football Team ◽  
Managerial Implications

Problem Definition: We develop a framework to study purchase behavior from distinct segments of heterogeneous customers and to optimize prices for different policies in a sports ticket market with multiple sales channels. Academic/Practical Relevance: Sports teams face challenges in maintaining or increasing ticket sales levels. With the growth of analytics, they aim to implement data-driven pricing techniques to improve gate revenues; however, they do not have state-of-the-art demand estimation and price optimization tools that take into account the range of valuations across different seat sections and opponent match-ups. Methodology: Partnering with a college football team, we develop a data-driven pricing tool which (1) segments customers in two sales channels, using transaction-level data and anonymous customer profiles; (2) explores the decision-making process of different customers within these segments using the Multinomial Logit and Mixed Multinomial Logit frameworks; and (3) computes optimal or near-optimal prices subject to some business constraints enforced by the team management. In addition, our method takes the sequential arrivals of customers and the capacity constraints of seat categories into account. Results: Our estimation results show that customers differ significantly in their sensitivities to price and distance to the field within each segment, in addition to the differences across segments. We also observe that customers become less likely to choose a seat category as its remaining inventory falls below a certain point. Managerial Implications: By analyzing different policies, we show that price optimization could increase revenue by as much as 7.6%. In addition, better categorization of games and further refinement of seat category differentiation and related pricing may help further boost this figure up to 11.9%.

Data-Driven Sports Ticket Pricing for Multiple Sales Channels with Heterogeneous Customers

2019 ◽  
Author(s):  
Hayri Alper Arslan ◽  
Robert F. Easley ◽  
Ruxian Wang ◽  
Ovunc Yilmaz
Keyword(s):  
Data Driven ◽  
Ticket Pricing ◽  
Sales Channels

Partial Completion as a Nonprofit Strategy

2021 ◽  
Author(s):  
Can Zhang ◽  
Atalay Atasu ◽  
Karthik Ramachandran
Keyword(s):  
Nonprofit Organizations ◽  
Service Provision ◽  
Problem Definition ◽  
Product Service ◽  
Service Portfolio ◽  
Wide Range ◽  
Partial Completion ◽  
Innovative Solution ◽  
Managerial Implications ◽  
Level 3

Problem definition: Faced with the challenge of serving beneficiaries with heterogeneous needs and under budget constraints, some nonprofit organizations (NPOs) have adopted an innovative solution: providing partially complete products or services to beneficiaries. We seek to understand what drives an NPO’s choice of partial completion as a design strategy and how it interacts with the level of variety offered in the NPO’s product or service portfolio. Academic/practical relevance: Although partial product or service provision has been observed in the nonprofit operations, there is limited understanding of when it is an appropriate strategy—a void that we seek to fill in this paper. Methodology: We synthesize the practices of two NPOs operating in different contexts to develop a stylized analytical model to study an NPO’s product/service completion and variety choices. Results: We identify when and to what extent partial completion is optimal for an NPO. We also characterize a budget allocation structure for an NPO between product/service variety and completion. Our analysis sheds light on how beneficiary characteristics (e.g., heterogeneity of their needs, capability to self-complete) and NPO objectives (e.g., total-benefit maximization versus fairness) affect the optimal levels of variety and completion. Managerial implications: We provide three key observations. (1) Partial completion is not a compromise solution to budget limitations but can be an optimal strategy for NPOs under a wide range of circumstances, even in the presence of ample resources. (2) Partial provision is particularly valuable when beneficiary needs are highly heterogeneous, or beneficiaries have high self-completion capabilities. A higher self-completion capability generally implies a lower optimal completion level; however, it may lead to either a higher or a lower optimal variety level. (3) Although providing incomplete products may appear to burden beneficiaries, a lower completion level can be optimal when fairness is factored into an NPO’s objective or when beneficiary capabilities are more heterogeneous.

The Effects of Patent Extension and Take-Back Regulation on Green Pharmacy

2021 ◽  
Author(s):  
Tianqin Shi ◽  
Nicholas C. Petruzzi ◽  
Dilip Chhajed
Keyword(s):  
Pharmaceutical Industry ◽  
Social Welfare ◽  
Technical Information ◽  
Problem Definition ◽  
Theoretic Model ◽  
Innovative Company ◽  
Take Back ◽  
Starting Point ◽  
European Environmental Agency ◽  
Managerial Implications

Problem definition: The eco-toxicity arising from unused pharmaceuticals has regulators advocating the benign design concept of “green pharmacy,” but high research and development expenses can be prohibitive. We therefore examine the impacts of two regulatory mechanisms, patent extension and take-back regulation, on inducing drug manufacturers to go green. Academic/practical relevance: One incentive suggested by the European Environmental Agency is a patent extension for a company that redesigns its already patented pharmaceutical to be more environmentally friendly. This incentive can encourage both the development of degradable drugs and the disclosure of technical information. Yet, it is unclear how effective the extension would be in inducing green pharmacy and in maximizing social welfare. Methodology: We develop a game-theoretic model in which an innovative company collects monopoly profits for a patented pharmaceutical but faces competition from a generic rival after the patent expires. A social-welfare-maximizing regulator is the Stackelberg leader. The regulator leads by offering a patent extension to the innovative company while also imposing take-back regulation on the pharmaceutical industry. Then the two-profit maximizing companies respond by setting drug prices and choosing whether to invest in green pharmacy. Results: The regulator’s optimal patent extension offer can induce green pharmacy but only if the offer exceeds a threshold length that depends on the degree of product differentiation present in the pharmaceutical industry. The regulator’s correspondingly optimal take-back regulation generally prescribes a required collection rate that decreases as its optimal patent extension offer increases, and vice versa. Managerial implications: By isolating green pharmacy as a potential target to address pharmaceutical eco-toxicity at its source, the regulatory policy that we consider, which combines the incentive inherent in earning a patent extension on the one hand with the penalty inherent in complying with take-back regulation on the other hand, serves as a useful starting point for policymakers to optimally balance economic welfare considerations with environmental stewardship considerations.

Truthful Mechanisms for Medical Surplus Product Allocation

2020 ◽  
Vol 22 (4) ◽  
pp. 735-753 ◽  
Author(s):  
Can Zhang ◽  
Atalay Atasu ◽  
Turgay Ayer ◽  
L. Beril Toktay
Keyword(s):  
Private Information ◽  
Value Added ◽  
Information Provision ◽  
Problem Definition ◽  
Preference Ranking ◽  
Additional Increase ◽  
Healthcare Facilities ◽  
Selection Decisions ◽  
Managerial Implications ◽  
Recipient Selection

Problem definition: We analyze a resource allocation problem faced by medical surplus recovery organizations (MSROs) that recover medical surplus products to fulfill the needs of underserved healthcare facilities in developing countries. The objective of this study is to identify implementable strategies to support recipient selection decisions to improve MSROs’ value provision capability. Academic/practical relevance: MSRO supply chains face several challenges that differ from those in traditional for-profit settings, and there is a lack of both academic and practical understanding of how to better match supply with demand in this setting where recipient needs are typically private information. Methodology: We propose a mechanism design approach to determine which recipient to serve at each shipping opportunity based on recipients’ reported preference rankings of different products. Results: We find that when MSRO inventory information is shared with recipients, the only truthful mechanism is random selection among recipients, which defeats the purpose of eliciting information. Subsequently, we show that (1) eliminating inventory information provision enlarges the set of truthful mechanisms, thereby increasing the total value provision; and (2) further withholding information regarding other recipients leads to an additional increase in total value provision. Finally, we show that under a class of implementable mechanisms, eliciting recipient valuations has no value added beyond eliciting preference rankings. Managerial implications: (1) MSROs with large recipient bases and low inventory levels can significantly improve their value provision by appropriately determining the recipients to serve through a simple scoring mechanism; (2) to truthfully elicit recipient needs information to support the recipient selection decisions, MSROs should withhold inventory and recipient-base information; and (3) under a set of easy-to-implement scoring mechanisms, it is sufficient for MSROs to elicit recipients’ preference ranking information. Our findings have already led to a change in the practice of an award-winning MSRO.

scholarly journals A data-driven approach to measuring epidemiological susceptibility risk around the world

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alessandro Bitetto ◽  
Paola Cerchiello ◽  
Charilaos Mertzanis
Keyword(s):  
Factor Model ◽  
Principal Component ◽  
Data Driven ◽  
Dynamic Factor ◽  
Country Level ◽  
Epidemiological Risk ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Total Variability ◽  
Pca Method

AbstractEpidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

Managing Congestion in Matching Markets

2021 ◽  
Author(s):  
Nick Arnosti ◽  
Ramesh Johari ◽  
Yash Kanoria
Keyword(s):  
Problem Definition ◽  
Theoretic Model ◽  
Matching Markets ◽  
Contact Potential ◽  
Managerial Implications ◽  
The Commons ◽  
Game Theoretic ◽  
Face Search ◽  
Uniqueness Of Equilibrium ◽  
Never Screened

Problem definition: Participants in matching markets face search and screening costs when seeking a match. We study how platform design can reduce the effort required to find a suitable partner. Practical/academic relevance: The success of matching platforms requires designs that minimize search effort and facilitate efficient market clearing. Methodology: We study a game-theoretic model in which “applicants” and “employers” pay costs to search and screen. An important feature of our model is that both sides may waste effort: Some applications are never screened, and employers screen applicants who may have already matched. We prove existence and uniqueness of equilibrium and characterize welfare for participants on both sides of the market. Results: We identify that the market operates in one of two regimes: It is either screening-limited or application-limited. In screening-limited markets, employer welfare is low, and some employers choose not to participate. This occurs when application costs are low and there are enough employers that most applicants match, implying that many screened applicants are unavailable. In application-limited markets, applicants face a “tragedy of the commons” and send many applications that are never read. The resulting inefficiency is worst when there is a shortage of employers. We show that simple interventions—such as limiting the number of applications that an individual can send, making it more costly to apply, or setting an appropriate market-wide wage—can significantly improve the welfare of agents on one or both sides of the market. Managerial implications: Our results suggest that platforms cannot focus exclusively on attracting participants and making it easy to contact potential match partners. A good user experience requires that participants not waste effort considering possibilities that are unlikely to be available. The operational interventions we study alleviate congestion by ensuring that potential match partners are likely to be available.

Incentives for Shared Services: Multiserver Queueing Systems with Priorities

2021 ◽  
Author(s):  
Hanlin Liu ◽  
Yimin Yu
Keyword(s):  
Cooperative Game ◽  
Cost Allocation ◽  
Service Providers ◽  
Queueing Systems ◽  
Service Level ◽  
Problem Definition ◽  
Shared Services ◽  
Scheduling Policy ◽  
Shared Service ◽  
Managerial Implications

Problem definition: We study shared service whereby multiple independent service providers collaborate by pooling their resources into a shared service center (SSC). The SSC deploys an optimal priority scheduling policy for their customers collectively by accounting for their individual waiting costs and service-level requirements. We model the SSC as a multiclass [Formula: see text] queueing system subject to service-level constraints. Academic/practical relevance: Shared services are increasingly popular among firms for saving operational costs and improving service quality. One key issue in fostering collaboration is the allocation of costs among different firms. Methodology: To incentivize collaboration, we investigate cost allocation rules for the SSC by applying concepts from cooperative game theory. Results: To empower our analysis, we show that a cooperative game with polymatroid optimization can be analyzed via simple auxiliary games. By exploiting the polymatroidal structures of the multiclass queueing systems, we show when the games possess a core allocation. We explore the extent to which our results remain valid for some general cases. Managerial implications: We provide operational insights and guidelines on how to allocate costs for the SSC under the multiserver queueing context with priorities.

Joint Product Framing (Display, Ranking, Pricing) and Order Fulfillment Under the Multinomial Logit Model for E-Commerce Retailers

2021 ◽  
Author(s):  
Yanzhe (Murray) Lei ◽  
Stefanus Jasin ◽  
Joline Uichanco ◽  
Andrew Vakhutinsky
Keyword(s):  
Large Scale ◽  
Matrix Decomposition ◽  
Multinomial Logit Model ◽  
Problem Definition ◽  
Order Fulfillment ◽  
Cardinality Constraints ◽  
Joint Product ◽  
Shipping Costs ◽  
Managerial Implications ◽  
Randomization Scheme

Problem definition: We study a joint product framing and order fulfillment problem with both inventory and cardinality constraints faced by an e-commerce retailer. There is a finite selling horizon and no replenishment opportunity. In each period, the retailer needs to decide how to “frame” (i.e., display, rank, price) each product on his or her website as well as how to fulfill a new demand. Academic/practical relevance: E-commerce retail is known to suffer from thin profit margins. Using the data from a major U.S. retailer, we show that jointly planning product framing and order fulfillment can have a significant impact on online retailers’ profitability. This is a technically challenging problem as it involves both inventory and cardinality constraints. In this paper, we make progress toward resolving this challenge. Methodology: We use techniques such as randomized algorithms and graph-based algorithms to provide a tractable solution heuristic that we analyze through asymptotic analysis. Results: Our proposed randomized heuristic policy is based on the solution of a deterministic approximation to the stochastic control problem. The key challenge is in constructing a randomization scheme that is easy to implement and that guarantees the resulting policy is asymptotically optimal. We propose a novel two-step randomization scheme based on the idea of matrix decomposition and a rescaling argument. Managerial implications: Our numerical tests show that the proposed policy is very close to optimal, can be applied to large-scale problems in practice, and highlights the value of jointly optimizing product framing and order fulfillment decisions. When inventory across the network is imbalanced, the widespread practice of planning product framing without considering its impact on fulfillment can result in high shipping costs, regardless of the fulfillment policy used. Our proposed policy significantly reduces shipping costs by using product framing to manage demand so that it occurs close to the location of the inventory.

Dynamic Type Matching

2021 ◽  
Author(s):  
Ming Hu ◽  
Yun Zhou
Keyword(s):  
Sufficient Conditions ◽  
Sharing Economy ◽  
Problem Definition ◽  
Periodic Review ◽  
Optimal Matching ◽  
Demand And Supply ◽  
Holding Costs ◽  
Current Period ◽  
Dynamic Type ◽  
Managerial Implications

Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of demand and supply types, and a reward for each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform decides on the optimal matching policy to maximize the expected total discounted rewards, given that unmatched demand and supply may incur waiting or holding costs, and will be fully or partially carried over to the next period. Academic/practical relevance: The problem is crucial to many intermediaries who manage matchings centrally in a sharing economy. Methodology: We formulate the problem as a dynamic program. We explore the structural properties of the optimal policy and propose heuristic policies. Results: We provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show that those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. Managerial implications: The priority property simplifies the matching decision within a period, and the trade-off reduces to a choice between matching in the current period and that in the future. Then the optimal matching policy has a match-down-to structure when considering a specific pair of demand and supply types in the priority hierarchy.

Product Flexibility Strategy Under Supply and Demand Risk

2021 ◽  
Author(s):  
Yimin Wang ◽  
Scott Webster
Keyword(s):  
Supply Chain ◽  
Supply And Demand ◽  
Problem Definition ◽  
Base Case ◽  
Supply Network ◽  
Aggregation Effect ◽  
Risk Aggregation ◽  
Managerial Implications ◽  
Product Flexibility ◽  
Global Uncertainty

Problem definition: With heightened global uncertainty, supply chain managers are under increasing pressure to craft strategies that accommodate both supply and demand risks. Although product flexibility is a well-understood strategy to accommodate risk, there is no clear guidance on the optimal flexibility configuration of a supply network that comprises both unreliable primary suppliers and reliable backup suppliers. Academic/practical relevance: Existing literature examines the value of flexibility with primary and backup suppliers independently. For a risk-neutral firm, research shows that (a) incorporating flexibility in a primary supplier by replacing two dedicated ones (in absence of backup supply) is always beneficial and that (b) adding flexibility to a reliable backup supplier (in absence of product flexibility in primary suppliers) is always valuable. It is unclear, however, how flexibility should be incorporated into a supply network with both unreliable primary suppliers and reliable backup suppliers. This research studies whether flexibility should be incorporated in a primary supplier, a backup supplier, or both. Methodology: We develop a normative model to analyze when flexibility benefits and when it hurts. Results: Compared with a base case of no flexibility, we prove that incorporating flexibility in either primary or backup suppliers is always beneficial. However, incorporating flexibility in both primary and backup suppliers can be counterproductive because the supply chain performance can decline with saturated flexibility, even if flexibility is costless. A key reason is that the risk-aggregation effect of consolidating flexibility in an unreliable supplier becomes more salient when flexibility is already embedded in a backup supplier. Managerial implications: This research refines the existing understanding of flexibility by illustrating that flexibility is not always beneficial. When there is a choice, a firm should prioritize incorporating flexibility in a reliable backup supplier.

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