Why markdown as a pricing modality?

Markdown as a pricing modality is ubiquitous in retail whereas everyday low price (EDLP) remains relatively rare (despite its several advantages, such as simplicity). This paper explores whether and why retailers can use either of these pricing modalities as an effective defense against a competitor entering the market with the alternative pricing modality. Various studies have shown that consumers are strategic and heterogeneous in their valuation of a product. Consumers are also shown to be regret-prone, and anticipation of regret affects their purchase decisions. Consumers experience availability regret when they are unable to purchase products due to stockouts and high-price regret when they miss an opportunity to purchase products at low prices. Considering such factors, consumers decide whether, when, and from which retailer to purchase the product. In such a market environment, we find that the possible entry of a competitor should deter retailers from using the EDLP pricing modality but not markdown. We also identify a new reason for the markdown retailer to ration stock (in addition to the reason for discouraging consumers to wait for the markdown). In particular, we show that the markdown retailer can use inventory rationing to preclude a cutthroat competition and bankruptcy after the entry of an EDLP retailer. We also quantify how consumer regret affects both retailers' decisions and resulting profits. In particular, in a competitive market, the EDLP retailer cannot simply disregard consumers' availability and high-price regret (even when it stocks ample inventory and does not discount prices). We show that high-price regret and availability regret have complementary effects on the markdown retailer's rationing strategy and the EDLP retailer's price decision. Finally, using a proprietary price data set from a large department store, we show that ignoring regret factors causes the markdown retailer to leave up to 20% of its profits on the table. In addition, in a competitive market, the markdown retailer rations too aggressively when regret is ignored and, as a result, leaves some of the forgone profit to its competitor-the EDLP retailer. The retail industry is often characterized by its slim profit margins. In such an environment, the aforementioned results also suggest that retailers should seriously consider investing in developing the capacity to estimate and quantify the role of regret in consumers' purchase decisions

Dynamic pricing and inventory control of online retail of fresh agricultural products with forward purchase behavior

In this paper we formulate and analyze a novel model on a retailer’s inventory and pricing decisions for fresh agricultural products with consumers’ forward consuming behavior under online channel. We consider a dynamic stochastic setting, where every period consists of two stages, discounting pricing stage and regular sale stage. At the beginning of the period, the retailer decides how much new fresh agricultural products to order and sets discount price for leftover inventories from the previous period which will be disposed otherwise, and determines regular price for fresh products on the second stage, respectively. Since forward purchase consumers may buy the products during discount pricing stage, which may cannibalize future sales at regular price, the retailer needs to make a trade-off decision between regular price and discounting price. We bring forward a dynamic optimization model and use nonlinear programming method of Karush Kuhn Tucker condition to obtain the optimal dynamic strategy, which is comparatively analyzed to dominate the related static strategy. We also show that consumers forward buying behavior will negatively influence the retailer’s profit. When the price is set too low in regular or discounting sales, the profit will show an up-down trend if the inventory exceeds a certain threshold. Meanwhile, when fresh goods returns are allowed and resold in the secondary stage, the retailer’s profit will increase. We finally conduct numerical studies to further characterize the optimal policy, and to evaluate the loss of efficiency under static policies when compared to the optimal dynamic policy

Strategic Pricing in Service Industries.

This dissertation studies several strategic-level pricing decisions of firms that are motivated by recent changes of pricing policies in several service industries. It consists of three essays, each analyzing a different problem of selecting the optimal pricing policy for firms in a certain service industry. All three essays contribute to the arising areas of strategic-level revenue management and consumer-driven operations management. The first essay studies whether preventing resale of tickets benefits the ticket providers for sporting and entertainment events. Different from what common wis- dom may suggest, I find that this event organizers can benefit from reductions in consumers’ (and speculators’) transaction costs of resale in many cases. Further, I propose ticket options (where consumers would initially purchase an option to buy a ticket and then exercise at a later date) as a novel ticket pricing policy, and show that ticket options naturally reduce ticket resale and increase event organizers’ revenues. The second essay studies a conditional upgrade strategy that has recently become common in the travel industry. A consumer can accept a conditional upgrade offer after making a reservation and pay the fee to upgrade at check-in if the higher-quality product type is still available. I identify multiple benefits of conditional upgrades including demand expansion, price correction, and risk management. Moreover, I find that using conditional upgrades can generate higher revenues than having the ability to optimize product prices and use dynamic pricing. The third essay studies the firm’s strategic decision of whether to bundle the an- cillary service (e.g., baggage delivery) into the main service (e.g., air travel) or to unbundle and charge separate prices. I find that a firm that can price-discriminate when selling the main service should unbundle the ancillary service because con- sumers’ likelihoods of purchasing the ancillary service are low, or a large proportion of consumers are myopic instead of forward-looking, or the firm is dependent on inter- mediaries to make sales. I also find that the firm’s ability to price-discriminate when selling the main service reverses some classic bundling results for a uniform-pricing firm.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113531/1/cuiyao_1.pd

Coordinated planning in revenue management

Revenue management has been applied in service industries for more than thirty years. Since then, revenue management has been transferred to other industries like manufacturing or e- fulfillment. Short-term revenue management decisions are taken based on other, longer-term decisions such as decisions about actual capacity, segment-based prices or the price fences in place. While optimization approaches have been developed for each of these planning tasks in isolation, existing approaches typically do not consider interactions between planning tasks. This thesis considers coordinated planning in revenue management, that is the interaction of revenue management decisions with other planning tasks. First, we provide an overview of both the literature on coordinated decision making in the context of revenue management in different industries, and the literature on existing frameworks, which aim to structure the planning tasks around revenue management. We find that the planning tasks relevant to revenue management differ across the industries considered. Moreover, planning tasks are relevant on different hierarchical levels in different industries. We discuss an approach for an industry-independent framework. Based on the relevant planning tasks identified, we investigate the long-term performance of revenue management and therefore the integration of revenue management and customer relationship management. We present a stochastic dynamic programming approach, where the firm’s allocation decision impacts future customer demands by influencing the repurchase probabilities of customers, depending on whether their request has been accepted or rejected. We show that a protection level policy is not necessarily optimal in a two-period setting. In a numerical study, we find that the value of looking ahead in time is low on average but may be substantial in some scenarios. However, the benefit from regular demand updates is considerably higher than the additional value of looking ahead in time on average. Lastly, we investigate the interaction of revenue management and fencing. We account for the trade-off between price-driven demand leakage on the one hand and costs for fencing on the other hand. We show that fencing decisions have an impact on the optimal capacity allocation, but that this is not the case vice versa as the fencing decision does not depend on the allocation decision. Taking both decisions sequentially is therefore optimal. We extend our approach in order to account for additional stock-out-based demand substitution. Then, both decisions depend on each other and firms should take both decisions simultaneously

Bundling retailing under stochastic market

Ph.DDOCTOR OF PHILOSOPH

Data-driven methods for personalized product recommendation systems

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.Cataloged from PDF version of thesis.Includes bibliographical references.The online market has expanded tremendously over the past two decades across all industries ranging from retail to travel. This trend has resulted in the growing availability of information regarding consumer preferences and purchase behavior, sparking the development of increasingly more sophisticated product recommendation systems. Thus, a competitive edge in this rapidly growing sector could be worth up to millions of dollars in revenue for an online seller. Motivated by this increasingly prevalent problem, we propose an innovative model that selects, prices and recommends a personalized bundle of products to an online consumer. This model captures the trade-off between myopic profit maximization and inventory management, while selecting relevant products from consumer preferences. We develop two classes of approximation algorithms that run efficiently in real-time and provide analytical guarantees on their performance. We present practical applications through two case studies using: (i) point-of-sale transaction data from a large U.S. e-tailer, and, (ii) ticket transaction data from a premier global airline. The results demonstrate that our approaches result in significant improvements on the order of 3-7% lifts in expected revenue over current industry practices. We then extend this model to the setting in which consumer demand is subject to uncertainty. We address this challenge using dynamic learning and then improve upon it with robust optimization. We first frame our learning model as a contextual nonlinear multi-armed bandit problem and develop an approximation algorithm to solve it in real-time. We provide analytical guarantees on the asymptotic behavior of this algorithm's regret, showing that with high probability it is on the order of O([square root of] T). Our computational studies demonstrate this algorithm's tractability across various numbers of products, consumer features, and demand functions, and illustrate how it significantly out performs benchmark strategies. Given that demand estimates inherently contain error, we next consider a robust optimization approach under row-wise demand uncertainty. We define the robust counterparts under both polynomial and ellipsoidal uncertainty sets. Computational analysis shows that robust optimization is critical in highly constrained inventory settings, however the price of robustness drastically grows as a result of pricing strategies if the level of conservatism is too high.by Anna Papush.Ph. D

Are energy market integrations a green light for FDI?

This paper studies the effect of energy market integration (EMI) on foreign direct investment (FDI). EMIs diminish energy uncertainty and price volatility in the host country and affect FDI through two channels: first, by harmonizing energy prices and, second, by reducing price dispersion. FDI may, as a result, increase both within and outside the EMI area, through energy stability mechanisms and price mechanisms, respectively. An empirical application on a global dataset including bilateral FDI data, during 2003-2012, using the gravity equation, shows that the integration of Portugal and Spain's electricity market in 2007 increased the amount of FDI's participants. Additionally, a positive increase in FDI from neighboring countries (in this instance, France), albeit lower in magnitude, is observed

Essays on Sales Promotion and Consumer Decision Making Process

Sales promotion is a common tool used by retailers. However, even though it is commonly used in marketplace, sometimes it cannot achieve the results as retailers expect. Thus, the specific process of consumer decision making and factors that influence the effect of promotions on consumer behavior need further research. This thesis is composed of a literature review of sales promotions, consumers response to a specific sales promotion (Minimum Purchase Requirement deal), and consumers’ decision-making process when using different symbols to shop. The first chapter presents an integrative review to summarize past empirical or theoretical literature to provide a comprehensive understanding of consumers’ and marketers’ response towards monetary and non-monetary sales promotion. The second chapter examines how the consumer’s regulatory focus influences their shopping behavior when they choose to use Minimum Purchase Requirement deals. The third chapter focuses on the interaction effect between symbol usage and personality traits on consumer’s consideration set in online shopping. The relationship among the three chapters is that the first chapter reviews the existing research about sales promotion, and the second chapter focuses on a specific type of sales promotion and examines the effect of consumers’ goal pursuit strategy (regulatory focus) on purchasing behavior when using the deal. Even though the third chapter focuses on external triggered decision-making cues (symbol usage), psychological factors (personality traits) and decision-making process (consideration set) in shopping rather than sales promotions, it follows the same thread of research in consumer decision making process and factors influencing consumer behavior. The thesis aims to contribute to the literature of sales promotion, regulatory focus, priming effect of symbol usage and consumer decision making and behavior. The findings have important implications for marketing practitioners and retailers