Retailer Dynamic Pricing And Pass Through Behavior

Digital technologies are driving the application of dynamic pricing. Today, this pricing strategy is used not only for perishable products such as flights or hotel rooms, but for almost any product or service category. With dynamic pricing, retailers frequently adjust their prices over time to respond to factors such as demand, their supply and that of competitors, or the time of sale. Additionally, dynamic pricing allows retailers to take advantage of a large share of consumers' willingness to pay while avoiding losses from unsold products. Ultimately, this can lead to an increase in revenue and profit. However, the application of dynamic pricing comes with great challenges. In addition to the technological implementation, companies have to take into account that dynamic pricing can cause complex and unintended behavioral consequences on the consumer side. The key objective of this dissertation is to provide a deeper understanding of the impact of dynamic pricing on consumer behavior. To this end, this dissertation presents insights from four perspectives. First, how reference prices as a critical component in purchase decisions are operationalized. Second, how customers search for products priced dynamically, differentiated by business and private customers, as well as by different devices used for the search. Third, whether and how dynamic pricing influences the impact of internal reference prices on purchase decisions. Finally, this dissertation demonstrates that consumers perceive price changes as personalized in different purchase contexts, leading to reduced perceptions of fairness and undesirable behavioral consequences.

This paper studies how prices comove across products, firms and locations to gauge the relative importance of retailer versus manufacturer-level shocks in determining prices. I make use of a large panel data set on prices for a cross-section of retailers in the U.S. I analyze prices at the barcode or "Universal Product Code'' (UPC) level for individual stores. I find that only 16% of the variation in prices is common across stores selling an identical product. 65% of the price variation is common to stores within a particular retail chain (but not across retail chains), while 17% is completely idiosyncratic to the store and product. Product categories with frequent temporary "sales'' exhibit a disproportionate amount of completely idiosyncratic price variation. My results suggest that most of the observed price variation arises from retail-level rather than manufacturer-level demand and supply shocks. However, the behavior of prices is difficult to relate to observed variation in costs and demand at the retail level. This suggests that retail prices may vary largely as a consequence of dynamic pricing strategies on the part of retailers or manufacturers, rather than static demand and supply shocks.

We use micro data collected at the border and at retailers to characterize the effects brought by recent changes in US trade policy - particularly the tariffs placed on imports from China - on importers, consumers, and exporters. We start by documenting that the tariffs were almost fully passed through to total prices paid by importers, suggesting the tariffs' incidence has fallen largely on the United States. Since we estimate the response of prices to exchange rates to be far more muted, the recent depreciation of the Chinese renminbi is unlikely to alter this conclusion. Next, using product-level data from several large multi-national retailers, we demonstrate that the impact of the tariffs on retail prices is more mixed. Some affected product categories have seen sharp price increases, but the difference between affected and unaffected products is generally quite modest, suggesting that retail margins have fallen. These retailers' imports increased after the initial announcement of possible tariffs, but before their full implementation, so the intermediate passthrough of tariffs to their prices may not persist. Finally, in contrast to the case of foreign exporters facing US tariffs, we show that US exporters lowered their prices on goods subjected to foreign retaliatory tariffs compared to exports of non-targeted goods.

The objective of this study is to propose novel dynamic pricing mechanisms in the presence of strategic customers using data-driven approaches. Dynamic pricing is the latest trend in pricing strategies and allows optimal response to real-time demand and supply information. Firms often face uncertainties when making pricing decisions. One of the uncertainties often involved is unknown demand. Therefore, businesses seek to optimize revenue while learning demand and reducing the uncertainty involved in setting prices. Understanding consumer decision-making is another crucial aspect of pricing in revenue management. One of the detrimental effects of dynamic pricing is that it invokes a type of behavior in customers that is referred to as forward-looking, or strategic, in revenue management literature. The strategic customer considers future price decreases, and purchases the product if his or her discounted surplus is higher than the immediate surplus. In chapters 1 and 2, we study a retailer who is pricing dynamically to maximize his expected cumulative revenue. We assume that the retailer has no information regarding expected demand nor the type of customers he is facing, whether they are myopic or strategic in their shopping behavior. In the problem of dynamic pricing under demand uncertainty, we face an inherent trade-off between the exploration involved in learning demand and the exploitation which occurs due to revenue maximization. One way of modeling this trade-off is using the multi-arm bandit modeling approach. Many algorithms have been proposed to solve stochastic multi-arm bandit problems. Our focus is on the Thompson Sampling (TS) algorithm which takes a Bayesian approach and was introduced by William R. Thompson. We propose a pricing mechanism called Strategic Thompson Sampling algorithm which is built upon the TS algorithm. Our main contribution in these two chapters is to merge the literature on strategic behavior with the literature on dynamic pricing and demand learning based on the classical multi-arm bandit modeling approach. In these chapters, the retailer is applying our proposed Strategic Thompson Sampling algorithm to learn expected demand in an exploration-versus-exploitation fashion. We start our analysis with a Bernoulli demand scenario in chapter 1 and extend our work to a Normal demand scenario in chapter 2. For both Bernoulli and Normal demand scenarios, we demonstrate numerically that the retailer's long run price offer decreases as the patience level of the strategic customer increases. We further show that the retailer can be better off in terms of his expected cumulative revenue when facing strategic customers. One potential explanation for this observation is the retailer's lower exploration of non-optimal arms in the presence of strategic customers rather than myopic ones. Our intuition is analytically and numerically confirmed for both Bernoulli and Normal demand scenarios. We further provide and compare expected regret bounds on the retailer's expected cumulative revenue for both types of customers. We conclude that the retailer's regret is lower when facing strategic customers as compared to myopic ones. Our objective in chapter 3 is to improve our starting point by building an informative prior and more specifically, an empirical Bayes prior for the Bayesian online learning algorithm that performs binary prediction. The underlying model used in this chapter is a Bayesian Linear Probit (BLIP) model which performs binary classification on a public data set called "Census Income Data Set". Our goal is to build an informative prior using a portion of the training data set and start the BLIP model with the built-in prior rather than the non-informative standard Normal distributions. We further compare the prediction accuracies of the BLIP model with informative and non-informative priors. An empirical Bayes model (Blip with empirical Bayes prior) has been implemented recently in the production system of one of the largest online retailers. The web-lab experiment is currently running.

This paper assesses the dynamic pass-through of crude oil price shocks to retail fuel prices using a novel database on monthly retail fuel prices for 162 countries. The impulse response functions suggest that on average, a one cent increase in crude oil prices per liter translates into a 1.2 cent increase in the retail gasoline price at peak level six months after the shock. However, the estimates vary significantly across country groups, ranging from about 0.5 cent in MENA countries to two cents in advanced economies. The results also show that positive oil price shocks have a larger impact than negative price shocks on the retail gasoline price. Finally, the paper underscores the importance of the new dataset in refining estimates of the fiscal cost of incomplete pass-through.

We investigate the impact of strategic consumer behavior on retailers' dynamic pricing decisions. We present a stylized two-period model, and test the equilibrium predictions in a set of behavioral experiments in which human subjects played the role of pricing managers. Our main insight is that relative to equilibrium predictions, subjects underprice in the main selling season. Consequently, they sell more inventory and obtain higher revenue in that season. However, by doing so they significantly limit their ability to generate revenue in the markdown season, which, in the presence of strategic consumers is a major source of revenue.

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