optimal dynamic pricing of inventories with stochastic demand over finite horizons in many industries managers face the problem of selling a given stock of items by a deadline we investigate the problem of dynamically pricing such inventories when demand is price sensitive and stochastic and the firm s objective is to maximize expected revenues examples that fit this framework include retailers selling fashion and seasonal goods and the travel and leisure industry which markets space such as seats on airline flights cabins on vacation cruises and rooms in hotels that become worthless if not sold by a specific time we formulate this problem using intensity control and obtain structural monotonicity results for the optimal intensity resp price as a function of the stock level and the length of the horizon for a particular exponential family of demand functions we find the optimal pricing policy in closed form for general demand functions we find an upper bound on the expected revenue based on analyzing the deterministic version of the problem and use this bound to prove that simple fixed price policies are asymptotically optimal as the volume of expected sales tends to infinity finally we extend our results to the case where demand is compound poisson only a finite number of prices is allowed the demand rate is time varying holding costs are incurred and cash flows are discounted the initial stock is a decision variable and reordering overbooking and random cancellations are allowed

customer base analysis partial defection of behaviourally loyal clients in a non contractual fmcg retail setting customer relationship management crm enjoys increasing attention as a countermeasure to switching behaviour of customers because foregone profits of partially defected customers can be significant an increase of the retention rate can be very profitable in this paper we focus on the treatment of a company s most behaviourally loyal customers in a non contractual setting we build a model in order to predict partial defection by behaviourally loyal clients using three classification techniques logistic regression automatic relevance determination ard neural networks and random forests focusing on partial attrition of high frequency shoppers who exhibit a regular visit pattern may overcome the problem of unidentifiability of total defection in non contractual settings classification accuracy pcc and area under the receiver operating characteristic curve auc are used to evaluate classifier performance on a test hold out sample using real life data from an fmcg retailer we show that future partial defection can be successfully predicted i e exceeding the benchmark hurdle of the null model there are no significant differences in terms of performance among alternative classification techniques similar to direct marketing applications we find that past behavioural variables more specifically rfm variables recency frequency and monetary value are the best predictors of partial customer defection this set of variables complements demographic variables confirming findings by other authors about its importance in predicting churn behaviour moreover additional variables listed in decreasing order of importance such as the length of customer relationship mode of payment buying behaviour across categories usage of promotions and brand purchase behaviour are shown to be moderately useful to incorporate in attrition models

tracking the market dynamic pricing and learning in a changing environment dynamic pricing of commodities without knowing the exact relation between price and demand is a much studied problem most existing studies assume that the parameters describing the market are constant during the selling period this severely reduces their practical applicability since in reality market characteristics may change all the time without the firm always being aware of it in the present paper we study dynamic pricing and learning in a changing market environment we introduce a methodology that enables the price manager to hedge against changes in the market and provide explicit upper bounds on the regret a measure of the performance of the firm s pricing decisions in addition this methodology guides the selection of the optimal way to estimate the market process we provide numerical examples from practically relevant situations to illustrate the methodology

close the gaps a learning while doing algorithm for single product revenue management problems we consider a retailer selling a single product with limited on hand inventory over a finite selling season customer demand arrives according to a poisson process the rate of which is influenced by a single action taken by the retailer such as price adjustment sales commission advertisement intensity etc the relationship between the action and the demand rate is not known in advance however the retailer is able to learn the optimal action on the fly as she maximizes her total expected revenue based on the observed demand reactions using the pricing problem as an example we propose a dynamic learning while doing algorithm that only involves function value estimation to achieve a near optimal performance our algorithm employs a series of shrinking price intervals and iteratively tests prices within that interval using a set of carefully chosen parameters we prove that the performance of our algorithm is among the best of all possible algorithms in terms of the asymptotic regret the relative loss compared to the full information optimal solution our result closes the performance gaps between parametric and nonparametric learning and between the post price mechanism and the customer bidding mechanism important managerial insight from this research is that the values of information on both the parametric form of the demand function as well as each customer s exact reservation price are less important than prior literature suggests our results also suggest that firms would be better off to perform dynamic learning and action concurrently rather than sequentially

optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices abstract consider a continuous time inventory problem in which a retailer sets the price on a fixed number of a perishable asset that must be sold prior to the time at which it perishes the retailer can dynamically adjust the price between any of a finite number of allowable prices demand for the product is poisson with an intensity that is inversely related to the price the optimal policy is piecewise constant the maximum expected revenue is nondecreasing and concave in both the remaining inventory and the time to go for a given inventory level the optimal price declines as the time at which the products perish approaches at any given time the optimal price is nonincreasing in the number of items remaining unsold these results are extended to i the case in which the prices and corresponding demand intensities depend on the time to go and ii the case in which the retailer can restock to meet demand at a unit cost after the initial inventory has been sold

dynamic pricing with real time demand learning in many service industries the firm adjusts the product price dynamically by taking into account the current product inventory and the future demand distribution because the firm can easily monitor the product inventory the success of dynamic pricing relies on an accurate demand forecast in this paper we consider a situation where the firm does not have an accurate demand forecast but can only roughly estimate the customer arrival rate before the sale begins as the sale moves forward the firm uses real time sales data to fine tune this arrival rate estimation we show how the firm can first use this modified arrival rate estimation to forecast the future demand distribution with better precision and then use the new information to dynamically adjust the product price in order to maximize the expected total revenue numerical study shows that this strategy not only is nearly optimal but also is robust when the true customer arrival rate is much different from the original forecast finally we extend the results to four situations commonly encountered in practice unobservable lost customers time dependent arrival rate batch demand and discrete set of allowable prices

fdi as an outcome of the market for corporate control theory and evidence abstract much foreign direct investment fdi takes the form of mergers and acquisitions m a it is commonplace in finance to view acquisitions as manifestations of the market for corporate control following on that insight we propose a model of fdi in which headquarters bid to control overseas assets we derive an equation for bilateral fdi stocks that resembles the recently developed fixed effects approach to modelling bilateral trade flows we estimate the model and use its parameters to construct benchmarks for evaluating multilateral inward and outward fdi

discrete equilibrium price dispersion an explicit solution of an equilibrium model with price setting firms and searching customers makes possible a number of comparativestatics predictions about how cost differences among firms search costs of customers and taxes will affect the mean and variance of the distribution of market prices another implication of the model is that a firm s demand depends on the difference between its price and the average price in the market

simultaneously learning and optimizing using controlled variance pricing price experimentation is an important tool for firms to find the optimal selling price of their products it should be conducted properly since experimenting with selling prices can be costly a firm therefore needs to find a pricing policy that optimally balances between learning the optimal price and gaining revenue in this paper we propose such a pricing policy called controlled variance pricing cvp the key idea of the policy is to enhance the certainty equivalent pricing policy with a taboo interval around the average of previously chosen prices the width of the taboo interval shrinks at an appropriate rate as the amount of data gathered gets large this guarantees sufficient price dispersion for a large class of demand models we show that this procedure is strongly consistent which means that eventually the value of the optimal price will be learned and derive upper bounds on the regret which is the expected amount of money lost due to not using the optimal price numerical tests indicate that cvp performs well on different demand models and time scales this paper was accepted by assaf zeevi stochastic models and simulation

real time dynamic pricing with minimal and flexible price adjustment we study a standard dynamic pricing problem where the seller a monopolist possesses a finite amount of inventories and attempts to sell the products during a finite selling season despite the potential benefits of dynamic pricing many sellers still adopt a static pricing policy because of 1 the complexity of frequent reoptimizations 2 the negative perception of excessive price adjustments and 3 the lack of flexibility caused by existing business constraints in this paper we develop a family of pricing heuristics that can be used to address all these challenges our heuristic is computationally easy to implement it requires only a single optimization at the beginning of the selling season and automatically adjusts the prices over time moreover to guarantee a strong revenue performance the heuristic only needs to adjust the prices of a small number of products and do so infrequently this property helps the seller focus his effort on the prices of the most important products instead of all products in addition in the case where not all products are equally admissible to price adjustment due to existing business constraints such as contractual agreement strategic product positioning etc our heuristic can immediately substitute the price adjustment of the original products with the price adjustment of similar products and maintain an equivalent revenue performance this property provides the seller with extra flexibility in managing his prices

game theoretic pricing models in hotel revenue management an equilibrium choice based conjoint analysis approach abstract this paper explores a game theoretically founded approach to conjoint analysis that determines equilibrium room rates under differentiated price competition in an oligopolistic hotel market competition between hotels is specified in terms of market share functions that can be estimated using multinomial logit models of consumer choice the approach is based on choice based conjoint analysis that permits the estimation of attributes weights part worths for an additive utility formulation of the utility function from this room rates that equilibrate the market conditioned on the differences in services and facilities offered by competing hotels can be determined the approach is illustrated by an example

non cooperative competition among revenue maximizing service providers with demand learning this paper recognizes that in many decision environments in which revenue optimization is attempted an actual demand curve and its parameters are generally unobservable herein we describe the dynamics of demand as a continuous time differential equation based on an evolutionary game theory perspective we then observe realized sales data to obtain estimates of parameters that govern the evolution of demand these are refined on a discrete time scale the resulting model takes the form of a differential variational inequality we present an algorithm based on a gap function for the differential variational inequality and report its numerical performance for an example revenue optimization problem

price dispersion and incomplete learning in the long run abstract in each period a seller chooses a price and the customer for that period buys either one unit or nothing the relationship between price and purchase probability is one of two linear functions it is shown that the seller behaving optimally does not necessarily learn which function is the actual relationship since there can be a positive probability of sequences of prices converging to the price at which the two functions give the same purchase probability

the ekc for so2 does firm size matter abstract the environmental kuznets curve hypothesis ekc predicts an inverse u shaped relationship between environmental pollution and per capita income the literature with respect to the ekc is vast but far from conclusive this paper adds firm size to the standard ekc reduced form regression and analyses whether firm size matters once income and composition are controlled for results suggest that large firm countries are initially associated with higher levels of environmental damage however as economies develop large firm countries find it easier to adopt more stringent environmental legislation once environmental damage starts to decrease the decrease is much larger in large firm countries

adaptive ordering and pricing for perishable products we consider the combined problem of pricing and ordering for a perishable product with unknown demand distribution and censored demand observations resulting from lost sales faced by a monopolistic retailer we develop an adaptive pricing and ordering policy with the asymptotic property that the average realized profit per period converges with probability one to the optimal value under complete information on the distribution the pricing mechanism is modeled as a multiarmed bandit problem while the order quantity decision made after the price level is established is based on a stochastic approximation procedure with multiplicative updates

price inertia in a macroeconomic model of monopolistic competition this paper discusses the consequences of price setting by firms under monopolistic competitio n in dynamic models it is assumed that costs of price adjustment are speed dependent the keynesian regime prevails when firms choose a p oint on the demand curve if real labor costs are relatively high cu stomers may be rationed after analyzing firm behavior in a partial e quilibrium setting demand is made endogenous in a full scope macroec onomic model numerical examples are worked through by applying the t echnique of multiple shooting it is found that relatively small cost s of price adjustment give rise to substantial macroeconomic effects copyright 1988 by the london school of economics and political science

manufacturer allowances and retailer pass through rates in a competitive environment a commonly held belief has grocery and mass merchandise retailers gaining power relative to the upstream consumer package goods manufacturers one of the major justifications for this belief is that manufacturers are now giving retailers more side payments such as trade allowances slotting allowances etc however a number of researchers have shown that these concessions have not translated into increased profit for the retailer relative to the manufacturer this paper explores via an analytic model why one might see retailers getting concessions from the manufacturer without being able to translate them into high profits we do this by representing the interaction between the channel members as a one period profit maximizing game where manufacturers decide on how large a side payment i e a concession to give to each retailer and retailers decide on how much of this side payment to use to promote the manufacturer s product at the heart of our model is a demand function for each product offering i e a specific brand sold by a specific retailer this demand function is linear in own and other s prices but based on empirical evidence is assumed to be nonlinear in the effects of merchandising activities e g short term price discounts better shelf space advertising etc specifically merchandising effects are modeled with a square root function that also acknowledges most short term promotional effects result in brand or store switching versus increase category volume the model is composed of six parameters these are the inherent popularity of the brand at a particular outlet own price sensitivity cross but within the store price sensitivity customer s sensitivity to within store promotion activities customer s sensitivity to between store promotional activities and the degree to which promotional activities yield incremental product category sales we use our underlying demand formulation to find the equilibrium solution to the full information stackelberg leader game for different store and brand loyalty environments as captured by our six parameters these parameters are chosen based on empirical evidence to span possible market conditions that each channel member might have our findings and modeling efforts should be of interest to analytic channel modelers and scholars interested in the mass merchandising and grocery store industries for a theoretical point of view we build upon the case 4 model of lee and staelin 1997 to allow for promotional activities from a substantive point of view we show that manufacturers will freely give retailers side payments even though they know these retailers will pocket a substantial portion of this concession moreover we identify conditions within our model that lead to larger allowances lower pass through rates and lower retail profits outcomes that are compatible with recent industry trends in addition we highlight the difference in effect on profits prices etc of changes in consumers sensitivity to inter store differences in storewide merchandising activities one of the more counter intuitive results of these analyses is that it is in the manufacturer s best interest to help retailers increase their spatial monopoly by decreasing consumers tendency to cross store shop because of merchandising activities

actively learning about demand and the dynamics of price adjustment by observing the quantity demanded at particular prices a firm may learn about the parameters of its demand curve in such an environment price changes obstruct the learning process by inducing additional noise the authors paper constructs a dynamic model where a price setting firm endogenously controls the speed of learning the model provides a possible explanation for price inertia as a stable pricing policy allows the firm to learn more rapidly which improves future expected profits furthermore even in the long run learning continues to affect the firm s optimal price copyright 1990 by royal economic society

on the surprising sufficiency of linear models for dynamic pricing with demand learning we consider a multiperiod single product pricing problem with an unknown demand curve the seller s objective is to adjust prices in each period so as to maximize cumulative expected revenues over a given finite time horizon in doing so the seller needs to resolve the tension between learning the unknown demand curve and maximizing earned revenues the main question that we investigate is the following how large of a revenue loss is incurred if the seller uses a simple parametric model that differs significantly i e is misspecified relative to the underlying demand curve we measure performance by analyzing the price trajectory induced by this misspecified model and quantifying the magnitude of revenue losses as a function of the time horizon relative to an oracle that knows the true underlying demand curve the price of misspecification is expected to be significant if the parametric model is overly restrictive somewhat surprisingly we show under reasonably general conditions that this need not be the case this paper was accepted by gerard cachon stochastic models and simulation

dynamic pricing and learning with finite inventories we study a dynamic pricing problem with finite inventory and parametric uncertainty on the demand distribution products are sold during selling seasons of finite length and inventory that is unsold at the end of a selling season perishes the goal of the seller is to determine a pricing strategy that maximizes the expected revenue inference on the unknown parameters is made by maximum likelihood estimation we show that this problem satisfies an endogenous learning property which means that the unknown parameters are learned on the fly if the chosen selling prices are sufficiently close to the optimal ones we show that a small modification to the certainty equivalent pricing strategy which always chooses the optimal price w r t current parameter estimates satisfies regrett olog2t where regrett measures the expected cumulative revenue loss w r t a clairvoyant who knows the demand distribution we complement this upper bound by showing an instance for which the regret of any pricing policy satisfies i log t

the trans pacific partnership suddenly a game changer this article argues that the entry of canada mexico and most importantly japan has dramatically shifted the prospects and potential impact of the tpp more specifically the article offers four reasons why the entry of canada mexico and japan has transformed the tpp from a potentially failed agreement of limited economic significance and great geopolitical risk to a game changer for the region and beyond the article also recognizes that while the entry of canada mexico and japan has transformed the tpp negotiations their entry comes with some potentially serious risks the article discusses three specific risks the new entrants bring to the negotiations

on the minimax complexity of pricing in a changing environment we consider a pricing problem in an environment where the customers willingness to pay wtp distribution may change at some point over the selling horizon customers arrive sequentially and make purchase decisions based on a quoted price and their private reservation price the seller knows the wtp distribution pre and postchange but does not know the time at which this change occurs the performance of a pricing policy is measured in terms of regret the loss in revenues relative to an oracle that knows the time of change prior to the start of the selling season we derive lower bounds on the worst case regret and develop pricing strategies that achieve the order of these bounds thus establishing the complexity of the pricing problem our results shed light on the role of price experimentation and its necessity for optimal detection of changes in market response wtp our formulation allows for essentially arbitrary consumer wtp distributions and purchase request patterns

analytical solutions to the dynamic pricing problem for time normalized revenue abstract in this work we consider dynamic pricing for the case of continuous replenishment an essential ingredient in such a formulation is the use of time normalized revenue or profit function in other words revenue or profit per unit time this provides the incentive to sell many items in the shortest time and of course at a high price moreover for most firms what matters most is how much revenue or profit is achieved in a certain time frame for example per year this changes the problem qualitatively and methodologically we develop a new dynamic pricing model for this formulation we derive an analytical solution to the pricing problem in the form of a simple to solve ordinary differential equation ode equation the trajectory of this ode gives the optimal pricing curve unlike many of the models existing in the literature that rely on computationally demanding dynamic programming type solutions our model is relatively simple to solve also we apply the derived equation to two commonly used price demand functions the exponential and the power functions and derive closed form pricing curves for these functions

bayes solution to dynamic inventory models under unknown demand distribution this paper considers the periodic review inventory problem for which one or more parameters of the demand distribution are unknown with a known prior distribution chosen from the natural conjugate

emissions leakage environmental policy and trade frictions abstract we develop a two good general equilibrium model of a small open economy to decompose the effect of a country s unilateral strengthening of environmental policy on pollution emissions in the rest of the world known as emissions leakage we show analytically and numerically that the level of emissions leakage depends on the level of trade friction in the service sector in the model production in the manufacturing sector is associated with pollution emissions and production in the service sector is clean in a special case with free trade in manufacturing and no trade in services no leakage occurs allowing for trade in services we solve for the relationship between trade frictions in the service sector and leakage at lower levels of service sector s trade friction leakage from a small strengthening of environmental regulation decreases increases if services are imported exported finally we simulate the model calibrating the to the canadian economy to compare these effects relative sizes over a range of plausible parameter values leakage is about 18 lower when using trade friction levels estimated from the literature rather than assuming no trade friction in services

technical note dynamic pricing and demand learning with limited price experimentation in a dynamic pricing problem where the demand function is not known a priori price experimentation can be used as a demand learning tool existing literature usually assumes no constraint on price changes but in practice sellers often face business constraints that prevent them from conducting extensive experimentation we consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set the seller is allowed to make at most m price changes during t periods the objective is to minimize the worst case regret i e the expected total revenue loss compared with a clairvoyant who knows the demand distribution in advance we demonstrate a pricing policy that incurs a regret of ologmt or m iterations of the logarithm furthermore we describe an implementation of this pricing policy at groupon a large e commerce marketplace for daily deals the field study shows significant impact on revenue and bookings the e companion is available at https doi org 10 1287 opre 2017 1629

dynamic pricing advance sales and aggregate demand learning in airlines this paper uses a unique u s airlines panel data set to empirically study the dynamic pricing of inventories with uncertain demand over a finite horizon i estimate a dynamic pricing equation and a dynamic demand equation that jointly characterize the adjustment process between prices and sales as the flight date nears i find that the price increases as the inventory decreases and decreases as there is less time to sell consistent with aggregate demand learning and price adjustment demand shocks have a positive and much larger effect on prices than the positive effect of anticipated sales

the paradox of preferences regional trade agreements and trade costs in services abstract this paper analyzes the relationship between regional trade integration and trade costs in services industries the empirical analysis relies on the one hand on a dataset of theory consistent bilateral trade costs calculated for 55 countries over the period 1999 2009 and on the other hand on an analysis of services commitments in 66 regional trade agreements to which these countries are parties despite the proliferation of services rtas in the past decade we find that trade costs are only slightly lower due to the impact of these agreements in addition we find that the trade cost reductions that do take place tend to happen before the agreement is signed this mechanism is consistent with countries using services rtas as a way of locking in reforms finally we find that the preferential margin of services rtas is quite thin members and non members both see slightly lower trade costs when an rta is signed we discuss the possible explanations for these findings in terms of the nature of services rtas and their relationship with regulatory reform in signatory countries based on these results we argue that regionalism in the case of services seems relatively non discriminatory and does not lead to substantial trade preferences

a partially observed markov decision process for dynamic pricing in this paper we develop a stylized partially observed markov decision process pomdp framework to study a dynamic pricing problem faced by sellers of fashion like goods we consider a retailer that plans to sell a given stock of items during a finite sales season the objective of the retailer is to dynamically price the product in a way that maximizes expected revenues our model brings together various types of uncertainties about the demand some of which are resolvable through sales observations we develop a rigorous upper bound for the seller s optimal dynamic decision problem and use it to propose an active learning heuristic pricing policy we conduct a numerical study to test the performance of four different heuristic dynamic pricing policies in order to gain insight into several important managerial questions that arise in the context of revenue management

third country effects of regional trade agreements does regionalism negatively impact non members to answer this question we examine the effect of regional trade agreements rtas on imports from non members and the tariffs that they face using data from six rtas in latin america and europe we do not find evidence that implementation of the regional agreements is associated with trade diversion from third countries to regional members using detailed industry data on preference margins and most favoured nation mfn tariffs for three trade agreements in latin america over 12 years we find that greater preference margins do not significantly reduce imports from third countries we also look at the effect of preferences on external tariffs we find evidence that preferential tariff reduction tends to precede the reduction of external mfn tariffs in a given sector offering evidence of tariff complementarity overall the results suggest that regionalism does not significantly harm non members

learning and pricing with models that do not explicitly incorporate competition in revenue management research and practice demand models are used that describe how demand for a seller s products depends on the decisions such as prices of that seller even in settings where the demand for a seller s products also depends on decisions of other sellers the models often do not explicitly account for such decisions it has been conjectured in the revenue management literature that such monopoly models may incorporate the effects of competition because the parameter estimates of the monopoly models are based on data collected in the presence of competition in this paper we take a closer look at such a setting to investigate the behavior of parameter estimates and decisions if monopoly models are used in the presence of competition we consider repeated pricing games in which two competing sellers use mathematical models to choose the prices of their products over the sequence of games each seller attempts to estimate the values of the parameters of a demand model that expresses demand as a function only of its own price using data comprised only of its own past prices and demand realizations we analyze the behavior of the sellers parameter estimates and prices under various assumptions regarding the sellers knowledge and estimation procedures and we identify situations in which a the sellers prices converge to the nash equilibrium associated with knowledge of the correct demand model b the sellers prices converge to the cooperative solution and c the sellers prices have many potential limit points that are neither the nash equilibrium nor the cooperative solution and that depend on the initial conditions we compare the sellers revenues at potential limit prices with their revenues at the nash equilibrium and the cooperative solution and we show that it is possible for sellers to be better off when using a monopoly model than at the nash equilibrium

dynamic pricing with a prior on market response we study a problem of dynamic pricing faced by a vendor with limited inventory uncertain about demand and aiming to maximize expected discounted revenue over an infinite time horizon the vendor learns from purchase data so his strategy must take into account the impact of price on both revenue and future observations we focus on a model in which customers arrive according to a poisson process of uncertain rate each with an independent identically distributed reservation price upon arrival a customer purchases a unit of inventory if and only if his reservation price equals or exceeds the vendor s prevailing price we propose a simple heuristic approach to pricing in this context which we refer to as decay balancing computational results demonstrate that decay balancing offers significant revenue gains over recently studied certainty equivalent and greedy heuristics we also establish that changes in inventory and uncertainty in the arrival rate bear appropriate directional impacts on decay balancing prices in contrast to these alternatives and we derive worst case bounds on performance loss we extend the three aforementioned heuristics to address a model involving multiple customer segments and stores and provide experimental results demonstrating similar relative merits in this context

bayesian dynamic pricing policies learning and earning under a binary prior distribution motivated by applications in financial services we consider a seller who offers prices sequentially to a stream of potential customers observing either success or failure in each sales attempt the parameters of the underlying demand model are initially unknown so each price decision involves a trade off between learning and earning attention is restricted to the simplest kind of model uncertainty where one of two demand models is known to apply and we focus initially on performance of the myopic bayesian policy mbp variants of which are commonly used in practice because learning is passive under the mbp that is learning only takes place as a by product of actions that have a different purpose it can lead to incomplete learning and poor profit performance however under one additional assumption a constrained variant of the myopic policy is shown to have the following strong theoretical virtue the expected performance gap relative to a clairvoyant who knows the underlying demand model is bounded by a constant as the number of sales attempts becomes large this paper was accepted by gerard p cachon stochastic models and simulation

a continuous time yield management model with multiple prices and reversible price changes this article studies a continuous time yield management model in which reversible price changes are allowed we assume that perishable assets are offered at a set of discrete price levels demand at each level is a poisson process to maximize the expected revenue management controls the price dynamically as sales evolve we show that a subset of these prices that form a concave envelope is potentially optimal we formulate the problem into an intensity control model and derive the optimal solution in closed form properties of the optimal solution and their policy implementations are discussed numerical examples are provided

assessing the impact of derived behavior information on customer attrition in the financial service industry the value of the customer has been widely recognized in terms of financial planning and efficient resource allocation including the financial service industry previous studies have shown that directly observable information can be used in order to make reasonable predictions of customer attrition probabilities however these studies do not take full account of customer behavior information in this paper we demonstrate that efficient use of information can add value to financial services industry and improve the prediction of customer attrition to achieve this we apply an orthogonal polynomial approximation analysis to derive unobservable information which is then used as explanatory variables in a probit hazard rate model our results show that derived information can help our understanding of customer attrition behavior and give better predictions we conclude that both researchers and the financial service industry should gather and use derived financial information in addition to directly observable information

an adjustment scheme for nonlinear pricing problem with two buyers we examine a contracting problem with asymmetric information in a monopoly pricing setting traditionally the problem is modeled as a one period bayesian game where the incomplete information about the buyers preferences is handled with some subjective probability distribution here we suggest an iterative online method to solve the problem we show that when the buyers behave myopically the seller can learn the optimal tariff by selling the product repeatedly in a practical modification of the method the seller offers linear tariffs and adjusts them until optimality is reached the adjustment can be seen as gradient adjustment and it can be done with limited information and so that it benefits both the seller and the buyers our method uses special features of the problem and it is easily implementable

reoptimization and self adjusting price control for network revenue management we consider a standard dynamic pricing problem with finite inventories finite selling horizon and stochastic demands where the objective of the seller is to maximize total expected revenue we introduce a simple improvement of the popular static price control known in the literature the proposed heuristic only requires a single optimization at the beginning of the selling horizon and does not require any reoptimization at all this provides an advantage over the potentially heavy computational burden of reoptimization especially for very large applications with frequent price adjustments in addition our heuristic can be implemented in combination with a few reoptimizations to achieve a high level revenue performance this hybrid of real time adjustment and reoptimization allows the seller to enjoy the benefit of reoptimization without overdoing it

optimal experimentation in a changing environment this paper studies optimal experimentation by a monopolist who faces an unknown demand curve subject to random changes and who maximizes profits over an infinite horizon in continuous time we show that there are two qualitatively very different regimes determined by the discount rate and the intensities of demand curve switching and the dependence of the optimal policy on these parameters is discontinuous one regime is characterized by extreme experimentation and good tracking of the prevailing demand curve the other by moderate experimentation and poor tracking moreover in the latter regime the agent eventually becomes trapped into taking actions in a strict subset of the feasible set

dynamic pricing with online learning and strategic consumers an application of the aggregating algorithm we study the problem faced by a monopolistic company that is dynamically pricing a perishable product or service and simultaneously learning the demand characteristics of its customers in the learning procedure the company observes the sales history over consecutive learning stages and predicts consumer demand by applying an aggregating algorithm aa to a pool of online stochastic predictors numerical implementation uses finite sample distribution approximations that are periodically updated using the most recent sales data these are subsequently altered with a random step characterizing the stochastic predictors the company s pricing policy is optimized with a simulation based procedure integrated with aa the methodology of the paper is general and independent of specific distributional assumptions we illustrate this procedure on a demand model for a market in which customers are aware that pricing is dynamic may time their purchases strategically and compete for a limited product supply we derive the form of this demand model using a game theoretic consumer choice model and study its structural properties numerical experiments demonstrate that the learning procedure is robust to deviations of the actual market from the model of the market used in learning

dynamic pricing under a general parametric choice model we consider a stylized dynamic pricing model in which a monopolist prices a product to a sequence of t customers who independently make purchasing decisions based on the price offered according to a general parametric choice model the parameters of the model are unknown to the seller whose objective is to determine a pricing policy that minimizes the regret which is the expected difference between the seller s revenue and the revenue of a clairvoyant seller who knows the values of the parameters in advance and always offers the revenue maximizing price we show that the regret of the optimal pricing policy in this model is theta sqrt t by establishing an omega sqrt t lower bound on the worst case regret under an arbitrary policy and presenting a pricing policy based on maximum likelihood estimation whose regret is cal o sqrt t across all problem instances furthermore we show that when the demand curves satisfy a well separated condition the t period regret of the optimal policy is log t numerical experiments show that our policies perform well

the value of market access and national treatment commitments in services trade agreements this paper looks at market access and national treatment commitments for services in the general agreement on trade in services gats and in 95 regional trade agreements rtas involving the countries that are covered in the oecd services trade restrictiveness index stri the objective is to quantify the impact of legal bindings on trade in services that result from a reduction in the uncertainty faced by exporters bilateral bindings indices are created for five broad service sectors professional services computer services telecoms financial services and transport services they indicate how close the sector is from a fully bound regime with no possibility to introduce any new trade barrier by comparing commitments with the actual trade regime these bilateral indices are then tested over the period 2000 2014 in a structural gravity model despite differences across sectors the results confirm that the legal bindings typically found in services trade agreements tend to have a positive impact on exports even if no actual liberalisation takes place

stalking information bayesian inventory management with unobserved lost sales retailers are frequently uncertain about the underlying demand distribution of a new product when taking the empirical bayesian approach of scarf 1959 they simultaneously stock the product over time and learn about the distribution assuming that unmet demand is lost and unobserved this learning must be based on observing sales rather than demand which differs from sales in the event of a stockout using the framework and results of braden and freimer 1991 the cumulative learning about the underlying demand distribution is captured by two parameters a scale parameter that reflects the predicted size of the underlying market and a shape parameter that indicates both the size of the market and the precision with which the underlying distribution is known an important simplification result of scarf 1960 and azoury 1985 which allows the scale parameter to be removed from the optimization is shown to extend to this setting we present examples that reveal two interesting phenomena 1 a retailer may hope that compared to stocking out realized demand will be strictly less than the stock level even though stocking out would signal a stochastically larger demand distribution and 2 it can be optimal to drop a product after a period of successful sales we also present specific conditions under which the following results hold 1 investment in excess stocks to enhance learning will occur in every dynamic problem and 2 a product is never dropped after a period of poor sales the model is extended to multiple independent markets whose distributions depend proportionately on a single unknown parameter we argue that smaller markets should be given better service as an effective means of acquiring information

nonparametric advertising budget allocation with inventory constraint abstract in this paper we study the optimization problem of the advertising budget allocation for revenue management faced by a marketer besides the advertising budget the marketer is subject to an inventory constraint during the promotion season the marketer can affect sales by spending on advertising but does not initially know the relationship between the advertising expense and consequent sales we propose a nonparametric learning while doing budget allocation policy for the problem specifically we first conduct a sequence of advertising experiments to learn predict the market sales response through observing realized sales exploration then based on the learned sales function determine the following budget allocation planning exploitation in particular during the exploration and exploitation phases we need to balance the advertising and inventory budgets simultaneously we show that our policy is asymptotically optimal as the size of the market increases by constructing a worst case example we show that our policy achieves near best asymptotic performance we also provide numerical illustrations to show how our policy works and discuss how its performance changes as the system parameters vary we also glen some managerial implications of our model and policy from the numerical results

dynamic pricing with an unknown demand model asymptotically optimal semi myopic policies we consider a monopolist who sells a set of products over a time horizon of t periods the seller initially does not know the parameters of the products linear demand curve but can estimate them based on demand observations we first assume that the seller knows nothing about the parameters of the demand curve and then consider the case where the seller knows the expected demand under an incumbent price it is shown that the smallest achievable revenue loss in t periods relative to a clairvoyant who knows the underlying demand model is of order t in the former case and of order log t in the latter case to derive pricing policies that are practically implementable we take as our point of departure the widely used policy called greedy iterated least squares ils which combines sequential estimation and myopic price optimization it is known that the greedy ils policy itself suffers from incomplete learning but we show that certain variants of greedy ils achieve the minimum asymptotic loss rate to highlight the essential features of well performing pricing policies we derive sufficient conditions for asymptotic optimality

a taxonomy and research overview of perishable asset revenue management yield management overbooking and pricing this paper proposes the term perishable asset revenue management to denote the field that combines the areas of yield management overbooking and pricing for perishable assets after summarizing the characteristics common to problems in this field the paper discusses the objectives and constraints faced by decision makers then it offers a comprehensive taxonomy with 14 different elements and reviews the research that has been done related to each element finally it suggests some important areas of future research that can help bridge the gap between theory and application

counting your customers compounding customer s in store decisions interpurchase time and repurchasing behavior abstract consumer buying decision is an important element of any company s efforts to count its customers stochastic models have been used extensively in previous studies of marketing and are usually constructed so as to easily fit the data and make predictions however the regularity of customer interpurchase time in store decisions and repeat buying behavior are not considered simultaneously in the literature since the empirical evidence suggests that all the above factors do indeed influence the firm s sales we here present a methodology which takes all these elements into account the methodology employed is illustrated by taking the customer purchase data for tea as an example by not eliminating light buyers the integrated model we developed achieves precise results and addresses the relative importance of each element while predicting firm sales

buy n times get one free loyalty cards are they profitable for competing firms a game theoretic analysis abstract this paper evaluates whether firms offering loyalty programs lps should choose a restricted redemption policy by imposing a specific number of purchases before customers can redeem their points such restriction is commonly offered in form of buy n times get one free loyalty cards we develop a multinomial logit model where consumer s utility depends on the value of the product and of the rewards using an iterative algorithm we numerically solve a nash game for two firms offering loyalty programs optimal strategies and profits are obtained for three different scenarios games 1 both firms do not restrict redemption 2 both firms restrict redemption and 3 only one firm restricts redemption while the other firm does not our main findings indicate that each firm s optimal strategies are significantly affected by whether the competitor decides to restrict or not to restrict redemption in particular a firm that restricts reward redemption should offer a higher price if its competitor also restricts redemption further the dominant strategy of the game depends on customers valuations of time and rewards for example when customers highly value time but do not highly value rewards the dominant strategy for both firms is not to restrict redemption alternatively firms can face a prisoner dilemma situation leading to unrestricted redemption policy for intermediate levels of customer valuation of both time and rewards

myopic policies for some inventory models with uncertain demand distributions the majority of papers on stochastic inventory theory make the assumption that the distribution of consumer demand in each time period is known with certainty while this assumption is unsupported in many applied contexts it is conventionally held that more realistic models are more difficult to solve and will not yield simple operational policies this paper shows that a simple inventory policy based upon a critical fractile can be optimal or near optimal in some inventory models with parameter adaptive demand processes in these some parameter of the demand distribution is not known with certainty and estimates of the parameter are updated in a statistical fashion as demand is observed through time examples include exponentially smoothed forecasts and bayesian updating of parameter estimates bounds on the value loss relative to optimal cost when using the critical fractile policy can be calculated directly from the problem data some numerical examples illustrate the technique

dynamic pricing in the presence of inventory considerations research overview current practices and future directions the benefits of dynamic pricing methods have long been known in industries such as airlines hotels and electric utilities where the capacity is fixed in the short term and perishable in recent years there has been an increasing adoption of dynamic pricing policies in retail and other industries where the sellers have the ability to store inventory three factors contributed to this phenomenon 1 the increased availability of demand data 2 the ease of changing prices due to new technologies and 3 the availability of decision support tools for analyzing demand data and for dynamic pricing this paper constitutes a review of the literature and current practices in dynamic pricing given its applicability in most markets and its increasing adoption in practice our focus is on dynamic intertemporal pricing in the presence of inventory considerations

a knowledge and physical capital model of international trade flows foreign direct investment and multinational enterprises abstract this paper addresses two important issues at the nexus of the literatures on international trade foreign direct investment fdi foreign affiliate sales fas and multinational enterprises mnes first the introduction of a third internationally mobile factor physical capital to the standard 2 2 2 knowledge capital model of mnes with skilled and unskilled labor allows us to resolve fairly readily the puzzle in the modern mne literature that foreign affiliate sales among two identical economies completely displace their international trade intra industry trade and intra industry fdi and fas can coexist for national and multinational firms with identical productivities in identical countries second the introduction also of a third country to the model suggests a formal n country theoretical rationale for estimating gravity equations of bilateral fdi flows and fas in a manner consistent with estimating gravity equations for bilateral trade flows

dynamic pricing and warranty policies for products with fixed lifetime abstract we consider a repairable product with known market entry and departure times a warranty policy is offered with product purchase under which a customer can have a failed item repaired free of charge in the warranty period it is assumed that customers are heterogeneous in their risk attitudes toward uncertain repair costs incurred after the warranty expires the objective is to determine a joint dynamic pricing and warranty policy for the lifetime of the product which maximizes the manufacturer s expected profit in the first part of the analysis we consider a linearly decreasing price function and a constant warranty length we first study customers purchase patterns under several different pricing strategies by the manufacturer and then discuss the optimal pricing and warranty strategy in the second part we assume that the warranty length can be altered once during the product lifetime in developing a joint pricing and warranty policy numerical studies show that a dynamic warranty policy can significantly outperform a fixed length warranty policy

a methodology based on profitability criteria for defining the partial defection of customers in non contractual settings abstract the defection or churn of customers represents an important concern for any company and a central matter of interest in customer base analysis an additional complication arises in non contractual settings where the characteristics that should be observed to saying that a customer has totally or partially defected are not clearly defined as a matter of fact different definitions of the churn situation could be used in this context focusing on non contractual settings in this paper we propose a methodology for evaluating the short time economic effects that using a certain definition of churn would have on a company with this aim we have defined two efficiency measures for the economic results of a marketing campaign implemented against churn and these measures have been computed using a set of definitions of partial defection our methodology finds that definition maximizing both efficiency measures and moreover the monetary amount that the company should invest per customer in the campaign for achieving the optimal solution this has been modelled as a multiobjective optimization problem that we solved using compromise programming numerical results using real data from a spanish retailing company are presented and discussed in order to show the performance and validity of our proposal