Presence Of Strategic Customers Research Articles

Demand cannibalization during product rollovers in the presence of strategic customers

This paper studies how different types of demand cannibalization take place during product rollovers and their effects on a firm’s profitability. Due to continuous product upgrades, demand cannibalization among various generations of products has become a common practice, raising interesting investigations from professionals and academics. Within this context, we model firms’ and customers’ decision-making as a two-period sequential game and derive equilibrium results under a dual rollover strategy, with and without a trade-in program. We find that demand cannibalization is essentially caused by customers’ strategic comparison of available purchase options. Our analysis further reveals that both the cannibalization effect and the postponement effect resulting from customers’ waiting for upgraded products are necessary for firms to improve profits. On the contrary, the postponement effect due to waiting for a discount is entirely detrimental to the firm. We propose effective launch strategies to avoid unfavorable demand cannibalization under different conditions. In addition, offering trade-in programs helps not only suppress strategic waiting, but also foster demand for an upgraded product. These strategies are particularly effective when the firm holds a greater quantity of inventory or when the degree of incremental innovation is high.

A deep Q-learning approach to optimize ordering and dynamic pricing decisions in the presence of strategic customers

In this paper, we present an optimization method to analyze the simultaneous decisions on dynamic pricing and ordering quantities for seasonal products, by a retailer in monopolistic condition. Customers are assumed to be strategic and may postpone their purchase to get a lower price in future. The problem has been investigated in the context of multiple substitute products. We have developed a model based on deep neural networks to estimate customers’ demand. The problem is complex and cannot be solved using classical optimization methods. Therefore, we have developed a reinforcement learning algorithm called deep Q-learning algorithm (DQL) to solve the problem. The proposed algorithm is a combination of a Q-learning algorithm and two deep neural networks for the primary and discount sales periods, which uses the neural network to estimate the Q-values in a large space of states and actions. The performances of the demand model and the proposed optimization algorithm have been tested using a real-world dataset taken from the clothing industry. The results of our experiments demonstrate that the proposed demand model performs better than a fully connected neural network-based model and a latent class model tested in this paper. Furthermore, the performance of the DQL algorithm is significantly superior to those of two simulated annealing and genetic algorithms. In addition, the results of a comparison between the DQL algorithm and another reinforcement learning algorithm called State-Action-Reward-State-Action (SARSA) indicate that the proposed algorithm results in higher revenues and takes less time to converge. Consequently, the proposed algorithm has a high potential for solving such a large scale integrated pricing and ordering optimization problem.

Simple Monotonic Readjustment Policies with Applications to Markdown Pricing and Pricing in the Presence of Strategic Customers

A Simple Monotonic Dynamic Pricing Policy We consider a canonical revenue management setting in which a monopolist seeks to maximize expected total revenues by selling a fixed inventory of product to customers who arrive sequentially over time. It is known in the literature that a simple dynamic pricing policy can perform reasonably well in such setting. However, the literature typically assumes that price can move freely over time (i.e., it can either go up or down). In practice, firms often impose certain restrictions on price movement. In this paper, we consider the setting in which price can only move monotonically over time (either only decreasing or only increasing). We devise a simple provably near-optimal dynamic pricing policy. Aside from markdown applications, our policy can also be applied to the setting with strategic customers.

Dynamic pricing for the successive-generation products in the presence of strategic customers and limited trade-in duration

PurposeThis paper investigates the dynamic pricing strategy of a firm for the successive-generation products under the conditions of the limited trade-in duration and strategic customers. Further, it explores the effect of a limited trade-in duration on the choice of the myopic and strategic customers, besides the optimal dynamic pricing and trade-in strategy of the firm.Design/methodology/approachBased on the choice behavior of the myopic and strategic customers, the authors have developed a two-period game-theoretic analytical model to decide the optimal retail prices of the successive-generation products and the optimal trade-in rebate when the firm adopts a dynamic pricing strategy and then investigate three extensions of the basic model to discuss the change in the results owing to the relaxation of certain conditions.FindingsThe authors find from the results that, in terms of profit maximization, it is better to extend the limited trade-in duration, and hence, the firm should implement a dynamic pricing strategy. However, in the situation of using a static pricing strategy, the firm should extend the limited trade-in duration only if the incremental value of the new generation products is below a certain threshold. Moreover, the firm should use a dual rollover strategy instead of a single rollover one. If all customers in the market are myopic, then the firm should also extend the limited trade-in duration.Research limitations/implicationsThis study mainly discusses the impact of limited trade-in duration on the firm's dynamic pricing strategy when facing strategic customers, which provides several directions for future research. First, if the government offers subsidies to consumers, how will strategic consumers make purchase decisions? How would the enterprise make its pricing decision? Second, when asymmetric information exists between consumers and firms, how will it affect consumers' choice behavior and firms' pricing decisions? All these issues are worth exploring in the future.Practical implicationsThese results offer certain managerial insights for the firm in the decision making on pricing within the trade-in program.Originality/valueThis is the first work to study the dynamic pricing strategy of the firm for the successive-generation products under the conditions of the limited trade-in duration and strategic customers. Further, this work discusses the changes in results owing to the relaxation of certain conditions.

Omni-channel retail operations in the presence of strategic customers: The benefit of inventory commitment.

This paper studies the impact of strategic customer behavior on retailers’ omni-channel strategies. Customers in the market are classified into strategic omni-channel customers and myopic B&M (brick and mortar) store customers. The retailer firstly charges a full price but will then salvage the leftover inventory at a lower price (markdown policy) for strategic omni-channel customers after the customers’ demand is realized. The strategic omni-channel customers choose purchase timing to maximize their own expected profit. While, the myopic B&M store customers do not choose purchase timing. By characterizing rational expectation equilibrium, we find that the inventory level under markdown policy is higher than that in the classic model where no markdown policy is implemented. Also, the policy will transfer more strategic customers to the online channel to buy at full price so that the retailer will benefit from it. Besides, inventory commitment policy can further increase retailer’s profit based on markdown policy. In addition, we extend the model to the supply chain and present the contrasting view that the total profit of the decentralized supply chain under the wholesale price contract is higher than that of the centralized supply chain. The influence of strategic customer behavior and system parameters on the retailer’s optimal decision is discussed through numerical study.

Reselling or agency model under markdown pricing policy in the presence of strategic customers

This paper establishes a game‐theoretic model to investigate the effect of strategic customers on the business model choice between the reselling and the agency models under a markdown pricing policy. We find that the supplier always prefers the agency model whereas the retailer's preference depends on the commission rate, the proportion of strategic customers, and the supplier's inventory level. Results also reveal that regardless of the business model, the proportion of strategic customers, the patience level of strategic customers, and the supplier's inventory level are three driving forces in the markdown pricing policy. Finally, different from the conventional wisdom that suggests the centralized decisions always outperform the decentralized decisions, we find that the decentralized decisions under the agency model can achieve the same performance as the centralized decisions when the supplier has a relatively small inventory level. Surprisingly, when the supplier has a relatively large inventory level, the agency model outperforms the centralized model since the former leads to lower prices and stimulates more demand.

Demand Postponement with Strategic Service Customers

We study capacity decisions in a service environment with seasonal arrival rates and strategic customers. Due to seasonality, the firm uses a postponement strategy where compensation is given to the peak-time customers who cannot be served in the peak time and agree to delay service. We extend the analysis of demand postponement to the case with strategic customers, i.e., off-peak customers who may pretend to be peak-time customers purely to receive the postponement discount. Focusing on the capacity and discount decisions for the service provider, we develop a game-theoretic model to study when the service provider should consider using a postponement strategy in the presence of strategic customers. At the first stage, the service provider decides on the capacity level and discount. At the second stage, strategic customers decide whether to show up in the peak time. We find that strategic customers exhibit a “follow-the-crowd” type of behavior. Unsurprisingly, we find that the presence of strategic customers does not benefit the service provider. More interestingly, we find that the presence of strategic customers may benefit the peak-time customers, unlike the inventory literature with strategic (arrival-timing) customers, in which the supplier’s response is usually detrimental to non-strategic customers. In the presence of strategic customers, the supplier needs to offer a lower discount or build more capacity than without strategic customers. If the supplier does not have control over the discount value, she might need to build excess capacity to prevent the strategic customers from showing up in the peak time.

Optimal pricing and ordering decisions with reference effect and quick replenishment policy

AbstractThis paper considers a revenue management problem in which a retailer determines joint pricing and ordering decisions for a single product over a two‐period selling horizon. The goal is to investigate the single and combined influences of reference effect (RE) and quick replenishment (QR) policy on the customers' purchasing behaviors, the retailer's optimal decisions, and the total profit of two periods. We first propose a discrete‐time joint pricing and ordering model with RE and QR policy in the presence of strategic customers. Then we extend to the other two models, in which the retailer does not implement the QR policy and the retailer ignores the customers' RE, to, respectively, explore the values of RE and QR policy to the retailer. Several significant results and managerial insights are derived through theoretical analysis and numerical experiments. Implementing the QR policy could help the retailer cut nearly half of the initial order quantity and improve the profit significantly, especially when the strength of RE is large and the storage cost is high. With the QR policy, if the retailer ignores the RE that factually exists in the customers' minds, she would set lower prices, place fewer initial order quantity, and lose much profit, especially when she sells luxury products or the customers' purchasing behaviors are strongly affected by RE.

Demand Postponement with Strategic Service Customers

We study capacity decisions in a service environment with seasonal arrival rates and strategic customers. Due to seasonality, the firm uses a postponement strategy where compensation is given to the peak-time customers who cannot be served in the peak time and agree to delay service. We extend the analysis of demand postponement to the case with strategic customers, i.e., off-peak customers who may pretend to be peak-time customers purely to receive the postponement discount. Focusing on the capacity and discount decisions for the service provider, we develop a game-theoretic model to study when the service provider should consider using a postponement strategy in the presence of strategic customers. At the first stage, the service provider decides on the capacity level and discount. At the second stage, strategic customers decide whether to show up in the peak time. We find that strategic customers exhibit a follow-the-crowd type of behavior. Unsurprisingly, we find that the presence of strategic customers does not benefit the service provider. More interestingly, we find that the presence of strategic customers may benefit the peak-time customers, unlike the inventory literature with strategic (arrival-timing) customers, in which the supplier's response is usually detrimental to non-strategic customers. In the presence of strategic customers, the supplier needs to offer a lower discount or build more capacity than without strategic customers. If the supplier does not have control over the discount value, she might need to build excess capacity to prevent the strategic customers from showing up in the peak time.

Optimal order quantity in the presence of strategic customers

We consider a monopolistic retailer of a seasonal product who faces random demand of strategic customers. The selling season is segmented into two periods, a normal selling period and a sales or discounted price period. The retailer preannounces the total sales quantity and the prices offered in these periods. Customers have identical product valuation in the normal selling period. However, a customer’s valuation of the product in the sales period is his private information and is assumed to be randomly distributed. A strategic customer decides to either buy in the normal selling period or wait for the sales period by comparing his expected utility in the respective periods. In a market of such strategic customers, a retailer faces the problem of determining his optimal order quantity. Hence, in this research we consider a two-stage decision making process; in the first stage, the retailer decides the initial order quantity and in the second stage strategic customers decide to either buy during the normal selling period or wait for the sales period. The focus of this work is to determine the order quantity of the retailer which maximizes his expected profit in a monopoly market of such decision making strategic customers. We determine the optimal order quantity of the retailer given these optimal decisions of strategic customers by extending the work of Song et al. (Prod Oper Manag 28(5):1305–1319, 2019). Numerical illustrations and parametric analysis are performed to get insights about the developed model.

Impact of Adopting Quick Response and Agility on Supply Chain Competition with Strategic Customer Behavior

A growing trend towards computerization and competition in supply chains results in uncertainty and quick variability that make the decisions difficult for both levels of retailers and manufacturers. In this paper, two Bi-Level Stackelberg models are developed under non- and agile conditions in the presence of strategic customers. Our main novelty approach in this paper is to consider both levels competing with each other in a sequential game to determine the optimal production and order quantities and prices with and without agile abilities. In addition, both proposed models are simplified single-level using the Karush-Kuhn-Tucker (KKT) approach. Then, they are remodeled by the Robust Optimization technique due to existing uncertain parameters. To have a better assessment of the models’ efficiency and applicability, they have been implemented in a real case and finally, the results are compared and analyzed.

A service system with perishable products where customers are either fastidious or strategic

Many service systems in the fast food industry consist of two types of customers: fastidious and strategic. A fastidious customer will always join the queue and wait for a fresh product, while a strategic customer, depending on queue length and on stock availability, may either join the queue for fresh products, purchase an inventoried pre-prepared (perishable) product, or balk. When the system is empty, the server produces pre-prepared items up to a pre-determined capacity level. The server may assign different prices to pre-prepared items and to fresh products in order to maximize expected profit, while taking into account revenue from selling food, sojourn and balking costs, capacity costs, and costs associated with food deterioration. We formulate and analyze this stochastic queueing-inventory system and derive its steady-state probabilities using matrix geometric methods. Our economic analysis, which follows a Stackelberg game and Nash equilibrium, shows that the presence of strategic customers is always beneficial for non-strategic customers, and can also be beneficial for the operating server. Moreover, in some cases, the server benefits from charging a higher price for less-fresh (pre-prepared) products than for fresh items, and even when pre-prepared items are offered at a discount, the discount may have a stronger positive effect on non-strategic customers' utility than on strategic customers' utility. Notably, in some cases, the percentage increase in the customers' utility (as compared with the case in which pre-prepared food is not offered) may be even higher than the percentage increase in the server's expected profit, even though the server is the one who controls the decision variables.

Two-Period Pricing with Selling Effort in the Presence of Strategic Customers

This paper investigates the implications of selling effort upon a monopolistic seller’s optimal pricing decisions and its performance in the presence of strategic customers. In our model, strategic customers can anticipate future price discounts and decide on when to purchase products from the seller over two periods. The direct impact of selling effort is to induce more customers to purchase in the first period. Two pricing policies are under consideration: (i) preannounced pricing under which the seller commits to a price path over two periods in advance; (ii) contingent pricing under which the seller can dynamically set a price in each period. The analytical results demonstrate that preannounced pricing always dominates contingent pricing from the seller’s perspective. Moreover, compared with the results under contingent pricing, selling effort is enhanced but market demand is shrunk under preannounced pricing. Through sensitivity analysis, we further find that when customers become more willing to postpone their purchases to the second period, the seller should reduce its selling effort in the first period if the demand-enhancement effect of selling effort is sufficiently low.

Dual Channel Management with Strategic Customers

Problem definition: We study how a manufacturer shall manage the retail channel in the presence of a direct channel and strategic customers. We consider two periods, in which the manufacturer can sell through a single retailer or two different retailers in sequence with static or dynamic wholesale pricing. The retailer(s) sets the retail prices that the manufacturer’s direct channel needs to match, while the customers in each channel may strategically time their purchases. Academic/practical relevance: Although direct channel and strategic customer behavior are widely present in practice, how to manage the retail channel in such circumstances has not been investigated. Methodology: We derive complete subgame perfect Nash equilibria of this dynamic game. Results: We find that when the manufacturer can only sell through a single retailer, dynamic wholesale pricing is preferred when the market share of the retail channel is large, as it mitigates double marginalization. On the other hand, static wholesale pricing can alleviate strategic customer waiting, which is the most effective when the customers’ waiting incentive is intermediate. If the manufacturer can sell through two retailers, it can cause intertemporal competition between them, which may not only alleviate double marginalization but also counteract customer waiting. As such, the results change structurally. Selling through a single retailer can be optimal only under static wholesale pricing and only if the market share of the retail channel is small and the customers are moderately strategic. In the other scenarios, selling through two retailers with dynamic (static) wholesale pricing is optimal when the customers’ waiting incentive is weak (strong), and static wholesale pricing may be more preferred when the market share of the retail channel becomes larger. We also find that in the presence of strategic customers, the manufacturer fully controlling the retail prices may not be optimal for itself or the supply chain, particularly when the market share of the direct channel is large. Managerial implications: These results can be practically useful for managing the retail channel in the modern business environment.

Inventory Decisions in the Presence of Strategic Customers: Theory and Behavioral Evidence

We consider the inventory decision of a retailer facing strategic customers. We develop a behavioral theory that accounts for reference dependence, which makes predictions on how the presence of strategic customers leverages retailer behavior. Specifically, the reference‐dependent retailer shall decrease her order quantity when there are more strategic customers in the population. As such, the conventional pull‐to‐center bias for newsvendor is generalized, since the presence of strategic customers may induce the retailer to pull her order further below the center even when the production cost is low. Furthermore, increasing proportion of strategic customers reduces the retailer's ordering bias under low cost, yet amplifies it when the cost is high. Our subsequent experiments find pull‐below‐center effect, validate the theoretical predictions, and establish the asymmetry of reference dependence with the estimated behavioral parameters. We also study extensions to our model and carry out robustness checks of our experimental results.

Profit of a Supply Chain versus its Decentralization in the Presence of Strategic Customers

A strategic customer is the customer who can intentionally delay purchases and wait for products on sale. In the selling season of seasonal products, there is usually a sale period because product valuation drops significantly. We study the behavior of strategic customers in a supply chain selling seasonal products. Contrary to strategic customers, myopic customers buy or leave, and do not wait. Previous researches with myopic customers suggest that decentralization decreases the supply chain’s profit. We simulate supply chain members’ interaction and learning processes of decisions to investigate how decentralization of a supply chain affects its profit in the presence of strategic customers. We study the effect of customer heterogeneity over product valuation and find that: (i) The profit of the decentralized supply chain is higher than that of the centralized one when the degree of customer heterogeneity is high, the expected product valuation is low, and customers have low time sensitivity. (ii) Decentralization retards delaying purchases of strategic customers. In addition, we find that the endogenization of the sale price can weaken the negative effect of strategic customers on the supply chain’s profit.

Dynamic Product Rotation in the Presence of Strategic Customers

Dynamic product rotation is perceived as a useful lever to increase sales. The effect over individual customers is, however, unclear: more choice in the future may induce them to postpone a purchase if the current offer is not sufficiently appealing, hoping to buy a better product in the future; moreover, visiting a store to learn about a new product may be costly, thereby diminishing the value of product updates. We analyze a model of strategic customer behavior in the face of a rotating product offering, with variable assortment depth. We find that the customers’ visit and purchase decisions follow a relatively simple structure: a customer should visit the store only when new products have been introduced and purchase a product if the value it provides is higher than a threshold. We then use this structure to examine the retailer’s optimal product-rotation policy. The structure of the optimal policy depends on product-rotation costs and capacity constraints. When capacity constraints are tight, the retailer spreads out product introductions throughout the selling season. Strategic customers tend to become more demanding (their purchasing thresholds increase) as the frequency of product rotation increases, so the marginal benefit of each additional product rotation decreases. In contrast, the purchasing thresholds for myopic customers—who buy the first item that fits their needs—are independent of the number of products introduced in the season. As a result, when the cost of product rotation is low, the frequency of product updates is higher when the retailer sells to strategic customers, whereas the opposite is true when the cost of product rotation is high. When capacity constraints are less stringent and product-rotation costs are convex in the assortment depth, we find that the level of product variety should increase as the season progresses when the firm sells to strategic customers, while the level of product variety stays constant over the season when faced with myopic customers. Finally, when there are no capacity constraints, it is optimal to introduce all products in a single period. This paper was accepted by Serguei Netessine, operations management.

Dynamic Pricing for Heterogeneous Time-Sensitive Customers

A core problem in the area of revenue management is pricing goods in the presence of strategic customers. We study this problem when customers are heterogeneous with respect to their initial valuations for the item and their time sensitivities, i.e., the customers differ in both their initial valuations and the rates at which their initial valuation decreases with delay in purchase. We characterize the optimal mechanism for selling durable goods in such environments and show that delayed allocation and dynamic pricing can be effective screening tools for maximizing firm profit. We also investigate the impact of production and holding costs on the optimal mechanism.

Dynamic Pricing for Heterogeneous Time-Sensitive Customers

Problem definition: A core problem in the area of revenue management is pricing goods in the presence of strategic customers. We study this problem when customers are heterogeneous with respect to their initial valuations for the good and their time sensitivities—that is, the customers differ in both their initial valuations and the rates at which their initial valuation decreases with a delay in the purchase. Academic/practical relevance: In many settings, especially in fashion and electronic retail, a customer’s valuation for the product is time-sensitive and decreases over time. In these situations, customers are not only different in terms of their initial willingness to pay for these products when they are first introduced to the market, but they are also different in terms of how rapidly they lose interest in these products. We show that when a firm sells products in such settings, it can realize significant benefits by incorporating dynamic pricing, even in the absence of demand uncertainty. Methodology: Dynamic mechanism design. Results: We characterize the optimal mechanism for selling durable goods when the customers differ in both their initial valuations and the rates at which their initial valuation decreases. We show that delayed allocation and dynamic pricing can be effective screening tools for maximizing firm profit and can also increase social welfare. We also investigate the impact of production and holding costs on the optimal mechanism. Managerial implications: Our work shows how firms can exploit scenarios in which their customers have time-sensitive valuations and are forward-looking to achieve a win–win, by both generating additional revenue and improving social welfare.

On Revenue Management with Strategic Customers Choosing When and What to Buy

We study a network revenue management model in which a decision maker offers multiple products, which consume capacitated resources, for sale to customers over a finite horizon. The decision maker employs an anonymous posted-price policy, and arriving customers strategize on (a) when, and (b) which product to purchase so as to maximize their utility. Customers’ choices are driven by heterogeneous product valuations that decay over time at heterogeneous rates. Both initial valuations and decay rates are private information. We derive for all nonanticipating dynamic pricing policies an upper bound to expected revenues. We use our bound to conduct theoretical and numerical performance analyses of static pricing policies. In our theoretical analysis, we derive a constant factor guarantee for the performance of static pricing, for the classical fluid-type regime where inventory and demand grow large. Our numerical analysis shows static pricing to be able capture at least 75%-90% of maximum possible expected revenue under a wide range of realistic problem parameters, and suggests that static pricing is likely to perform better under higher product proliferation and lower load factor in the presence of strategic customers.