Heterogeneous Customers Research Articles

The optimal decision of service provider considering extra waiting area value-added services – pooled or dedicated?

Service providers often provide chargeable extra waiting area value-added services (EWS) to enhance customers’ waiting experiences, e.g., in VIP and business lounges at airports, train stations, luxury brand stores, etc., to improve profitability. The impact of EWS on customers’ value perception is subject to their heterogeneous sensitivities and also depends on the reference effect due to EWS’ observable status. This paper investigates the optimal level of EWS and price decisions for a service provider facing heterogeneous customers, namely the EWS-preferring (E-type) customers and ordinary (O-type) customers, considering the observable status (observable vs. unobservable) of EWS using pooled or dedicated capacities in operations through M/M/1 queue models. The optimal price and level are solved and the optimal profit is calculated under each scenario. It is found that customers’ heterogeneous sensitivities play an important role in the price and intensity decisions. Offering observable EWS to leverage the reference effect in customers’ value perception can lead to a higher profit. Furthermore, using pooled capacity in operations with EWS is more profitable when service costs are higher. These results offer significant managerial implications and provide practical guidelines for service providers regarding the intensity of EWS, service price, and whether EWS should be observable to customers through pooled or dedicated capacities.

Service pricing and charging strategy for video platforms considering consumer preferences

AbstractHeterogeneity of consumer preferences is a crucial feature of the platform economy, and service price and charging strategies crucially influence customer choice behavior. To enhance consumer experience and boost product value, video platforms offer value‐added services, also known as premium membership features. For instance, Youku's benefits of value‐added service include seamless video playback, high‐definition picture quality, and exclusive episodes. This study constructs a two‐stage Hotelling model from monopoly to duopoly competition. Considering the heterogeneity of consumer preferences for product quality and platform usage costs, we analyze the impact mechanism of the customer preferences heterogeneity and the service pricing of video platform enterprises. In addition, we analyze the Nash equilibrium strategies of competing enterprises in the competition scenario, and we consider two charging strategies: freemium and bundled sales. The results indicate that the consumer sensitivity factors significantly impact the optimal pricing of services and the profits of enterprises and that this impact varies with the heterogeneity of customer composition and the type of charging strategies. For the optimal solutions in a competition scenario, the proportion of quality‐sensitive customers is positive for the freemium model, whereas it is negative for the bundled sales model. Furthermore, we analyze the impact of heterogeneous customer composition on the choice of charging strategy utilizing numerical analysis. This study provides a theoretical basis and decision support for product development and value‐added service pricing that can be applied to video platform enterprises and other media platforms.

Pricing heterogeneous products to heterogeneous customers who buy sequentially

This paper considers optimal pricing in a system with limited substitutable resources, such as certain goods or services. Prices for the different resources have to be set and then customers with heterogeneous preferences show up sequentially. Customers, of n types, select an item from the m available resources, depending on their valuations of the resources and the prices. The goal is to analyze this optimization problem, characterize a set of candidates to optimal solutions and provide methods for solving it. We prove that this problem is NP-hard to approximate within a factor O(n1-ε)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^{1-\\varepsilon })$$\\end{document} for any fixed ε>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon >0$$\\end{document}. Another important contribution is to prove that, the space of prices (which in principle is a continuous domain in Rm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {R}}^m$$\\end{document}), can be reduced to a finite set of vectors of cardinality mm-2nm2m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m^{m-2}n^m2^m$$\\end{document}. For a deterministic version of the problem, where the customer types are known to the firm, we provide a mathematical program that chooses the best set of prices. We report extensive computational results showing the usefulness of our exact approach to solve medium size problems with up to 200 customers and different assortments of products and customer types. We then show how to approximate the stochastic model by a small number of solutions of deterministic scenarios solved using a mixed-integer linear program.

A behavioral study of capacity allocation in revenue management

We investigate how human decision-makers handle the two-class capacity allocation problem—a foundational revenue management problem and a cornerstone for more sophisticated scenarios. We consider the problem with ordered and unordered arrivals, and then introduce a simplified version with single up-front decision. We explore the decisions across two price ratios, symbolizing heterogeneous vs. more homogeneous customer bases. We find that decision-makers ineffectively allocate capacity, resulting in suboptimal revenues and underutilization of RM potential. Theoretically, the decision-making format for the capacity does not matter, whereas we find that decision-makers set higher protection levels when making an up-front decision rather than sequential decisions and earn higher revenues for heterogeneous customers. Decision-makers perform poorly in sequential decision-making, and heuristics can be useful in improving performance. Moreover, we observe several regularities in decision-making. With higher customer heterogeneity, decision-makers tend to accept customers who would have been declined by the optimal policy, indicating they are less demanding. Conversely, with more homogeneous customers, they tend to turn away customers who would have been accepted by the optimal policy, implying they are more demanding. Lastly, we investigate whether the decision-making is affected by feelings of regret and find strong effects for both winner’s and loser’s experienced regret.

Return strategies and channel pricing in omni-channel retail considering heterogeneous customer perceptions

As Internet technology advances, the retail industry is increasingly adopting omni-channel operational strategies. This research delves into the interplay between return losses and customer perceptions of online products, and how these factors influence profit-maximizing omnichannel retailers’ optimal return strategy and channel pricing. Previous studies have primarily focused on cross-channel aspects and have not thoroughly explored the impact of return loss on profitability in an omni-channel context. The study assumes that profit-maximizing retailers allow consumers to choose their purchase channels based on perceived utility of different channels, while adhering to the retailer’s stipulated return strategy. To this end, we introduce three distinct return strategies (returning products via the original purchase channel, offline return, and online return) for omni-channel retailers. We aim to identify the most profitable return strategy by considering diverse return losses associated with each channel and consumers’ perceptions of online products. Our analysis identifies scenarios in which a particular return strategy either boosts or reduces retailer profit, thereby facilitating optimal channel pricing decisions. Our findings reveal that customers’ purchasing channel selections are affected by their perception, retailer’s return strategy, and net return loss. We also find that no matter what kind of operation scenarios the retailers adopted, the retailers should let all customers return to the online channel to generate maximum profit when the difference between the retailer’s online and offline net return loss is less than the difference between the customer’s online and offline return hassle cost. Conversely, an offline return strategy proves more profitable. Our findings are supported by numerical simulations, guiding omni-channel retailers in making informed decisions regarding return strategies and channel pricing.

Как отпугнуть клиента и получить прибыль: фильтрация в системе массового обслуживания

We consider a simple exponential queueing system with a finite capacity and heterogeneous customers. Type I customers are patient, they join the queue if the system capacity allows it. Type II customers are impatient, so that they refuse to join the queue if the waiting time is too large (so-called wait-based balking). The system makes profit servicing customers, and the profit depends on the type of the customer. We show that if type II customers bring less profit than type I customers then the administration may, in certain cases, increase profit by slowing down the service (decreasing the service rate). It makes the system unattractive for relatively unprofitable type II customers who stop forming the queue and thus leave the place for more profitable customers who otherwise would not have joined the queue due to capacity restriction.

The order of the factors matters: How digital transformation and servitization integrate more efficiently

This article draws on existing debates on standardization versus adaptation to propose two possible pathways for digital servitization. On the one hand, the standardization pathway posits that digital transformation enables servitized firms to make their service-based business model more standardized, and as a result, scalable. On the other hand, the adaptation pathway advocates that servitization enables highly digitalized firms to make their digital offerings more adaptable to heterogeneous customer needs, and as a result, customizable. We investigate which of these two paths integrates more effectively, and which one is thus likely to prevail in the long run. We use a purpose-built survey of 127 Spanish product firms to test these relationships using partial least squares structural equation modelling (PLS-SEM), and test single- and multi-mediation models. The results corroborate the existence of both pathways, but also suggest that the standardization pathway contributes more to performance than the adaptation pathway. This is consistent with historical transitions in adoption (services existed before digital transformation) and services being dependent on digital technologies delivered remotely. These findings suggest the benefits of customization in digital servitization are lower than previous studies seem to imply, and provide important managerial implications.

Retraction Note: Research on heterogeneous customer hotel supply chain channel selection model based on game theory

Retraction Note: Research on heterogeneous customer hotel supply chain channel selection model based on game theory

Group buying in multiple channels with consideration of heterogeneous customers

Group buying can create high demand for firms in a short period by offering significant price discounts to attract more consumers who are unfamiliar with the items and enhance their willingness to make purchases. However, group buying may negatively affect sales profits; thus, firms must consider the pros and cons of participating in a third-party group-buying platform. This study considers a firm that offers a new product through the direct channel, i.e., brick and mortar stores, and may also be on a third-party group-buying website; additionally, it develops a two-stage game model in which heterogeneous consumers with different perceived values for the new product are classified into strategic and group-buying consumers to investigate whether cooperating with a group-buying website would earn more profits for the firm. The contribution of this study is the proposed game model for adequately assessing both brick and mortar and group-buying sales under channel competition. The results of the investigation show that high network externalities can elevate retail prices and group-buying quantities but lower wholesale prices. If only one channel is considered, cooperating merely with the group-buying website is more profitable than cooperating with a single sales channel. In addition, third-party group-buying websites can offer appropriate promotional programs to improve demand and profit, and as a result, a win–win situation can be achieved.

Integrated location and routing for cold chain logistics networks with heterogeneous customer demand

The critical interdependence between facility location and vehicle routing is a fundamental component of cold chain logistics management (CCLM). Furthermore, integrating information within CCLM has the potential to enhance operational efficiency, reduce costs, improve risk management, and elevate product quality, ultimately ensuring that temperature-sensitive goods are delivered in best condition. This paper introduces a novel non-linear multi-objective model designed to concurrently optimize warehouse facility location and vehicle routing, addressing the challenges inherent in cold chain logistics processes. The model seeks to minimize the aggregate costs related to transportation, facility location, and delivery tardiness. The study accounts for several pragmatic assumptions to address real-world scenarios: multiple delivery requests per customer, handling mixed commodities, and distributing mixed commodities using a single vehicle. This paper firstly studies simultaneous pickup and delivery with multiple requests and heterogeneous customer demands, each of which should be preserved in a different range of temperatures and needs different vehicle types. The epsilon-constraint method is employed to validate the proposed model, and a set of advanced, hybrid multi-objective evolutionary algorithms (MOEA) are presented to tackle the problem in a real-world context. A comprehensive set of performance metrics is utilized supported by rigorous statistical testing.

Multisource Data Driven Product Ranking Model With Heterogeneous Customers

Multisource Data Driven Product Ranking Model With Heterogeneous Customers

Optimal Pricing Design for Coordinated and Uncoordinated IoT Networks

An Internet of Things (IoT) system can include several different types of service providers, who sell IoT service, network service, and computation service to customers, either jointly or separately. A deep understanding of complicated coupling among these providers in terms of pricing and service decisions is critical to the success of IoT networks. This paper studies the impact of the provider interaction structures on the overall IoT system with heterogeneous customers. Specifically, we first study a generic IoT scenario with three interaction structures: coordinated, vertically-uncoordinated, and horizontally-uncoordinated structures. Despite the challenging non-convex optimization problems involved in modeling and analyzing these structures, we successfully obtain the closed-form optimal pricing strategies of providers in each interaction structure. We further extend the analysis to a specific IoT scenario with local computation capability (e.g., Internet of Vehicles (IoV)). We prove that the coordinated structure is better than two uncoordinated structures for both providers and customers, as it avoids selfish price markup behaviors in uncoordinated structures. Between the two uncoordinated structures, when customers' demand variance is large and utility-cost ratio is medium, vertically-uncoordinated structure is better than horizontal one for both providers and customers, due to the complementary providers' competition in horizontally-uncoordinated structure. Counter-intuitively, we identify that providers' optimal prices do not change with their costs at the critical point of customers' full participation in the vertically-uncoordinated structure.

A Charge Curve and Battery Management System Aware Optimal Charging Scheduling Framework for Electric Vehicle Fast Charging Stations With Heterogeneous Customer Mix

A Charge Curve and Battery Management System Aware Optimal Charging Scheduling Framework for Electric Vehicle Fast Charging Stations With Heterogeneous Customer Mix

Equilibrium Behavior in Tandem Markovian Queues with Heterogeneous Delay-Sensitive Customers

We consider a system of two unobservable Markovian queues in tandem with strategic customers, who are heterogeneous regarding their delay sensitivity. The customers decide upon arrival whether to balk or join the system and receive service in the first queue or in both queues. We analyze their equilibrium strategic behavior which is specified by double-threshold strategies regarding their delay sensitivity parameter (one threshold for each queue). Moreover, we compare the strategic behavior of the heterogeneous customer population with its homogeneous counterpart. We complement our theoretical results with numerical experiments and provide managerial insights into the optimal control of the system parameters.

Reinforcement Learning for Dynamic Pricing of Shared-Use Autonomous Mobility Systems Considering Heterogeneous Users: Model Development and Scenario Testing

A key aspect of the success of shared-use autonomous mobility systems will be the ability to price rides in real time. As these services become more prevalent, it becomes of high importance to detect shifts in behavior to quickly optimize the system and ensure system efficiency and economic viability. Therefore, (1) pricing algorithms should be able to price rides according to complex underlying demand functions with heterogeneous customers and (2) the algorithm should be able detect nonstationary behavior (e.g., changing customers’ willingness to pay) from its previously learnt decisions and alter its pricing mechanism accordingly. We formulate a dynamic pricing and learning problem as a Markov decision process and subsequently solve it through a reinforcement learning (RL) algorithm, with heterogeneous customers accepting the trip characteristics (price, expected wait time) probabilistically. Insights from a fixed fleet operation of an autonomous private ridesourcing system in Chicago are presented. Given our formulation of the demand model, the algorithm learns in 25 days, increasing revenue by 90% and decreasing customer wait times by 90% compared to day 5. After gathering insights from the RL algorithm and applying optimal static pricing (i.e., a constant specific surge multiplier), we find that RL can achieve near 90% optimality in revenue. The RL algorithm, nevertheless, proves to be robust. Two scenarios are tested where a sudden shock occurs or customers slowly change their willingness to pay, illustrating that RL can quickly adapt its parameters to the situation.

On strategic customers with correlated utility attributes: Effects and information benefits

In this paper, we investigate a service system where heterogeneous customers arrive with varying assessments of service reward and unit waiting cost. Additionally, we allow for correlation between a customer’s valuations for service reward and unit waiting cost. We first model the service facility as an unobservable M/M/1 queue and demonstrate that the correlation between the previously mentioned utility attributes affects various critical performance metrics. If the expected net utility of the customers, when exactly half of the customers join the facility, is positive (negative), then the equilibrium arrival rate increases (decreases) with the correlation. We also show that the expected utility of arriving customers decreases with correlation. Finally, we examine the observability of queue length within our model and calculate regions of model parameters where disclosing queue length information benefits the customers and the service provider the least or the most.

Pricing and investment decisions when facing heterogeneous customers under different supply chain power structures

This paper examines the pricing and joint investment decisions of a two-echelon supply chain with a manufacturer and a retailer under power structures. A manufacturer invests in a specific quality level to produce a quality product. Meanwhile, a retailer invests in the extended warranty service provision as an after-sales service. This study investigates the interplay of these two investments assuming two heterogeneous customer demands exist. Three decision-making analytical power structure models are developed, namely centralized, manufacturer-led decentralized, and retailer-led decentralized models. In both decentralized models, the Stackelberg game determines the optimal price and investment decisions to maximize member's profit. However, independently optimal decision-making with these models does not coordinate the supply chain and its members. Thus, this study proposes wholesale price contracts considering power structures to coordinate effectively and achieve a win–win situation. A numerical analysis has been performed to determine the optimal level for quality investment, extended warranty length, pricing, and supply chain performance. Furthermore, a sensitivity analysis explores key parameter's impact on heterogeneous customer demands and supply chain performance.

Customer segmentation, pricing, and lead time decisions: A stochastic-user-equilibrium perspective

Multiple price/lead time options are often used as a competitive tool in supply chain operations when customers have heterogeneous price and lead time preferences. We study a two-echelon supply chain network consisting of manufacturers and retailers facing customers that differ in their price- and time-sensitivity. We examine how many price/lead time options should be provided by manufacturers and retailers under decentralized and centralized supply chain management with time-cost tradeoffs. We adopt a stochastic-user-equilibrium (SUE) approach in a supply chain network by incorporating discrete choice theory and using a multinomial logit-based variational inequality to express equilibrium conditions. This is one of the first papers to study time-cost tradeoffs in a supply chain network by introducing an SUE approach. One critical part of our analysis is the establishment of concavity of profit functions, which allows for analytical derivation of the equilibrium strategies. Another critical part of our analysis is the development of SUE conditions in decentralized and centralized supply chain networks. We demonstrate that the variance of heterogeneous customers’ time-sensitivity distribution plays a crucial role in customer segmentations in a time-cost tradeoff supply chain. We find that under SUE conditions, there exists a unique equilibrium in the decentralized and centralized supply chain networks, respectively. We conduct comparative statics analyze to demonstrate the effect of SUE and UE conditions and the influence of decentralized versus centralized supply chain management paradigms. We show how the SUE approach can be applied to supply chain network management with time-cost tradeoffs. Our approach can be extended to other tradeoff decision problems in supply chain network management, especially those in which customers are heterogeneous.

Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations

The rapid development of electric vehicles (EVs) has increased the demand for public fast-charging stations (FCSs). Multiple self-interested charging service operators (CSOs) in an urban transportation network compete with each other through pricing signals to maximize their respective profits, where the information about customer demands and heterogeneous preferences work as important decision criteria. With the prevailing data transaction, the information superiority differences between CSOs may significantly affect the competitive pattern in the charging market. In this study, a Nash-Stackelberg-Nash game framework is established to investigate the strategic pricing of CSOs under different information distribution scenarios. The customer response pattern, namely, the routing and charging choices of EV drivers to pricing signals, is described via a computationally efficient extended stochastic user equilibrium model comprehensively considering heterogeneous customer preferences and decision stochasticity. Then, aiming at different data forms, both parameter-driven and sample-driven CSO pricing models are proposed. The pricing models are reformulated as a tractable single-level mixed-integer linear program, and the game equilibrium is solved alternately via Gauss-Seidel iteration. Numerical simulations are conducted to analyze the impact of single CSO information superiority and double CSO information distribution on CSO profitability and customer experience in both data forms. The influences of endogenous factors like CSO data amount and processing ability, as well as exogenous scenario parameters like customer preference distribution and perception error distribution, are discussed.

Modularization of the front‐end logistics services in e‐fulfillment

AbstractThis study exploits service modularity in front‐end logistics services in e‐fulfillment, from a customer‐centric approach, particularly in order management, delivery, and return. Through an online survey of UK customers, the service priorities of 494 respondents via AHP (Analytic Hierarchical Process) were analyzed. Extracting customers' service priorities, ordering behavior, and demographic information as input data, the clustering algorithm KAMILA (KAy‐means for MIxed LArge data sets) was further applied. The three identified customer clusters (multichannel shoppers, infrequent shoppers, and online fans) provide preliminary evidence on how commonality and variability aspects of service modularity in front‐end logistics services can optimize the number of service options and their performance levels. Therefore, our study, building on value co‐creation and modularity, proposes a systematic way of exploiting service modularity for the customer segmentation process that addresses heterogeneous customer preferences cost‐efficiently and uncomplicatedly. Furthermore, we provide a framework for the governance of front‐end logistics services, guiding outsourcing decisions. Accordingly, it reveals the implications of customer priorities and service decomposition logic choices on value creation. Finally, the propositions formulated aim to develop theoretical foundations for explaining how the heterogeneity in customer priorities for logistics services can be managed with modularity, creating value both for customers and retailers.