Types Of Customers Research Articles

Is less always more? Conservation, efficiency and water education programs

Water scarcity and stress are not new phenomena for many regions around the world. Problems are exacerbated by growing populations, changing weather patterns and reduced water supplies, associated with longer run climatic change. To help mitigate the problems, consumer conservation programs, including education programs directed at outdoor water use, have been created. We analyze the impact of one such program - the “WaterSmart” workshop offered by the largest water utility in New Mexico located in the southwestern United States. The utility's objective was for residential customers to water to their landscape needs. We find the program's impact varied by time, weather, and customer type. The program reduced average household water use, with impacts decaying quickly up to a two-month lag. Further, while water use declined under mild drought conditions, it increased during extreme drought. Finally, water use always increased for low-usage households. These findings suggest that the program increased residential outdoor water use efficiency, while the conservation impact depended on weather conditions and household characteristics. This suggests that water shortages in the short run could be ameliorated or exacerbated, depending on conditions, as the trade-offs between efficient maintenance of urban vegetation and water saving are considered.

Omni-channel strategies for fresh produce with extra losses in-store

Omni-channel retailing has become increasingly popular among retailers. Thus, whether it is profitable to implement omni-channel operations is regarded as an important question for retailers. In this paper, omni-channel strategies for a fresh produce retailer is investigated in consideration of both fresh produce losses (natural losses and extra losses in-store) and customer types (traditional and online). We analyse the effectiveness of the omni-channel operation by comparing the equilibrium profits and consumer surpluses of three cases: store channel only, buy-online-deliver-to-home (BODH), and buy-online-pick-up-in-store (BOPS). The results show that omni-channel operation will be profitable and more effective when selling fresh produce with a higher loss rate. When the proportion of online customers is higher, omni-channel operation not only improves the retailer’s profits but also increases the consumer surplus. In addition, the BOPS mode can be more profitable than the BODH mode when the cost for home delivery is relatively high, and interestingly, the advantage of the BOPS mode will be smaller with a lower extra loss rate in-store.

Optimization strategy of price‐based demand response considering the bidirectional feedback effect

Time of use pricing strategy can not only reduce the load fluctuation but also improve the power system reliability. Generally, before carrying out a time of use pricing optimization, it needs to perform a period partition optimization for dividing peak-valley periods. In existing researches, however, the period partition optimization and the time of use pricing optimization are implemented independently. Although the output of period partition optimization is used as the input of time of use pricing optimization, the bidirectional feedback effect between period partition optimization and time of use pricing optimization is not considered, which will lead to the existing time of use pricing optimization may not be the globally optimal solution. Therefore, this paper investigates a new optimization method for time of use pricing optimization with the consideration of bidirectional feedback effect, establishes a comprehensive customer satisfaction degree model considering the customer load proportion coefficient, and defines an elastic cofactor to describe the electricity price response difference of multiple types of customers. The Roy Billinton test system is used to verify the correctness and effectiveness of the proposed method in this paper.

Selection of optimum green energy sources by considering environmental constructs and their technical criteria: a case study

Optimum green energy sources selection needs a strategic decision for reducing the use of conventional resources. In the selection of optimum green resources, constraints like technical and customer requirements play an important role. The proposed integrated AHP-QFD approach has been applied to a specific northeastern region, India, to select the optimal green energy sources addressing the technical and customer requirements. AHP is used to calculate the intensity of relative priority for customer requirements. The main reason behind choosing AHP methodology is its reliability and consistency in decision-making process. The prime idea of QFD is to translate typical customer requirements into a significant technical requirement by means of transformation for every stage of development and selection. The relative priority and normalized priority of each technical requirement are computed using QFD transformation. An overall score for each green energy sources alternative is then calculated to select the optimum green energy sources based on conflicting multiple criteria. The proposed integrated methodology reveals that the solar energy is the optimum choice for future green energy investment projects followed by other sources likely hydropower, biomass and biogas, and it also suggests that exhausted source is replaced by the available sources for future clean energy planning. Based on the study findings, this research also provides guidelines for Tripura’s green energy expansion policy for practice. The uniqueness of the present research is to ascertain the relationship between needs and strategies by taking opinions from experts as well as residents.

A Service System with Packing Constraints: Greedy Randomized Algorithm Achieving Sublinear in Scale Optimality Gap

A service system with multiple types of arriving customers is considered. There is an infinite number of homogeneous servers. Multiple customers can be placed for simultaneous service into one server, subject to general packing constraints. The service times of different customers are independent even if they are served simultaneously by the same server; the service time distribution depends on the customer type. Each new arriving customer is placed for service immediately into either an occupied server, that is, one already serving other customers, as long as packing constraints are not violated or into an empty server. After service completion, each customer leaves its server and the system. The basic objective is to minimize the number of occupied servers in steady state. We study a greedy random (GRAND) placement (packing) algorithm, introduced in our previous work. This is a simple online algorithm that places each arriving customer uniformly at random into either one of the already occupied servers that can still fit the customer or one of the so-called zero servers, which are empty servers designated to be available to new arrivals. In our previous work, a version of the algorithm, labeled GRAND(aZ), is considered, in which the number of zero servers is aZ with Z being the current total number of customers in the system and positive a being an algorithm parameter. GRAND(aZ) is shown in our previous work to be asymptotically optimal in the following sense: (a) the steady-state optimality gap grows linearly in the system scale r (the mean total number of customers in service), that is, as c(a)r for some positive c(a), and (b) c(a) vanishes as a goes to zero. In this paper, we consider the GRAND(Z p ) algorithm, in which the number of zero servers is Z p , where p < 1 is a fixed parameter, sufficiently close to 1. We prove the asymptotic optimality of GRAND(Z p ) in the sense that the steady-state optimality gap is sublinear in the system scale r. This is a stronger form of asymptotic optimality than that of GRAND(aZ).

Strategic arrivals to a queue with service rate uncertainty

We study the problem of strategic choice of arrival time to a single-server queue with opening and closing times when there is uncertainty regarding service speed. A Poisson population of customers choose their arrival time with the goal of minimizing their expected waiting times and are served on a first-come first-served basis. There are two types of customers that differ in their beliefs regarding the service-time distribution. The inconsistent beliefs may arise from randomness in the server state along with noisy signals that customers observe. Customers are aware of the two types of populations with differing beliefs. We characterize the Nash equilibrium dynamics for exponentially distributed service times and show how they substantially differ from the model with homogeneous customers. We further provide an explicit solution for a fluid approximation of the game. For general service-time distributions we provide an algorithm for computing the equilibrium in a discrete-time setting. We find that in equilibrium customers with different beliefs arrive during different (and often disjoint) time intervals. Numerical analysis further shows that the mean waiting time increases with the coefficient of variation of the service time. Furthermore, we present a learning agent-based model (ABM) in which customers make joining decisions based solely on their signals and past experience. We numerically compare the long-term average outcome of the ABM with that of the equilibrium and find that the arrival distributions are quite close if we assume (for the equilibrium solution) that customers are fully rational and have knowledge of the system parameters, while they may greatly differ if customers have limited information or computing abilities.

Development and Supervision of Robo-Advisors under Digital Financial Inclusion in Complex Systems

With the rapid development of the market economy, there are more and more projects in the financial industry, and their complexity and technical requirements are getting higher and higher. The development of computer technology has promoted the birth of robot consultants, and it is of great significance to use robot consultants to manage and supervise financial industry projects. In order to further analyze the development and supervision of robo-advisors under the digital inclusive financial system, this paper uses complex systems and clustering algorithms as technical support to carry out research. First, the traditional K-means algorithm is used to select the initial clustering center, to improve the noise and outlier processing capabilities, and to build a data mining system based on the improved algorithm. Then, a product design model for robo-advisors is built and the risks of robo-advisors are analyzed from three aspects: technology, market, and law. Analyzing the performance of the improved K-means algorithm, in the operation of the experimental dataset B, the accuracy of the clustering result after 6 iterations reached 97.08%, which shows that the algorithm has good performance. During the trial operation of the data mining system, the four types of customers of financial institutions were accurately clustered, and it was concluded that the main type of customers who brought benefits to financial institutions was high-income customers accounting for 10.75%. Robo-advisory product models are used to build five risk-level investment portfolios and conduct risk backtests. Except for the growth and income portfolio, other portfolios have consistently outperformed the performance benchmark during the analyzed time period. Running the research system of this paper in a financial institution, comparing the capital budget before and after the operation, found that the system can improve the accuracy of the budget and reduce the risk of the robo-advisor for the financial institution.

A customer portfolio management model that relates company's marketing to its long-term survival.

A typical customer relationship management model is designed to increase the value of a company’s existing customers in the next period. While useful in the short term, such a process, followed blindly period after period, would drive the company out of business when those existing customers all eventually died. In reality, no company would do this. Instead, these short-term models are nested within a long-term view of customer management, and it is long-term customer management that the proposed model addresses. The model assumes that a company has identified a set of customer types across which it needs balance in order to remain viable in the long-term (e.g., a company might wish to maintain a supply of “entry-level customers” in order to eventually replenish its collection of more profitable “loyal customers”). Though the model is applicable in any industry, we illustrate it for automobiles. Results reveal the strengths with which each marketing intervention causes General Motors to attract each of their desired customer types. The model is extended to also reveal differences in the ways that marketing interventions by Ford, Toyota, and Honda change the strengths with which those automakers attract customers.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11747-020-00765-9.

Online Auction Customer Segmentation Using a Neural Network Model

Online auction is a huge growing business. Most of online buyers face a big problem of predicting the seller' s behavior to submit a reasonable price for wining a bid. Unfortunately, auction web sites e.g. eBay provide only a user ID or nickname to identify a consumer. This situation built up a wall between users for truly knowing each other. To overcome such a prob- lem, this research proposes a neural network model called SOM to segment online auction cus- tomers into homogenous groups. Based on the segmented groups, the behavior of online bidders can be divided into three types: patient deals, impulsive deals and analytic deals. To demonstrate the feasibility of the proposed methodology, 1470 records retrieved from Taiwan eBay are used to conduct an empirical study. In conclusion, the percentages of each customer type are 39.3 % (impulsive deals), 27.8 % (analytic deals) and 32.2 % (patient deals). The analyzed result shows that more than sixty percent of bidder's behave rationally and patiently.

The Power of Opaque Products in Pricing

We study the power of selling opaque products, that is, products where a feature (such as color) is hidden from the customer until after purchase. Opaque products, which are sold with a price discount, have emerged as a powerful vehicle to increase revenue for many online retailers and service providers that offer horizontally differentiated items. In the opaque selling models we consider, all of the items are sold at a single common price alongside opaque products that may correspond to various subsets of the items. We consider two types of customers, risk-neutral ones, who assume they will receive a truly random item of the opaque product, and pessimistic ones, who assume they will receive their least favorite item of the opaque product. We benchmark opaque selling against two common selling strategies: discriminatory pricing, where one explicitly charges different prices for each item, and single pricing, where a single price is charged for all the items. We give a sharp characterization of when opaque selling outperforms discriminatory pricing; namely, this result holds for situations where all customers are pessimistic or the item valuations are supported on two points. In the latter case, we also show that opaque selling with just one opaque product guarantees at least 71.9% of the revenue from discriminatory pricing. We then provide upper bounds on the potential revenue increase from opaque selling strategies over single pricing and describe cases where the increase can be significantly more than that of discriminatory pricing. Finally, we provide pricing algorithms and conduct an extensive numerical study to assess the power of opaque selling for a variety valuation distributions and model extensions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Online Resource Allocation Under Partially Predictable Demand

Real-Time Resource Allocation: Beyond Stochastic Demand Modeling

Stochastic monotonicity approach for a non-Markovian priority retrial queue

This paper considers a non-Markovian priority retrial queue which serves two types of customers. Customers in the regular queue have priority over the customers in the orbit. This means that the customer in orbit can only start retrying when the regular queue becomes empty. If another customer arrives during a retrial time, this customer is served and the retrial has to start over when the regular queue becomes empty again. In this study, a particular interest is devoted to the stochastic monotonicity approach based on the general theory of stochastic orders. Particularly, we derive insensitive bounds for the stationary joint distribution of the embedded Markov chain of the considered system.

Consumption values and mobile banking services: understanding the urban–rural dichotomy in a developing economy

PurposeThis study develops a theoretical model of consumption values regarding the technology adoption of mobile banking (m-banking) services, with the financial service sector as the empirical context. This study aims to evaluate whether consumption values influence trust and intention. Furthermore, the authors explore how the consumer type (i.e. urban vs rural) differs in consumption values regarding adopting m-banking services.Design/methodology/approachThe data for this study were gathered from 246 responses collected from individuals living in a country with a developing market, using a survey instrument. The six study hypotheses were tested using partial least squares structural equation modeling.FindingsThe authors found support for effects from functional, epistemic and emotional value on intention. Functional and emotional value significantly influenced trust, while social and epistemic value did not. Social value was a significant moderator between functional value and intention. Consumers who were relatively unconcerned with social value were more motivated by functional value, while consumers who placed great emphasis on social value were motivated by epistemic value. Multigroup analysis showed that the effect from functional value on trust was stronger for urban than rural customers, while the effect from emotional value on trust was stronger for rural than urban customers.Practical implicationsOverall, functional value is the strongest predictor of trust and intention; therefore, bank managers are encouraged to promote m-banking services' functional value to increase trust and attract more users by promoting their companies' m-banking application. M-banking customers can also be classified based on the benefits in which they are most interested.Originality/valueThe study is one of the first attempts to demonstrate empirically how consumption values' dimensions drive m-banking use among different types of customers in a developing market context with a high m-banking penetration rate.

Managing Opportunistic Consumer Returns in Retail Operations

Retailers use lenient return policies to stimulate demand and increase revenues, as such policies help customers assess uncertain product valuations at low (or no) return cost and yield higher equilibrium prices. However, generous refunds also yield unintended consequences such as opportunistic returns, which take place when customers intentionally rent a product for short-term use. Accounting for 11% of all product returns in the United States in 2017, opportunistic returns prompt retailers to seek tactics to address adverse revenue and cost implications. We consider two alternative proposals using a price- and refund-setting newsvendor framework with two customer types: honest returners and renters. The first proposal, targeted-refunds, uses retail analytics firms to distinguish renters from honest returners and implements return policies tailored for each segment. The second proposal, menu-of-refunds, presents customers multiple price-refund pairs and lets them self-select. We compare and contrast the optimal decisions and the profit implications of both proposals with respect to two benchmark settings: one without any renters and another proposal, uniform-refunds, wherein the retailer merely reoptimizes its decisions while acknowledging that renters exist. We characterize the conditions under which the menu-of-refunds proposal separates customer types and thus matching or exceeding the performance of the targeted-refunds proposal. Furthermore, we study several alternative model specifications to confirm that our main finding concerning the effectiveness of the menu-of-refunds proposal is robust. This paper was accepted by Vishal Gaur, operations management.

Customer orientation and e-WOM in the hotel sector

Customers frequently engage in electronic word of mouth (e-WOM) to share their service experience. This is particularly important in hospitality where the role of front-line employees and their level of customer orientation are critical. However, it has not yet been studied how the customer orientation of service employees (COSE) influences e-WOM. This study addresses this gap. Qualitative data were collected from family-run hotels through three independent studies with 25 different participants: focus group, in-depth interviews and an online focus group. This research provides novel findings that contribute to a deeper understanding of how the COSE dimensions play different roles in the three types of customers' e-WOM (positive, neutral and negative). This investigation also contributes to the literature by identifying new factors that could be incorporated in the COSE model, such as attitude, customisation and extra role performance. Some managerial contributions regarding COSE and a future research agenda are proposed.

Joint Assortment Optimization and Customization under a Mixture of Multinomial Logit Models: On the Value of Personalized Assortments

We consider a joint assortment optimization and customization problem under a mixture of multinomial logit models. In this problem, a firm faces customers of different types, each making a choice within an offered assortment according to the multinomial logit model with different parameters. The problem takes place in two stages. In the first stage, the firm picks an assortment of products to carry subject to a cardinality constraint. In the second stage, a customer of a certain type arrives into the system. Observing the type of the customer, the firm customizes the assortment that it carries by, possibly, dropping products from the assortment. The goal of the firm is to find an assortment to carry and a customized assortment for each customer type that can arrive in the second stage to maximize the expected revenue from a customer visit. The problem arises, for example, in online platforms, where retailers commit to a selection of products before the start of the selling season, but they could potentially customize the displayed assortments for each customer type. We refer to this problem as the Customized Assortment Problem (CAP). Letting m be the number of customer types, we show that the expected revenue of CAP can be $\Omega(m)$ times greater than the optimal expected revenue of the corresponding model without customization and this bound is tight. We establish that CAP is NP-hard to approximate within a factor better than (1-1/e), so we focus on providing an approximation framework for CAP. As our main technical contribution, we design a novel algorithm, which we refer to as Augmented Greedy, and building on it, we give a $\Omega(1/ \log m)$-approximation algorithm to CAP. Lastly, we present a fully polynomial-time approximation scheme for CAP when the number of customer types is constant. In our computational experiments, we demonstrate the value of customization by using a dataset from Expedia and check the practical performance of our approximation algorithm.

ОСОБЛИВОСТІ ФОРМУВАННЯ КРЕДИТНОЇ ПОЛІТИКИ ТА КРЕДИТНОГО ПОРТФЕЛЯ КОМЕРЦІЙНОГО БАНКУ В СУЧАСНИХ УМОВАХ

The level of development of production, social infrastructure and the degree of development of society as a whole largely depends on how developed the banking sector of the state is. An important role in fulfilling the tasks is assigned to commercial banks, which can provide credit resources to enterprises and organizations, as well as citizens. In addition, an efficient and stable banking sector is a key factor in the growth of the national economy. The purpose of the article is to determine the features of the formation of credit policy and loan portfolio of a commercial bank in modern economic conditions. The article identifies the features of the formation of credit policy and loan portfolio of a commercial bank in modern economic conditions. It is generalized that the main specific features of credit relations are: their emergence at the stage of redistribution of final income; formation on the basis of return and with the payment of interest; impact on the structure and volume of cash flows and features of the bank loan market. It is proved that the study of the bank loan market involves the analysis of the structure of the loan portfolio, its most important areas are the assessment of the structure of the loan portfolio by type of customers, terms of lending, as well as by type of currency and more. It is determined that the credit policy of a banking institution is one of the main elements of banking policy, which is a strategy and tactics of the bank to attract temporarily free funds in the economy and direct them to credit operations. In the process of credit risk management, an important element is the ability of the bank's management to choose correctly: the sphere of management in which it is advisable to concentrate the bank's lending activities at a given time; "Its client", based on its creditworthiness and other factors that are of paramount importance for the bank in deciding on the possibility of granting a loan, etc. The credit policy of the banking institution determines the priorities in the development and improvement of lending activities, development of the credit process and increase its efficiency and ensures the stability, profitability and liquidity of the bank.

Identity crisis of artists during the Covid-19 pandemic and shift towards entrepreneurship

Objective: The objective of the article is to understand the changes in artists' identity facing the global obstacle caused by the Covid-19 pandemic and the impact of the identity crisis into the shift towards entrepreneurial behaviours. Creative individuals seems to be perfect examples of adjustments to the changing environment. Research Design & Methods: The qualitative research was conducted in form of in-depth interviews with key informants (artists with different entrepreneurial experiences from different countries and cultures) and auto-ethnography. Findings: The Covid-19 pandemic caused a crisis of the artist's identity. Individuals with complex identities deal with the crisis better than sole identity individuals. Artists-entrepreneurs are increasingly looking for new forms of activity during crisis. The Artistry-Creativity-Entrepreneurship Matrix which allow to understand the shifts among complex identity individuals towards one fractional identity in case of a crisis. Implications & Recommendations: The results can be used by: individuals (entrepreneurs, managers, artists) having complex/mixed identities for better understanding of a crisis situation and its impact and possibilities flowing from different layers of human personality with underlining of creativity;2) business looking for new types of customers and/or wanting to understand more complex market participants. Contribution & Value Added: The article describes the unexplored areas of artistry among creative entrepreneurs. Distinction between artistry and creativity is marked here clearly. The application of the theory of aesthetics from the field of artistic creativity as a basis for the analysis of the phenomenon of entrepreneurial creativity, opens up new potential research areas of creativity among entrepreneurs.

Business Feasibility Study for Storage-Based Customer Flexibility Platform of Load-Serving Entity

A storage-based customer flexibility (SCF) platform enables on-demand remote access to a shared pool of utility-scale energy storage resources at substantially low costs. It can provide users the ability to charge and discharge electrical energy to and from remote batteries. This study demonstrates the economic feasibility of an SCF platform business under realistic conditions. Accordingly, the load-serving entity is set as the SCF operator, and the subscriptions from various types of customers and multi-use battery energy storage systems are considered to increase profitability. An economic feasibility verification is provided based on the generating pattern of individual customers, and a robust optimization is performed for the SCF platform operations considering the market price uncertainty, aggregated requests, and customers' willingness to pay for the SCF service. The proposed study is simulated in a California environment.

Shopping behaviour of e - commerce customers on the example of generation Z

Research background: In today's competition markets and different types of customers, it is difficult to achieve a competitive advantage for merchants without knowing the shopping behaviour or intentions of customers. We can classify online store customers into many categories, but we have chosen a category - Generation Z. Purpose of the article: Generation Z can be generally defined as the technically most advanced generation of the entire population. This generation represents demanding customers who seek experiences and shop the mostly online. Generation Z members have relatively high ambitions regarding their careers, lives and they know exactly what, where and how they shop. The aim of this article is to present key factors influencing purchasing behaviour of Generation Z in Slovakia. Methods: Methods were used to meet the aim of the paper, to present the results of the author's primary research, processed by reducing the number of attributes influencing purchasing behavior using factor analysis. The target group of primary research respondents was born between 1997 and 2012. Findings & Value added: Purchasing behavior of the Z generation is mainly influenced by design of the website, the importance of delivery methods together with the possibility of online payment, reviews of merchants and shopping at a well-known seller. An interesting finding is Generation Z's lack of interest in a new wave of trends influencing shopping preferences through popular people on the Internet, called influencers.