Customer Behavior Model Research Articles

Analysis of Customer Digital Behavior through the Canvas Business Model: An Initial Proposal

The main objective of this work is to propose a framework containing a sequence of actions and steps that can be used to assist managers and organizations in decision-making and improvement of their managerial and commercial performance. This work followed quantitative and qualitative methodology involving a bibliometric research, meta-analysis through the application of secondary data, bibliographic analysis and the exposure of the proposed model for the approval of managers and professors in the area of management and marketing, linked to a research group of a federal university in Rio de Janeiro. The theoretical contribution of the work was to establish a conceptual link between knowledge about customer behavior and business models. The practical contribution was the elaboration and description of a self-applicable theoretical model in the organizational decision-making environment, based on market and commercial elements present in the daily life of the manager and the organization, especially the small ones. The main conclusion was that the theoretical model was approved when appreciated by managers and professors of the area, however, it still needs to be validated quantitatively and in organizational environment for the generalization of its use.

When to announce the queueing information for bounded rationality customers: a discrete-event–based simulation model

In queueing systems where queues are invisible, it is critical for companies to make decisions about the timing of announcing the anticipated delay to customers. In this paper, a simulation model is built to simulate an invisible queueing system, and several queueing scenarios are considered, including different system congestion and different company goals, to explore the impact of different announcement timings. In the modeling of customer behavior, we argue that it is difficult for companies to announce perfectly accurate delay and rarely have customers fully trust the announcement, so the quantal-response model is included to model customers’ probabilistic choice behavior due to the bounded rationality. Meanwhile, we consider customer heterogeneity, assign customers different initial patience and, as an extension, also assume that patience will be updated. We perform simulation experiments and analyze the experimental data to dissect the underlying reasons, and then give sound management suggestions. The experiments show that the optimal announcement timing is different for different scenarios, which shows that in practical decisions, companies should adopt different announcement strategies for different scenarios. What’s more, in some scenarios, delayed announcement at specific time is better than on-arrival announcement, which suggests that when we judge the value of announcement, we should add the definite word of specific timing to the announcement. The breakthrough point of this paper is to consider customers’ bounded rationality and the dynamic patience; meanwhile, it fills the gap of announcement timing research and explores the value of additional announcements after the initial announcement.

User Behavior Analysis and Market Forecasting of Agricultural E-commerce Platforms

Abstract In recent years, the burgeoning interest of enterprises in the e-commerce sector has underscored the necessity for effective analytical tools that monitor user engagement on agricultural products’ e-commerce platforms and provide precise market forecasts. To address this need, the current study proposes an analytical framework and develops a comprehensive market prediction system for agricultural products. Initially, behavioral analysis indices on the e-commerce platform are employed to delineate user behavioral patterns, which include traffic and purchase path analyses. Based on a case study, enhancements are made to the conventional RFM (Recency, Frequency, Monetary) customer behavior model, both strategically and structurally, thus enriching the analysis of user behavior on such platforms. Subsequently, a four-tiered e-commerce market prediction system is established, incorporating a multilayered deep learning network designed to anticipate market trend shifts. The analysis reveals that the application of deviation standardization processing significantly refines the representation of user behavior to the platform’s merchants. Moreover, the implementation of this market prediction system not only reduces processing time from 8 seconds to 4 seconds but also decreases the average relative error by 0.31%. These improvements highlight the system’s enhanced predictive accuracy, confirming its utility in navigating the complexities of the agricultural products market in the e-commerce domain.

НЕЙРО-ЭКОНОМИЧЕСКИЕ И КОГНИТИВНО-ПСИХОЛОГИЧЕСКИЕ АСПЕКТЫ ТЕХНОЛОГИЙ МЕРЧАНДАЙЗИНГА

This article is of a review and analytical nature and is devoted to the problems of applying modern discoveries of neuroscience (neurobiology, neurophysiology, neuroeconomics) in managing customer behavior. In particular, a multipolar model of customer behavior is proposed, combining the emotional-sensory and cognitive-psychological fields of irrationally rational consumer behavior. The authors found that purchasing decisions by visitors to points of sale can be made at three levels: 1) at the emotional and sensory level, dictated by neurobiological processes and being irrational, which are taken unconsciously during the direct neuro-communication interaction of the product and the visitor in the trading halls; 2) at the level of automatic decisions based on empirical experience that are rational – consciously reflective (objective experience) or irrational - unconsciously reflective (subjective experience), while the constancy of the product’s place in the space of the trading floor is a necessary condition for the formation of experience and the manifestation of such behavior; c) at a high cognitive level based on complex cognitive-psychological processes caused by the need to think about and justify rational decisions. They are aimed at reducing the qualitative, financial or other risk that can cause cognitive dissonance, and visitors are characterized by resistance to merchandising tools used at points of sale. The obtained results make a definite contribution to the development of the theory of neuromarketing and contribute to the formation of the neuro-cognitive concept of merchandising. The main provisions of the article may be of practical importance in applied research in the development of new neuromarketing and merchandising tools.

Marketplaces vs Shopping Malls: Today’s Trends and Forecasts

Nowadays in line with traditional off-line trade e-trade develops fast enough. Today’s customers show higher interest and loyalty to marketplaces. The goal of the article is to investigate new institutions being formed on the trade landscape, to analyze standing and current trends and factors influencing the development of shopping malls and marketplaces. The author’s hypothesis implies that in present conditions of trade development marketplaces act as an alternative and key competitors to shopping malls, as earlier shopping malls themselves became an alternative to department stores. Today we can observe that a certain part of customers and purchases leave off-line and choose on-line due to economic, social and geo-political reasons. It was promoted by digital technologies, COVID-19 pandemic and its after-effects, changes in customer behavior model, impact of sanction pressure on consumer market and so on. The author supposes that in case the current trend of fast growth of e-trade continuous (including marketplaces), by 2027 no less than 45% of all purchases will be done at marketplaces, while the share of traditional trade will drop and the role of shopping mall will reduce.

Advancing the marketing‐operations interface in omnichannel retail

Advancing the marketing‐operations interface in omnichannel retail

Characteristics of Customers of Islamic Financial Institutions in the village of Sukopuro, Jabung District, Malang Regency

Islamic Financial Institutions as institutions that were born with the aim of reaching the lower classes in the form of small people's economic institutions in an effort to develop productive businesses and investments in increasing the economic activities of small entrepreneurs based on sharia principles in the form of cooperatives. It is this institution that is rife in Sukopuro village and interacts with the farming community there in providing financing as a form of its operations. The interaction that was formed between the Islamic financial institution and the people of the village of Sukopuro led to the formation of a public perception of the operation of the Islamic financial institution. This perception that has been formed encourages a responsiveness and follow-up that can occur in society. This study aims to identify the characteristics of Islamic Financial Institution (LKS) customers that influence public perceptions of the level of trust and satisfaction of the people of Sukopuro village, Jabung sub-district towards Islamic Financial Institutions (LKS). This research uses quantitative research methods with community research locations in the village of Sukopuro, Malang Regency. Researchers took data with a questionnaire spread technique. The results of this study indicate that the characteristics of customers can be grouped into several criteria, namely gender, age, education, occupation, and income which then form a model of LKS customer behavior in the form of satisfaction and trust from the perceptions that have been formed.The abstract should summarize the content of the paper. Try to keep the abstract below 200 words. Do not make references nor display equations in the abstract. The journal will be printed from the same-sized copy prepared by you. Your manuscript should be printed on A4 paper (21.0 cm x 29.7 cm). It is imperative that the margins and style described below be adhered to carefully. This will enable us to keep uniformity in the final printed copies of the Journal. Please keep in mind that the manuscript you prepare will be photographed and printed as it is received. Readability of copy is of paramount importance.

MULTICRITERIA MODELS IN REVENUE MANAGEMENT

Revenue management (RM) deals with selling the right product to the right customer at the right time at the right price through the right channel to maximise revenue. The innovation of RM lies in the way decisions are made. The performance of revenue management approaches can be evalu- ated against several criteria. Both discrete and continuous multicriteria models can be used to analyse RM. The performance pyramid is a comprehensive, fully integrated per- performance system that captures multiple perspectives such as internal, financial, customer and innovation. The assessment is based on a combina- tion of Analytic Hierarchy Process (AHP), Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) approaches. Customer behavior modeling is gaining increasing attention in revenue management. Customer choice models can be extended with more inputs and more outputs. Evaluation of alternatives can be performed using DEA- based evaluation methods. The search for an efficient frontier in a DEA model can be formulated as a multiobjective linear programming problem. We propose to use an Aspiration Level Oriented Procedure (ALOP) to solve the problem. Keywords: revenue management, performance measurement, multiple criteria, Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Data Envelopment Analysis (DEA), customer behavior, efficient frontier, Aspiration Level Oriented Procedure (ALOP)

The current research aims to identify the relationship between customer behavior modeling and proactive orientation by surveying the opinions of a sample of workers in the city of Sayed Al-Awsiya for visitors in the holy province of Karbala. To achieve this goal, (60) questionnaires were distributed, and after analyzing them statistically. the results of the research concluded that there is

يهدف البحث الحالي إلى التعرف على العلاقة بين نمذجة سلوك الزبون والتوجه الاستباقي عن طريق استطلاع آراء عينة من العاملين في مدينة سيد الاوصياء للزائرين في محافظة كربلاء المقدسة. ولتحقيق هذا الهدف تم توزيع (60) استبانة، وبعد تحليلها احصائيا، توصلت نتائج البحث إلى وجود علاقة ارتباط بين نمذجة سلوك الزبون والتوجه الاستباقي، وكذلك وجود تأثير معنوي لنمذجة سلوك الزبون في التوجه الاستباقي، وبناءً على ذلك توصل البحث إلى العديد من الاستنتاجات والتوصيات أهمها تعمل مدينة سيد الاوصياء للزائرين على ربط الطقوس الدينية والاجتماعية بخدماتها، ويعكس التوجه الاستباقي في المقام الأول استراتيجية المشاريع الجديدة لتجاوز المنافسين، ومن ثم يمكن تفسيره على أنه سلوك رائد، يخلق روح المبادرة.

Service Quality Control using Queuing Theory

Customers are compelled to queue when a service system is at its busiest. This issue not only reduces customer pleasure, but it also causes the company to lose money. For consumer losses, this study proposes a queue model of customer queuing behavior. The goal is to reduce customer losses, hence researchers are looking at queue setup and optimization in random service systems. We developed three queuing models: MG1, GM1, and GG1 for estimating service quality control. We investigate queuing systems for predicting replies for service quality control based on queue models of customer behavior. The study found that the MG1 queue model yields the best service quality, however the GM1 and GG1 results are relatively close behind.

Service Quality Control using Queuing Theory

Customers are compelled to queue when a service system is at its busiest. This issue not only reduces customer pleasure, but it also causes the company to lose money. For consumer losses, this study proposes a queue model of customer queuing behavior. The goal is to reduce customer losses, hence researchers are looking at queue setup and optimization in random service systems. We developed three queuing models: MG1, GM1, and GG1 for estimating service quality control. We investigate queuing systems for predicting replies for service quality control based on queue models of customer behavior. The study found that the MG1 queue model yields the best service quality, however the GM1 and GG1 results are relatively close behind.

ПАНДЕМИЯ COVID-19, ИННОВАЦИИ И КОНКУРЕНТОСПОСОБНОСТЬ ПРЕДПРИ-ЯТИЙ СФЕРЫ УСЛУГ

Nowadays, innovation is becoming increas-ingly important for both scientists and practi-tioners due to the significant increase in the level of global competition and the constant search for customers to maximize the bene-fits from their purchasing decisions. The sub-ject of the study is innovations in the service sector and their impact on the competitive-ness of an organization in the context of the COVID-19 pandemic. The aim of the study is to study the mandatory introduction of inno-vations to increase competitiveness in a pandemic. Research methods: information search, systematic approach and compari-son. More than 30 scientific works of foreign English-speaking scientists have been ana-lyzed. It was revealed that the introduction of innovations at service enterprises in a crisis allows: to try to keep existing customers in a changing environment; consider the intro-duction of innovations as creating competi-tive value for customers, including for attract-ing new customers. It is concluded that in times of severe market disruption, offerings and business models can become obsolete as a result of changes in customer behavior and competitors' service value creation models.

Ways to Measure Loyalty of Service Users

The focus of this study was set on the empirical determination of the differences between the methods of measuring the loyalty of users of services. The research reviewed approaches to evaluating the loyalty of taxi service users in Ho Chi Minh (Vietnam), Moscow (Russia), and Novosibirsk (Russia). The goal implied the segmentation of service users depending on the patterns of conduct and giving advice on the development of loyalty programs. The novelty lies in grouping respondents by age and gender and determining the ways to measure their loyalty. The research findings imply that people under 30 tend to make a choice based on reviews of others. The study developed possible customer behavior models and options for actions depending on their satisfaction and loyalty. This matrix can be used for building a comprehensive strategy to increase the loyalty of their clients. Practical value of the study lies in the possibility of using the data obtained to model the behavior of consumers of different service types based on the proposed loyalty matrix.

RFM-based repurchase behavior for customer classification and segmentation

Customer behavior modeling and classification are well-studied areas for applications in retail. Past studies implemented the purchase behavior modeling based on the physical behavior of a subject. In this research, we apply the recency, frequency, and monetary (RFM) model and data modeling techniques to detect behavior patterns for a customer. Each transaction attributed to a customer is part of one's behavior, and an instance of the feature vector, it is modeled on a set of transactions to constitute repurchase behavior. The proposed scheme is validated by simulating a publicly accessible real-world data set with a need-tailored multi-layer perceptron (MLP) and also support vector machine (SVM) and decision tree classification (DTC) methods. The experiments yield a high customer classification rate of more than 97% for the different numbers of the customers. Empirical analysis shows that eight transactions are sufficient to classify a customer with high accuracy.

Model of the Consumer Switching Behavior Related to Healthy Food Products

This study aimed to examine the effect of customers’ attitudes, subjective norms, and perceived behavior control on their intention to switch to healthy food products. This research also tested brand awareness as a moderator of customers’ behavioral choices to switch to healthy food products by switching behavior. The study was conducted by distributing a questionnaire. It involved 318 participants and employed partial least square regression as the data analysis method. This study shows the significant influence of customers’ attitudes on their switching intentions toward healthy food products, and perceived behavior control significantly influences customers’ switching intentions toward healthy food products. Customers’ perceived behavior control, as well as their intentions to switch toward healthy food products, significantly influences their switching behavior. Brand awareness has a moderate influence on customers’ intentions to switching behavior on healthy food products. This study contributes to developing the model of customer behavior in switching from fast food products to healthy food products. This study reveals that subjective norms do not significantly influence the switching behavior of customers.

The effects of customer perceived value and perceived innovation on green products

Customer behavior of environmentally friendly products becomes marketing attention by implementing a green marketing strategy to improve customer’ green trust. The study concentrates on the correlation of eco-label attribute, perceived innovation, perceived quality, and green trust of a customer. The study was conducted in 2020 with a survey of supermarket’s customers who were familiar with green products. There were 200 customers who were selected randomly; data from a customer was taken by questionnaire. Then, data from the questionnaire was processed by using SEM approach through SmartPLS. The research finding determined that the implementation of an eco-label attribute may influence on customer’ green trust directly through customer perceived quality. Furthermore, it was determined that customer perceived quality could play the mediation role between eco-label attributes and green trust. Besides, it has known that the value of innovation of green products could not be affected by eco-label attributes and it could not affect customer’s green trust. The study provides a recommendation from the model of green customer behavior with mediation focuses on customer perceived quality. The finding can provide important information for marketers and producers who use the environmental issue, and it is implemented to green marketing strategy.

ЕMAIL-МАРКЕТИНГ ЯК ОСНОВНИЙ КАНАЛ ПЕРСОНАЛІЗОВАНОЇ КОМУНІКАЦІЇ ЗІ СПОЖИВАЧЕМ

This article illustrates the importance and effectiveness of the use of Еmail-marketing with the advantages and disadvantages of using in modern Ukrainian companies. The main questions that an Еmail Marketer should answer before planning an advertising mailing are highlighted. The characteristics that distinguish advertising mail from spam are highlighted. A comparison was made between e-mail and other popular channels of communication with the consumer. The current research of the Ukrainian market by the marketing research agency DigData on communication channels and consumer attitudes to advertising mailings is described. Also, such a feature of using e-mail on the Gmail service as filtering еmails by category, in particular in the "Advertising" tab, is described, and a study is considered where the ability of users to use these category tabs is analyzed. The possibilities of Еmail-marketing are considered, in particular through the prism of the main advantage - personalization of the letter in accordance with the interests of the consumer and with the possibility of taking into account previous consumer experience. The comparison of prices for the main methods of distribution (such as SMS and Viber) is given. An experiment was conducted in the service for еmail mailings MailChimp e-mail service to determine the difference in the effectiveness of the e-mails to inform about the upcoming campaign, taking into account the model of customer behavior with previous correspondence. A special role in the experiment presented in the study is devoted to the question of the convenience of receiving letters, namely the day when the client receives the mailing. The main conclusions of the study are indicated, which indicate that by adjusting the mailing to the client, you can get 10.3% better performance. Examples for further research are given and the still unresolved components of the problem of using Еmail-marketing among the mix of other marketing communication channels are highlighted.

Combining disciplines: Dealing with observed and cryptic animal residencies in passive telemetry data by applying econometric decision-making models

Migratory species do not necessarily behave migratory continuously. An important aspect of studying migratory species is therefore to distinguish between movement and resident behavior. Telemetry is a rapidly evolving technique to study animal movement, but the number of data processing techniques to account for resident behavior remains limited. In this study we describe how models that were initially developed to predict human customer behavior, i.e. two-part and three-part models, provide new insights in the movement of migrating eel by accounting for resident behavior apparent from telemetry data sets. In econometrics, two-part models take into account that the decision of a customer to purchase an item and the decision of the customer on the purchase quantity of the concerning product, might be affected by different factors. Similarly, the factors that affect the decision of a fish to migrate or to stay resident might be different from the factors that affect the swimming speed of the fish. Telemetry data of eel movement in the Permanent Belgian Acoustic Receiver Network (PBARN) of the Scheldt Estuary was used. This network with high detection probabilities allowed residencies to be recognized, defined, and introduced as zero values in a movement-residency data set. Two-part models, which consider movement decision, i.e. residency or movement, and movement intensity, i.e. swimming speed, as two different processes or parts of one larger model, outperformed one-part models that do not make that distinction. This underlines the complex migration behavior eels exhibit. These two-part models in turn were outperformed by three-part models that also accounted for cryptic (i.e. unobserved) residencies. While the one-part model identified the tides and the distance from the most upstream gate as most important for movement, the three-part models identified the tides as most important for the movement decision and the distance from the most upstream gate as most important for the movement intensity. Considering movement decisions, cryptic residencies and movement intensity in modeling efforts increased model performance by 9.8%, underlining the importance of acknowledging the potentially complex behavior animals exhibit.

Satisficing Search and Price Competition

The rising prevalence of algorithmic pricing in industry calls for a closer study of market outcomes that arise when these algorithms interact in a competitive environment. In this paper, we study the impact of boundedly rational customer choice on the landscape of joint demand curves in competition, and its consequences on the properties of competing pricing algorithms derived from the online convex optimization framework. In particular, we study price competition in a duopoly under a new customer behavior model motivated by online platforms offering perfectly substitutable services, e.g., ride-hailing. In this model, a customer samples the firms in an idiosyncratically preferred order until she finds one priced below her willingness-to-pay. We exhaustively characterize the equilibrium profile of the resulting pricing game when customers' willingness-to-pay distribution has a monotone hazard rate. We show that these games are plagued by a particular strictly-local Nash equilibrium, in which the price of the firm with a smaller market share is only a local best-response to the competitor's price, when a globally optimal response with a potentially unboundedly higher payoff is available. Through numerical experiments, we show that price dynamics resulting from distributed online gradient descent may often converge to this undesirable outcome, causing the smaller firm to incur linear regret relative to its best-response price. Our results thus suggest that algorithms that adaptively rely on local information for pricing, despite faring well in monopolistic settings, may suffer from serious drawbacks in competitive environments. We finally propose and test a practical resolution of this concern in the context of our model.

IModel

Deriving analytic performance models requires detailed knowledge of the architecture and behavior of the computer system being modeled as well as modeling skills. This detailed knowledge may not be readily available (or it may be impractical to gather) given the dynamic nature of production computing environments. This article presents a framework, called iModel , for automatically deriving and parameterizing analytic performance models for multi-tiered computer systems. Analytic performance models consist of a workload model and a system model. iModel uses system logs and configuration files to generate a high-level characterization of the system; e.g., open queuing network (QN) model versus closed QN model. By harvesting more information from the system logs and configuration files, iModel generates a workload model by inferring user-system interaction patterns in the form of a Customer Behavior Model Graph (CBMG) and generates a system model by discovering system components and their interaction patterns in the form of a Client-Server Interaction Diagram (CSID). iModel includes a library of well-known single-queue and QN models and their solutions stored in an XML-based repository. The generated workload model and system model are compared to the model repository to determine which model in the repository best matches the system’s observable behavior and architecture. This article also presents a black-box optimization approach that is used to derive analytic model parameters by observing the input-output relationships of a real system. This optimization approach can be used in any computer system (multi-tier or not) that can be modeled by single queues or QNs. The important question is whether the automatically generated and parameterized performance model has predictive power, i.e., can the derived model predict the output values that would be observed in the real system for different values of the input? The results presented in this article demonstrate that the analytic performance models derived by iModel are relatively robust and have predictive power over a wide range of input values.