• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.9-10
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• 2001

Simulation has been evolved with the advance of computer and technique of modeling application systems. Early simulations were numerical analysis of engineering models known as continuous simulation, analysis of random events using various random number generators thus named as Monte Carlo simulation, iud analysis o(\\\\`queues which are prevalent in many real world systems including manufacturing, transportation, telecommunication. Discrete-event simulation has been used far modeling and analyzing the systems with waiting lines and inefficient delays. These simulations, either discrete-event, continuous, or hybrid, have played a key role in industrial age by helping to design and implement the efficient real world systems. In the information age which has been brought up by the advent of Internet, e-business has emerged. E-business, any business using Internet, can be characterized by the network of extended enterprises---extended supply and demand chains. The extension of value chains spans far reaching scope in business functions and space globally. It also extends to the individual customer, customer preferences and behaviors, to find the best service and product fit for each individual---mass customization. Simulation should also play a key role in analyzing and evaluating the various phenomena of e-business where the phenomena can be characterized by dynamics, uncertainty, and complexity. In this tutorial, applications of simulation to e-business phenomena will be explained and illustrated. Examples are the dynamics of new economy, analysis of e-business processes, virtual manufacturing system, digital divide phenomena, etc. Partly influenced by e-business, a new trend of simulation has emerged called agent-based simulation, Agent-based simulation is a technique of simulation using software agent that have autonomy and proactivity which are useful in analyzing and integrating numerous individual customer's behavior. One particular form of agent-based simulation is swarm. This tutorial concludes with the illustration of swarm or swarm Intelligence applied to various e-business applications, and future directions and implications of this new trend of simulation.

• Asia Marketing Journal
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• v.7 no.1
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• pp.21-42
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• 2005

Customer Relationship Management (CRM) is a corporate marketing strategy maintaining and managing customers. And with CRM companies maximize the customer's value through a series of processes of new customer retention, VIP customer retention, customer value increase, potential customer activation, and customers for lifetime by collecting the customer information and taking advantage of it effectively. In particular, as the competitive environment is changing rapidly and getting more intense, maintaining the customer retention through customer churn management becomes more important in order to increase the customer value for maximizing the company's profit and to build up the relationship with customers. For example, the financial industry has managed the customer churn with the concept of customer segmentation. Recently the customer retention and churn management is becoming increasingly important in all business fields as well as financial industry since the companies expect the effect of preventing the customer churn by identifying characteristics of customers. However, despite the increasing interest and importance of the management of the customer churn, not many of studies are systematically executed by analyzing the data of customer churn. In this study we analyze the actual data of CRM activities for the customer retention, specifically the data of TV home-shopping. By doing so, we hope to identify the differences of demographic attributes and transaction specific characteristics in consumer behaviors between the churning customer and the retained customers. In addition, we try to find out the variables which can impact the churning of the customers and to predict the churn rate of individual customer through our proposed model of customer churn. In the end, based on our findings we suggest the possible marketing strategies for TV home-shopping companies.

• Korean Journal of Human Ecology
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• v.19 no.3
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• pp.523-536
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• 2010

This study analyzed the effects of demographic factors and sensory experiences of customers on customer satisfaction, using data from 302 customers in the Pusan, Ulsan, and Kyungnam areas who have visited family restaurants within 3 month period prior to the study. This study defined the sub-factors of sensory experience as vision, sound, smell, taste, and touch, and defined the sub-factors of customer satisfaction as main services, supplementary facilities, services related to sales promotion, served food, and the location of a restaurant. The study revealed that each evaluation score of the sub-factors of sensory experience and each evaluation score of the sub-factors of customer satisfaction was a little higher than the middle score. Respondents evaluated taste as the highest score among the sub-factors of sensory experience and evaluated main services as the highest score among the sub-factors of customer satisfaction. In terms of the effects of the sub-factors of sensory experiences and demographic factors on overall customer satisfaction, more positive taste experience, vision experience, and sound experience led to higher overall customer satisfaction and the married group in terms of marital state gave higher evaluation scores on overall customer satisfaction than the counterpart. In terms of the effects of the sub-factors of sensory experiences and demographic factors on individual customer satisfaction, consumers' sensory experiences were shown to exert far greater influences than the demographic variables.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.687-696
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• 2011

Customer Lifetime or Customer Lifetime Value is a essential metric of differentiated CRM marketing and differentiated marketing strategy as a company core competency. However, customer lifetime used in companies is easily obtained from a confined simple customer attrition rate at some specific time point regardless of customer characteristics. In this study, in order to overcome the constraints of previous simple methods and to make practical use of it in industries, we suggest a method that estimates a customer lifetime using a customer segment based survival analysis with the censored data of customers; in addition, we apply this method to A mobile telecom company data. A method using customer segment based survival analysis is suggested in this study 1) includes all customers having different subscription dates, 2) reduces individual error, 3) can reflect trends after the observed time point and is more realistic.

• Journal of Distribution Science
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• v.13 no.7
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• pp.85-90
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• 2015

Purpose - Global trends play a part to change the structure of the fashion industry. In particular, companies attempting to conduct innovative marketing centering on such products as SPA brands are growing into global companies. SPA stands for "Specialty Store Retailer of Private Label Apparel", meaning its activities are fully integrated from manufacturing through sales, including material procurement design, product, distribution, inventory management, and final sales. For this reason, more understanding of individual corporate profitability is very sensitive to consumer's attitudinal changes. The effects that corporate marketing activities on customer lifetime value through brand attitude were analyzed based on a structural equation model. Rust suggested value equity, brand equity, and relationship equity as customer equity driver. The study examines Chinese consumer because China is the fastest growing fashion market in the world. Research design, data, and methodology - The survey targeted Chinese college student age 20s. Only respondents who had purchased SPA brands in the past year were included for this research. A total of 303, except for 47 missing data of 350 distributed questionnaires were included in this research. The questionnaire is consists of six part to measure value, brand, relationship equity, attitude toward brand, purchase intention and demographic characteristics. This research conducted exploratory factor analysis and reliability test. To verify research hypotheses, structural equation model test was conducted. As for customer equity, diversified models in consideration of the scope of acquisition data, a method of collection of data, influencing factor, and predictability were suggested based on a net present value model. However, the history of customer equity study is relatively short, and sufficient empirical analyses have not been conducted, so more integrated analysis is required. In this study, the concept of driver suggested by Rust was applied to figure out the effects that consumer's attitude has on customer equity. The customer equity driver suggested by them consists of brand equity, value equity, and relationship equity. Results - This study reveals that value equity and brand equity have a positive influence on relationship equity. And, relationship equity has a positive influence on purchase intention through brand attitude. However, value equity and brand equity do not influence on brand attitude. Conclusion - The results of this research generated following implications. First, SPA brands need to take advantage of their value equity such as perceived low price and up-to-date fashion style to attract Chinese young consumer. Second, strong brand equity promises dominants position in the competitive market. As Chinese fashion market grows rapidly, SPA brands can consider branding strategy such as flagship store and celebrity marketing enhancing brand image. Third, the core concept of customer equity strategy is to maintain a relationship with their expecting and existing customers. The relationship equity is built by brand equity and value equity. When SPA brands serves product and service meet with individual customers, customers have intimacy to the brands.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.1-20
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• 2008

Based on the multi-attribute utility theory (MAUT), we present a survey method to measure consumer preferences. The multi-attribute utility theory has been used to make decisions in OR/MS field; however, we show that the method can be effectively used to estimate the demand for new services by measuring individual level utility function. Because conjoint method has been widely used to measure consumer preferences for new products and services, we compare the pros and cons of two consumer preference survey methods. Further, we illustrate how swing weighing method can be effectively used to elicit customer preferences especially for new telecommunications services, Multi-attribute utility theory is a compositional approach for modeling customer preference, in which researchers calculate overall service utility by summing up the evaluation results for each attribute. On the contrary, conjoint method is a decompositional approach, which requires holistic evaluations for profiles. Partworth for each attribute is derived or estimated based on the evaluation, and finally consumer preferences for each profile are calculated. However, if the profiles are quite new and unfamiliar to the survey respondents, they will find it very difficult to accurately evaluate the profiles. We believe that the multi-attribute utility theory-based survey method is more appropriate than the conjoint method, because respondents only need to assess attribute level preferences and not holistic assessment. We chose swing weighting method among many weight assessment methods in multi-attribute utility theory, because it is designed to perform in a simple and fast manner. As illustrated in Clemen and Reilly (2001), to assess swing weights, the first step is to create the worst possible outcome as a benchmark by setting the worst level on each of the attributes. Then, each of the succeeding rows "swings" one of the attributes from worst to best. Upon constructing the swing table, respondents rank order the outcomes (rows). The next step is to rate the outcomes in which the rating for the benchmark is set to be 0 and the rating for the best outcome to be 100, and the ratings for other outcomes are determined in the ranges between 0 and 100. In calculating weight for each attribute, ratings are normalized by the total sum of all ratings. To demonstrate the applicability of the approach, we elicited and analyzed individual-level customer preference for new telecommunication services-WiBro and HSDPA. We began with a randomly selected 800 interviewees, and reduced them to 432 because other remaining ones were related to the people who did not show strong intention for subscription to new telecommunications services. For each combination of content and handset, number of responses which favored WiBro and HSDPA were counted, respectively. It was assumed that interviewee favors a specific service when expected utility is greater than that of competing service(s). Then, the market share of each service was calculated by normalizing the total number of responses which preferred each service. Holistic evaluation of new and unfamiliar service is a tough challenge for survey respondents. We have developed a simple and easy method to assess individual level preference by estimating weight of each attribute. Swing method was applied for this purpose. We believe that estimating individual level preference will be quite flexibly used to predict market performance of new services in many different business environments.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.135-148
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• 2003

In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.7
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• pp.907-921
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• 2013

This study shows the importance of human factors in relationship marketing by revealing the influence of Hanbok salesperson's attributes on relationship quality and long-term relationship orientation at the time when a culture is newly understood. A questionnaire surveyed 376 women living around the Seoul and Daejeon Metropolitan areas from February to March 2013. SPSS WIN 20.0 and AMOS 20.0 programs analyzed the gathered data. We review the influence of Hanbok salesperson's attributes (expertise, ethics, communication skill, customer orientation, similarity, and likeability) on relationship quality and behavioral intention. The research indicates that only customer orientation and Hanbok salesperson's expertise attributes influence relationship quality. All the attributes had a positive influence; in addition, the relationship quality had a significant influence on customer loyalty. However, communication skill, similarity, and likeability did not influence relationship quality. Customer orientation, which provides a customized service based on the recognition of individual customer trends and expertise in developing a relationship with customers, are important factors to form relationship quality and loyalty.

• Journal of Distribution Science
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• v.17 no.6
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• pp.15-23
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• 2019

Purpose - This study evaluates the effects of e-marketing communication on brand loyalty in the mobile communication market in Kosovo. given that no prior studies of this nature are found. It explores the relationships, between e-marketing communication, brand image and customer satisfaction, and their impact on the brand loyalty. Research design, data, and methodology - The research model of four constructs was developed. The data was collected via online surveys, 423 completed surveys from mobile subscribers were received. To test the relationship between the individual variables and multiple variables with the dependent variable several test were performed including data validity and reliability, bivariate correlation, simple linear and multiple regression analysis using stepwise method. Results - Positive significant effect of e-Marketing communication on brand loyalty, as well its significant effect on both brand image and customer satisfaction, which subsequently have significant impact on brand loyalty as it was confirmed by previous studies. Conclusions - The findings confirm that e-Marketing communication is key factor in building well perceived brand image, fostering customer satisfaction, and leading to customer commitment and loyalty towards brands of mobile operators in Kosovo.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.445-453
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• 2002

Past and current customer behavior is the best predicator of future customer behavior. This paper introduces a procedure on personalized defection detection and prevention for an online game site. The basic idea for our defection detection and prevention is adopted from the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behavior (i.e. trim-out their usage volume) before their eventual withdrawal. For this purpose, we suggest a SOM (Self-Organizing Map) based procedure to determine the possible states of customer behavior from past behavior data. Based on this representation of the state of behavior, potential defectors are detected by comparing their monitored trajectories of behavior states with frequent and confident trajectories of past defectors. The key feature of this study includes a defection prevention procedure which recommends the desirable behavior state for the ext period so as to lower the likelihood of defection. The defection prevention procedure can be used to design a marketing campaign on an individual basis because it provides desirable behavior patterns for the next period. The experiments demonstrate that our approach is effective for defection prevention and efficient for defection detection because it predicts potential defectors without deterioration of prediction accuracy compared to that of the MLP (Multi-Layer Perceptron) neural network.