• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.473-478
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• 2007

As customers' participation in B2C e-commerce increases, customers are getting more involved in the delivery of services, which may even go beyond the completion of service transactions. To examine such proactive extra-role online customer behavior, we delve into organizational citizenship behavior framework, which has been recently extended to examine customer citizenship behavior (CCB) in the service market area. Although CCB is vital for online retailing success, MIS and e-commerce research efforts have generally focused on the customer's customer coproduction that are customer in-role behaviors. Moreover, although the effect of information created by anonymous strangers on other customers increase, interpersonal trust research have focused on only the relationship between a seller and a customer. Therefore, this study attempts to answer two research questions: What are motivational factors that affect CCB? How differently do the two kinds of interpersonal trusts (trust in online retailer and trust in customers) influence customers?

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.324-331
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• 2005

In this paper, we propose a new customer profile model based on customer behavior in Internet shopping mall. The proposed technique defines customer profile model based on customer behavior information such as click data, buy data, and interest categories. We also implement CBCPM(Customer Behavior-based Customer Profile Model) and perform extensive experiments. The experimental results show that CBCPM has higher precision, recall, and F1 than the existing customer profile model.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.3
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• pp.63-82
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• 2014

The main objective of this research is to investigate whether the RFM (recency-frequency-monetary value) information of a customer's redemption behavior of loyalty points can improve the prediction of future value of the customer. The conventional measurement of customer value has been primarily based on purchase transactions behavior although a customer's future behavior can be also influenced by other interactions between the customer and the firm such as redemption of rewards in a loyalty program. We theorize why a customer's redemption behavior can influence her future purchases and thereby the customer's total value based on operant learning theory, goal gradient hypothesis, and lock-in effect. Using a dataset from a major book store in Korea spanning three years between 2008 and 2010, we analyze both purchase transactions and redemption records of over 10,000 customers. The results show that the redemption-based RFM information does improve the prediction accuracy of the customer's future purchases. Based on this result, we also propose an improved estimate of customer lifetime value (CLV) by combining purchase transactions and loyalty points redemption data. Managerial implications will be also discussed for firms managing loyalty programs to maximize the total value customers.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.127-139
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• 2010

Customer behavior pattern discovery is the fundament for conducting customer oriented services and the services management. But, the composition, need, interest and experience of customers may be continuously changing, thereof lead to the difficulty in refining a stable description of their consistent behavior pattern. This paper presented a new method for the behavior pattern discovery from a changing collection of customers. It was originally inspired from the swarm intelligence of ant colony. By the adaptive clustering, some typical behavior patterns which reflect the characteristics of related customer clusters can extracted dynamically and adaptively.

• Culinary science and hospitality research
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• v.11 no.1
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• pp.70-86
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• 2005

This study is a research on eating-out customer's behavior of inquiring information through the supporting research on the searching information behavior of the customers who visit family restaurants. The result was deduced that the types of eating-out customer's behavior of searching information were different to age, sex, education, incomes, etc., based on demographic analysis. In addition, this study investigated the satisfaction with information and the information types of eating-out customers with verification.

• Korean Management Science Review
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• v.23 no.3
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• pp.183-194
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• 2006

In this paper, we propose a customer profiling technique based on customer behavior for personalized products recommendation in Internet shopping mall. The proposed technique defines customer profile model based on customer behavior Information such as click data, buying data, market basket data, and interest categories. We also implement CBCPT(customer behavior based customer profiling technique) and perform extensive experiments. The experimental results show that CBCPT has higher MAE, precision, recall, and F1 than the existing other customer profiling technique.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.338-345
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• 2023

Nowadays, many companies are striving to turn customer value into business value. Customer Relationship Management is a management system that develops effective and efficient marketing strategies by classifying customers in detail based on their information, i.e. databases, and consists of various information technologies. To implement this management system, a customer integration database must be established, and customer characteristics (buying behavior, preferences, etc.) must be analyzed with the databases established and the behavior of each customer must be predicted. This study aims to systematically manage a large amount of customer data generated by companies that apply Customer Relationship Management, in order to develop data design and data governance strategies that should be considered to increase customer value and even company value. We mainly looked at the characteristics of customer relationship management and data governance, and then explored the link between the field of customer relationship management and data governance. In addition, we have developed a data strategy that companies need to perform data governance for customer relationship management.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.143-146
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• 2004

With the rapid growth of e-commerce, marketers are able to observe not only purchasing behavior on what and when customers purchased, but also the individual Web usage behavior that affect purchasing. The richness of this information has the potential to provide marketers with an in-depth understanding of customer. Using commonly available Web log data, this paper examines Web usage behaviors at the individual level. By decomposing the buying process into a pattern of visits and purchase conversion at each visit, we can better understand the relationship between Web usage behavior and purchase decision. This allows us to more accurately forecast a shopper's future purchase decision at the site and hence determine the value of individual customers to the siteAccording to our research, not only information seeking behavior but also visiting duration of a customer and participative behavior such as participation in event should be considered as important predicators of purchase decision of customer in a cosmetic internet shopping mall.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.6
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• pp.37-46
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• 2015

This study investigates the relationships among product quality, consumer behavior, customer satisfaction, and customer loyalty on environmental viewpoint. For the empirical study it has been attempted to collect data by using a questionnaire including 18 questions. The respondents who had experienced using green or environmental products were 212 college students. In my research 167 valid cases are selected for a data analysis by SPSS based hierarchical regression analysis technique. The results show that green product quality had significant impacts on customer satisfaction and customer loyalty, and cost factor of green consuming behavior had moderating effects between customer satisfaction and customer loyalty. Empirical results imply that green product quality contributes to customer satisfaction and customer loyalty. Additionally, green consumer behavior on customer satisfaction increase customer loyalty.