• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.179-192
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• 2008

This case study shows constructing the knowledge-based system using a rule-based approach for detecting illegal transactions regarding money laundering in the Korea Financial Intelligence Unit (KoFIU). To better manage the explosive increment of low risk suspicious transactions reporting from financial institutions, the adoption of a knowledge-based system in the KoFIU is essential. Also since different types of information from various organizations are converged into the KoFIU, constructing a knowledge-based system for practical use and data management regarding money laundering is definitely required. The success of the financial information system largely depends on how well we can build the knowledge-base for the context. Therefore we designed and constructed the knowledge-based system for anti-money laundering by committing domain experts of each specific financial industry co-worked with a knowledge engineer. The outcome of the knowledge base implementation, measured by the empirical ratio of Suspicious Transaction Reports (STRs) reported to law enforcements, shows that the knowledge-based system is filtering STRs in the primary analysis step efficiently, and so has made great contribution to improve efficiency and effectiveness of the analysis process. It can be said that establishing the foundation of the knowledge base under the entire framework of the knowledge-based system for consideration of knowledge creation and management is indeed valuable.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.333-336
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• 2006

E-marketplace is a kind of B2B e-Business system that supports business transactions among companies. If e-marketplace is revitalized, we expect not only the development of related industry but also decrease of transaction cost among companies. It is necessary for the introduction and revitalization of e-marketplace in tourist industry from this point of view. Participants of tour e-marketplace are tour-related companies(travel agencies, lodging enterprises, shipping enterprises, etc.). Also tourists want to search a variety of tour products or contents. So tour e-marketplace has characteristics of B2C e-Business systems as well as B2B e-Business systems at once. The purpose of this study is to classify success factors that determine characteristics of tour e-marketplace through statistics survey from e-marketplace factors related tourism websites. First of all, we analyze success factors of B2B and B2C e-marketplace. Then we will set up influence factors of tour e-marketplace and conduct a survey about success factors of tour e-marketplace. Therefore, we could expect to find these good attributes in tour e-marketplace success through logistic regression and decision tree analysis from source data.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.12
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• pp.1721-1732
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• 2007

This study identified types of information source, and explored a path model for consumer information search by shopping attributes in the context of online decision making. Participants completed self-administered questionnaires during regularly scheduled classes. A total of 219 usable questionnaires were obtained from respondents who enroll at universities in the southwestern region of the United States. For data analysis, factor analysis and path model estimation were used. Consumer information source was classified into three types for online clothing purchases: Online source, Offline retail source, and Mass media. Consumers were more likely to rely on offline retail source for online clothing purchases, than other sources. In consumer information search by shopping attributes, online sources were more likely to be related to transaction-related attributes(e.g., incentive service), whereas offline retail source(e.g., displays in stores, manufacturer's catalogs and pamphlets) were more likely to be related to product and market related attributes(e.g., aesthetics, price) when purchasing clothing online. Also, the path model emphasizes the effect of shopping attributes on traditional retailer search behavior, leading to online purchase intention for clothing. This study supports consumer information search by attributes, and discusses a managerial implication of multi-channel retailing for apparel.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.12
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• pp.1866-1877
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• 2008

The purposes of this study were to identify the dimensions of perceived dissatisfaction, complaining behavior, and long-term orientation of customers about the Internet fashion shopping mall, and to empirically examine the effects of each dimension of perceived dissatisfaction and complaining behavior on long-term orientation. For this study, questionnaires were administered to 275 Internet shopping mall customer. To analyze collected data, descriptive analysis, factor analysis, Cronbach's $\alpha$, correlation analysis, and regression analysis were used. Major findings were as follows. First, college students were found to mainly complain of dissatisfaction at product quality, refunding/changing/maintenance repair, price, contract, delivery, and payment after transaction with the Internet shopping mall. Second, customer dissatisfaction was found to have high correlation with complaining behavior and partly with customer neglect or exit. Third, higher customer dissatisfaction was found to increase customer complaining behavior in general. Finally, higher complaining behavior was found to have connection with lower customer loyalty and higher customer neglect and exit.

• Journal of Global Scholars of Marketing Science
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• v.8
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• pp.61-87
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• 2001

Internet, which has been developed as a new exchange revolution, forms a huge virtual exchange market, and the innovative electronic commerce has completely broken off the way of existing goods distribution. This study begins with an awareness of the importance of customer retention to keep winning over the competition in internet shopping mall. In order to explain of the customer retention between individual and internet shopping mall, the study introduces first a satisfaction on shopping followed by an awareness of the importance of customer retention, and looks into a formation process of trust, satisfaction, and relationship orientation occurred by the offer of valuable convenience to customers. The study also explores the influence on shopping value by the characteristics with which internet shopping mall can bear, unfold by a cause and effect relationship the degree of shopping satisfaction, trust, and relationship orientation, and inquires a question to find out how to fuse the characteristics for internet retention. Therefore, this study has the following purposes: After examining prior research for the characteristics of internet shopping mall, it presents a possibility to connect shopping value with customer retention in light of theoretical system on characteristic elements derived from emotional and utilitarian perspectives. In order to achieve the purposes, the characteristics of internet retailing shop included site design, virtual reality, web awareness, customer concern, merchandise search, information supply, product value, and transaction system. Hypotheses were set up for the relationship with these characteristics and substantially analyzed. To prove this research, we analyzed collected data in which customers had experienced in shopping at internet shopping mall and discussed strategic current issues about its analytic results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.463-474
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• 2016

As IT environment has changed, paths of information security in financial environment which is based on IT have become more diverse and damage caused by information leakage has been more serious. Among security incidents, personal information leakage incident is liable to give the greatest damage. Personal information leakage incident is more serious than any other types of information leakage incidents in that it may lead to secondary damage. The purpose of this study is to find how much personal information leakage incident influences corporate value by analyzing 21 cases of personal information leakage incident for the last 15 years 1,899 listing firm through case research method and inferring investors' response of to personal information leakage incident surveying a change in transaction before and after personal information leakage incident. This study made a quantitative analysis of what influence personal information leakage incident has on outcome of investment by types of investors by classifying types of investors into foreign investors, private investors and institutional investors. This study is significant in that it helps improve awareness of importance of personal information security by providing data that personal information leakage incident can have a significant influence on outcome of investment as well as corporate value in Korea stock market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.518-527
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• 2016

The housing market is changing continuously according to the place and time and these changes have a ripple effect across various fields. On the other hand, the amount of housing that is consumed in the region also acts as a central cause of price movement. Moreover, the cause of variations in the housing market can be separated according to the characteristics of the housing consumer. In addition, the individual characteristics of the consumer varies according to the region. As a result, a study on the regional causal relationship of the housing market is underway. Although significant research has been done on the domestic home sales market, there has been limited research on the housing charter market. Therefore, in this paper, regional causal relationship of the housing market in the Gangnam and Gangbuk area in Seoul and Gyeonggi Province was analyzed using the vector error correction model, and is segmented by housing sale market and housing jeonse market. In addition, housing sale and housing jeonse of Gangam, Ganbuk and Gyeonggi province are defined as analysis variables, and time series data is the monthly material of June 2003 to November 2015. The results of the analysis, in the case of the housing sale market, showed that fluctuations in house prices in Gangnam area have a major influence on the fluctuations in house prices in the surrounding region. Similarly, in the case of the housing jeonse market, it was found that the jeonse price of Gangnam area has a significant impact on the jeonse price of housing in the surrounding area.

• Korean Journal of Human Ecology
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• v.16 no.6
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• pp.1197-1209
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• 2007

The advance of digital contents industry shifts the focus of consumptions; from analogue to digital ones. It gives significant impact on individual life as well as overall society and culture, and it leads to the increased consumption of digital contents. Nevertheless, current digital contents industry fails to secure the sufficient consumer protection systems including relevant rules and laws which regulate the distribution, use, and other transaction activities of digital contents and the efforts, on the part of contents providers, to provide information to consumers and to protect them. Digital contents, by its nature, is different from the existing products so that its nature is likely to cause unique consumer problems totally different from the offline transactions and the electrical transactions of existing products. This study, therefore, aims to identify the possible problems which may be incurred by consumers in their use of digital contents, specify the types of consumer damages, and provide the underlying materials to improve the systems related to digital contents and take legally complementary measures for consumer protection. To identify the types of consumer damages, this study analyzed the results from consumer counselling cases, experts opinion survey, and FGI. For consumer damage cases, this study analyzed the consumer complaints received by open consumer counselling sites of the Korea Consumer Agency and Seoul Electronic Commerce Center. For experts opinion survey, it conducted questionnaire survey of the group of experts from digital contents manufacturers or providers, and those who treated consumer damages directly. For FGI analysis, it organized a panel of students and employees who had used digital contents to understand the types of consumer damages. The results of this study can be summed up as follows. Based on the results from consumer counselling cases, experts opinion survey, and FGI analysis, the consumer damages related to digital contents can be classified, in their nature, into economic or financial damages (25 cases), emotional or psychological ones (15 cases), time-related ones (7 cases), physical ones (4 cases), and privacy-related ones (i.e. leakage of personal data)(3 cases). More specifying the types of damages, damages can be subdivided into contract-, charge-, maintenance-, use-, individual-related ones and other ones. Among them, both contract- and charge-related damages appeared only in the economic or financial damages, whereas user-specific individual damages appeared only in physical and emotional or psychological ones. On the other hand, maintenance- and use-related damages and other ones were observed in both categories of economical or financial damages and time-related ones. Use- and privacy-related damages, in particular, caused emotional or psychological damages.