• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.167-183
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• 2006

Since data mining attempts to find unknown facts or rules by dealing with also vaguely-known data sets, it always suffers from high error rate. In order to reduce the error rate, many researchers have employed multiple models in solving a problem. In this research, we present a new type of multiple models, called DyMoS, whose unique feature is that it classifies the input data and applies the different model developed appropriately for each class of data. In order to evaluate the performance of DyMoS, we applied it to a real customer churn problem of an automobile insurance company, The result shows that the DyMoS outperformed any model which employed only one data mining technique such as artificial neural network, decision tree and case-based reasoning.

• 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 the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.187-195
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• 2020

This study examined the relationships between the service convenience of restaurant kiosks and customer value, satisfaction, and trust. To this end, analyses were conducted with adults aged at least 18 years throughout the country who reported that they had used restaurant kiosks at least once over the last year. Information on restaurant kiosks was provided to minimize the possibility of errors in the survey. According to the results, first, the decision-making convenience and the convenience of restaurant kiosks were shown to have significant effects on the customer value. On the other hand, the place convenience and transaction convenience had no significant effect on customer value. Second, customer value was shown to have significant effects on trust and satisfaction. Third, trust was shown to have significant effects on satisfaction. If a restaurant considers kiosks in terms of customer service convenience, not labor cost reduction, they will maintain long-term relationships and enhance their differentiation by increasing satisfaction and trust. This study aims to present differentiated marketing strategies and practical implications of restaurants that have introduced kiosks.

• The Journal of Industrial Distribution & Business
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• v.8 no.7
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• pp.71-81
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• 2017

Purpose - The purpose of this study is to investigate the relationship between customer trust and intention to return to the traditional market by using empirical analysis. Research design, data, and methodology - For the empirical analysis of this study, questionnaires were conducted for adults and over 20s. A total of 200 questionnaires were distributed to consumers with experience in traditional markets, and 163 of them were used for empirical analysis. In order to analyze the relationship between customer trust and return visit intention by consumers visiting the traditional market, variables were selected through 10 constructive concepts and revised based on previous studies. The SPSS for win 18.0 was used for data analysis. Results - In order to clarify the relationship between consumer's visit to traditional market and customer's trust, it was found that the tolerance values of both the visiting factors and the consumers' perceptions of traditional markets were higher than .01. In the relationship between visitor's visit to traditional market and customer's trust, price was positively related to customer trust at 0.1% level. Image, product quality and freshness of traditional market were 5% Positive effects were found. These results show that consumers who visit traditional markets gain customer's trust in price, image, product quality and reliability of traditional market. Conclusions - In this study, the results of this study are as follows: First, the effect of customer trust on customer satisfaction is affected by the image (emotion) consumers feel about traditional market, the trust level about the price of goods offered by the market, The confidence level of freshness, the reliability of consumers' connection with the local economy, the consumer's traditional marketplace, and the level of awareness of the service (kindness) of the variables on the independent variables. As a result of the analysis, it was found that among the influence variables of customer trust used in this study, consumers had a high level of confidence about the price of commodities offered by the market, quality of goods, and freshness, The same relationship, market environment such as hygiene or cleanliness, connection with a local economy, service (kindness) of traditional market did not affect consumers' trust in traditional markets.

• Preventive Nutrition and Food Science
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• v.7 no.1
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• pp.95-104
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• 2002

A survey on Korean families′purchase of fresh fruits and vegetables was conducted to increase understanding of families′food decision making. Two hundred ninety seven families with at least one elementary-school age child were selected from four elementary schools to complete a questionnaire during April, 2001 in Gyeongju, Korea. Descriptive statistics, Chi-square test, T-test, and ANOVA statistics were used to analyze the data. The major findings are as follows: Families bought fresh fruits and vegetables at the public markets or the farmer′s markets and a large supermarket most frequently in both summer and winter. Families grew produce by themselves and bought them from farmers directly least frequently in both summer and winter. Families whose housewives had less than middle school education brought fruits and vegetables from Agricultural Co-ops and grew thens by themselves more frequently compared to those who had higher education. On the other hand, families whose housewives had graduated from 4 year college bought fruits and vegetables from large supermarkets more frequently compared to those who had lesser education. "Quality"and "safety production"of fruits and vegetables and "clean environment of store"were the three most important factors when they decided the place to buy fruits and vegetables. "Being treated as a valuable customer" and "ease of finding things"were the two least important factors. Families whose housewives were in their thirties valued "cleanness of the store"and "being treated as a valuable customer"important factors when they decided the place. Families whose housewives had less than middle school education thought that price, availability of public transportation, and availability of locally grown food were the important factors for deciding the place compared to those who had higher education. The price was the factor which low-income families thought important for decision making on the place to buy fruits and vegetables.

• Asia pacific journal of information systems
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• v.16 no.3
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• pp.67-93
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• 2006

Some consumers prefer online and others prefer offline. What makes them prefer online or offline? There has been a lack of theoretical development to adequately explain consumers' channel switching behavior between traditional physical stores and new virtual stores. Through consumers' purchase decision processes, this study examined the reasons why consumers changed channels depending on purchase process stages. Consumer's purchase decision process could be divided into three stages: pre-purchase stage, purchase stage, and post-purchase stage. We used the intention of channel selection as a surrogate dependent variable of channel selection. And some constructs, that is, channel function, channel benefits, customer relationship benefits, and perceived behavioral control, were selected as independent variables. In buying look-and-feel products, it was identified that consumers preferred virtual stores to physical stores at pre-purchase stage. To put it concretely, all constructs except channel benefits were more influenced to consumers at virtual stores. This result implied that information searching function, which is a main function at pre-purchase stage, was better supported by virtual stores than physical stores. In purchase stage, consumers preferred physical stores to virtual stores. Specially, all constructs influenced much more to consumers at physical stores. This result implied that although escrow service and trusted third parties were introduced, consumers felt that financial risk, performance risk, social risk, etc. still remained highly online. Finally, consumers did not prefer any channel at post-purchase stage. But three independent variables, i.e. channel function, channel benefits, and customer relationship benefits, were significantly preferred at physical stores rather than virtual stores at post-purchase stage. So we concluded that physical stores were a little more preferred to virtual stores at post-purchase stage. Through this study, it was identified that most consumers might switch channels according to purchase process stages. So, first of all, sales representatives should decide that what benefits should be given them through virtual stores at the pre-purchase stage and through physical stores at the purchase and post-purchase stages, and then devise collaborative channel strategies.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.161-171
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• 2009

In this paper, we propose an automatic method for setting data constraints of a data cleansing service, which is for managing the quality of data exchanged between composite services based on SOA(Service-Oriented Architecture) and enables to minimize human intervention during the process. Because it is impossible to deal with all kinds of real-world data, we focus on business data (i.e. costumer order, order processing) which are frequently used in services such as CRM(Customer Relationship Management) and ERP(Enterprise Resource Planning). We first generate an extended-element vector by extending semantics of data exchanged between composite services and then build a rule-based system for setting data constraints automatically using the decision tree learning algorithm. We applied this rule-based system into the data cleansing service and showed the automation rate over 41% by learning data from multiple registered services in the field of business.

• Korean Management Science Review
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• v.21 no.3
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• pp.101-113
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• 2004

In the rapidly propagating Internet based electronic transaction environment. the importance of real time order confirmation has been more emphasized, In this paper, using data mining techniques, we develop intelligent operations decision model to allow real time order confirmation at the time the customer places an order with required delivery terms. Among various operation plannings used for order fulfillment. mill routing is the first interface decision point to link the order receiving at the marketing with the production planning for order fulfillment. Though linear programming based mathematical optimization techniques are mostly used for mill routing problems, some early orders should wait until sufficient orders are gathered for optimization. And that could effect longer order fulfillment lead-time, and prevent instant order confirmation of delivery terms. To cope with this problem, we provide the intelligent decision model to allow instant order based mill routing decisions. Data mining techniques of decision trees and neural networks. which are more popular in marketing and financial applications, are used to develop the model. Through diverse computational trials with the industrial data from the steel company. we have reported that the performance of the proposed approach is effective compared to the present heuristic only mill routing results. Various issues of data mining techniques application to the mill routing problems having linear programming characteristics are also discussed.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.231-240
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• 2020

E-commerce development led to the explosion of online payment. Consumers have many choices when deciding on the online payment method for each transaction. Using a combination of both qualitative and quantitative methods with the help of SPSS AMOS version 22.0, the article explores the factors that influence consumers' online payment method decision behavior in Vietnam. Research results show that awareness of usefulness, awareness of risk, awareness of trust, awareness ease of use, product uncertainly perception and perceived behavioral control have effects on the behavior of deciding on online payment methods. Awareness of risk has the strongest negative impact on online payment method decision behavior and awareness of usefulness has the strongest positive impact on online payment method decision behavior. Based on these important results, the article proposes a number of implications: (i) continuing to invest and upgrade modern technology to ensure customer information absolutely confidential; (ii) converting all ATM cards on the market to EMV chip standard card technology; (iii) improving service activities, quickly handle things to create confidence for customers; (iv) credit institutions operating in the field of online payment linked to e-commerce sites, supermarkets, convenience stores, restaurants must ask partners to increase transparency for the products.

• CRM연구
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• v.4 no.2
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• pp.19-29
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• 2011

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.