• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.3
• /
• pp.518-523
• /
• 2006

Due to keen competition among companies, they have segmented customers and they are trying to offer specially targeted customer by means of the distinguished method. In accordance, data mining techniques are noted as the effective method that extracts useful information. This paper explores customer segmentation of the home study company using a hybrid decision tree and artificial neural network model. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship. The case study shows that the predicted accuracy of the proposed model is higher than those of regression, decision tree (CART), artificial neural networks.

• Korean Business Review
• /
• v.15
• /
• pp.79-97
• /
• 2002

In this study, an analytical CRM for customer segmentation is exercised by integrating and analyzing the customer profile data and the access data to a particular web site. We believe that effective customer segmentation will be possible with a basis of the understanding of customer characteristics as well as behavior on the web. One of the critical tasks in the web data-mining is concerned with both 'how to collect the data from the web in an efficient manner?' and 'how to integrate the data(mostly in a variety of types) effectively for the analysis?' This study proposes a panel approach as an efficient data collection method in the web. For the customer data analysis, OLAF and a tree-structured algorithm are applied in this study. The results of the analysis with both techniques are compared, confirming the previous work which the two techniques are inter-complementary.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.11a
• /
• pp.33-36
• /
• 2020

Virtual try on is getting interested from researchers these days because its application in online shopping. But single pose virtual try on is not enough, customer may want to see themselves in different pose. Multiple pose virtual try on is getting input as customer image, an in-shop cloth and a target pose, it will try to generate realistic customer wearing the in-shop cloth with the target pose. We first generate the target segmentation layout using conditional generative network (cGAN), and then the in-shop cloth are warped to fit the customer body in target pose. Finally, all the result will be combine using a Resnet-like network. We experiment and show that our method outperforms stage of the art.

• Journal of Information Technology Applications and Management
• /
• v.21 no.4_spc
• /
• pp.415-429
• /
• 2014

With the advent of smart devices, users are able to connect to each other through the Internet without the constraints of time and space. Because the Internet has become increasingly important to users in their everyday lives, reliance on it has grown. As a result, the number of web sites constantly increases and the competition between these sites becomes more intense. Even those sites that operate successfully struggle to establish new strategies for customer retention and customer development in order to survive. Many companies use various customer information in order to establish marketing strategies based on customer group segmentation A method commonly used to determine the customer groups of individual sites is to infer customer characteristics based on the customers' demographic information. However, such information cannot sufficiently represent the real characteristics of customers. For example, users who have similar demographic characteristics could nonetheless have different interests and, therefore, different buying needs. Hence, in this study, customers' interests are first identified through an analysis of their Internet news inquiry records. This information is then integrated in order to identify each web category. The study then analyzes the possibilities for the practical use of the proposed methodology through its application to actual Internet news inquiry records and web site browsing histories.

• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2004.06a
• /
• pp.65-67
• /
• 2004

• Korean Journal of Human Ecology
• /
• v.13 no.3
• /
• pp.453-466
• /
• 2004

This study reviewed the marketing strategies of domestic banks and introduced the theoretical framework of CRM model. The market segmentation for consumers in several domestic banks was compared and whether those informations were useful for consumers to evaluate the banks fit to their needs and for bank managers to promote their marketing strategies were also analyzed. The results of study showed that the domestic banks seemed to be apparently different in consumer services. This study showed that their private strategies must be somewhat different and it was important to search and keep those VIP's who contributed to their business. It was recommended to build the PB(private banking) center to counsel those VIP's and to analyze customers' characteristics.

• Korea Journal of Hospital Management
• /
• v.6 no.3
• /
• pp.90-108
• /
• 2001

This research examines the compensation package maximizing the utilities of internal customers by applying the market segmentation theory. Data were collected from four Korean hospitals in Seoul, Pusan and Kyunggi-do. The research is designed to seek the compensation package maximizing the utility of doctors and nurses by applying the market segmentation theory. The compensation package for doctors and nurses was classified into 5 attributes which are level of salary, payment method, education, promotion, reward method. The test results were as follows. First, the relative importance of each attribute in the compensation package is different. The level of salary is the most important, reward method is the next. Second, the utility of doctors increases by 8.7%, when they are segmented on the basis. of their preference for compensation attributes while that of nurses increases by 39.8%. The results of this study imply that the utility of doctors and nurses increases with differentiated compensation package for internal customer segmented by their preference.

• Proceedings of the KAIS Fall Conference
• /
• 2004.11a
• /
• pp.316-319
• /
• 2004

Due to keen competition among companies, companies have segmented customers and they are trying to offer specially targeted customer by means of the distinguished method. In accordance, data mining techniques are noted as the effective method that extracts useful information. This paper explores customer segmentation of the home study company using data mining. The purposes of this paper are especially competitor chum in the recent home study market, to understand the characteristics of the customer group who are expected chum in case competing companies do aggressive sales promotion. In addition, this paper aims to find the influential factors of their breakaway, and to prepare practical marketing strategy to keep the existing customers. The study of chum in the home study market is conducted and the model using decision tree to predict and select valuable customer. Finally, this paper presents how the results can be incorporated and measured as a part of an overall marketing campaign process.

• Information Systems Review
• /
• v.7 no.2
• /
• pp.23-40
• /
• 2005

Discovering customers' behavioral patterns from large data set and providing them with corresponding services or products are critical components in managing a current business. However, the diversity of customer needs coupled with the limited resources suggests that companies should make more efforts on understanding and managing specific groups of customers, not the whole customers. The key issue of this paper is based on the fact that the behavioral patterns extracted from the specific groups of customers shall be different from those from the whole customers. This paper proposes the idea of pre-segmentation before developing customer classification models. We collected three customers' demographic and transactional data sets from a credit card, a tele-communication, and an insurance company in Korea, and then segmented customers by major variables. Different churn prediction models were developed from each segments and the whole data set, respectively, using the decision tree induction approach, and compared in terms of the hit ratio and the simplicity of generated rules.

• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.21-36
• /
• 2022

Recently, as COVID-19 has made the e-commerce market expand widely, customers who have different consumption patterns appear in the market. Because companies can obtain opinions and information of customers from reviews, they increasingly face the requirements of managing customer reviews on online platform. In this study, we analyze customers and carry out market segmentation for classifying and defining type of customers in e-commerce. Specifically, K-means clustering was conducted on customer review data collected from Wemakeprice online shopping platform, which leads to the result that six clusters were derived. Finally, we define the characteristics of each cluster and propose a customer management plan. This paper is possible to be used as materials which identify types of customers and it can reduce the cost of customer management and make a profit for online platforms.