• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.272-284
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• 2009

In this paper, we proposed the methodology for analyzing online customer reviews by using text mining technologies. We introduced marketing segmentation into the methodology because it would be efficient and effective to analyze the online customers by grouping them into similar online customers that might include similar opinions and experiences of the customers. That is, the methodology uses categorization and information extraction functions among text mining technologies, matched up with the concept of market segmentation. In particular, the methodology also uses cross-tabulations analysis function which is a kind of traditional statistics analysis functions to derive rigorous results of the analysis. In order to confirm the validity of the methodology, we actually analyzed online customer reviews related with tourism by using the methodology.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.515-523
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• 2012

Most companies make efforts to maximize their profitability by improving loyalty to existing customers through customer relationship management (CRM). According to the Wikipedia, CRM is a widely implemented strategy for managing a company's interactions with customers, clients and sales prospects. And RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail. In general, one considers recency, frequency, and monetary for customer segmentation in RFM method. In this paper, we apply RFMP method added to the purchase period of advertising items in the traditional RFM model for real estate customer segmentation. We will be able to establish the differentiated marketing strategy by RFMP method.

• Korean Management Science Review
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• v.21 no.3
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• pp.55-69
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• 2004

This study analyzes customer buying-behavior patterns in a department store as time goes on, and predicts moving patterns of its customers. Through them, it suggests in this paper short-term and long-term marketing promotion strategies. RFM techniques are utilized for customer segmentation. Customers are clustered by using the Kohonen's Self Organizing Map as a method of data mining techniques. Then C5.0, a decision tree analysis technique, is used to predict moving patterns of customers. Using real world data, this study evaluates the prediction accuracy of predictive models.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.121-127
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• 2002

The volume of electronic commerce based on Internet and network traffic is increasing rapidly. The objective of this study is to examine the current status of the exponentially multiplying cyber-shopping mall phenomenon. To this end, data obtained from a single cyber-shopping mall exemplified customer purchasing behavior and provided decision tree and correspondence analysis derived customer segmentation and merchandise.

• Korean Management Science Review
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• v.19 no.1
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• pp.189-196
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• 2002

Data clustering is often one of the first steps in data mining analysis. It Identifies groups of related objects that can be used as a starling point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. This paper Purpose to present the development of two phase hierarchical clustering algorithm for group formation. Applications of the algorithm for product-customer group formation in customer relationahip management are also discussed. As a result of computer simulations, suggested algorithm outperforms single link method and k-means clustering.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.17-34
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• 2007

Organizations have realized the importance of CRM. To obtain the maximum possible lifetime value from a customer base, it is critical that customer data is analyzed to understand patterns of customer response. As customer databases assume gigantic proportions due to Internet and e-commerce activity, data-mining-based market segmentation becomes crucial for understanding customers. Here we raise a question and some issues of using single SOM approach for clustering while proposing multiple self-organizing maps approach. This methodology exploits additional themes on the attributes that characterize customers in a typical CRM system. Since this additional theme is usually ignored by traditional market segmentation techniques we here suggest careful application of SOM for market segmentation.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.386-394
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• 2011

Recently it is difficult to draw an improvement for customer satisfaction because the ratio of satisfied customers increases in customer satisfaction survey. In addition, the effectiveness of practical application of customer satisfaction survey decreases due to its constitution limitation on its data analysis. In order to solve these problems, it is necessary to develop a novel research to identify the strategy meanings and find dissatisfied factors of satisfied customers using the satisfied customers' reclassification. This study focuses on the satisfied customer segmentation based on Latent Class Analysis (LCA). The case study with high-speed internet service customers show that the satisfied customers are divided into three subgroups using LCA and we draw meaning results such as satisfaction and dissatisfaction factors through analyzing each group. This study is expected to play the role as the groundwork for the revitalization of customer satisfaction survey as well as improving customer satisfaction management.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.1 s.149
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• pp.1-11
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• 2006

The purpose of this study was to analyze the current CRM situation in Korean apparel industry. Specifically, research purposes were 1) to examine the concepts and benefits of CRM, 2) to examine CRM strategies, 3) to analyze CRM system(i.e., customer relationship management service, customer segmentation criteria, DB management system), and 4) to analyze the potential problems and CRM adoption plan. The subjects for this research were thirty CRM managers in Korean apparel firms classified by the company type(woman's wear, man's wear, casual wear, children's wear, retailer) interviewed from December 2003 to March 1004. The results of this study were as follows: First, the concept of CRM represented the prime customer relationship, continuous consideration, and customer management system. The benefits of CRM reflected re-sales, improvement of profit share, and acquisition of customer's data base. Second, concerning the CRM strategies, most companies focused on persistent customer management through mileage program, membership cards and also implemented product strategies such as demand forecasting, customization based on customer data analysis. We also found that industry preferred to use pricing strategies, for example, segmentation of customer through discrepancies of price in which customers are provided by discount and gift voucher services. Regarding distribution strategy, channel diversification, localized service, and convenient delivery system were used. As promotion strategies, they chose celebrating customers' personal events and promoting cultural events and issuing coupons. Third, regarding CRM system, information service was the most frequently adopted, important and highly beneficial category. Also POS/web-POS, homepage were main sources of information. RFM is the mostly commonly used customer segmentation criteria. Fourth, potential problems in CRM adoption were lack of CRM knowledge and performance measurement of CRM. Future CRM adoption plan included CRM education and development of CRM performance measures.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.567-581
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• 2005

Geographical Customer Relationship Management (gCRM) is an integrated solution of Geographic Information System (GIS) and Customer Relationship Management (CRM). In gCRM, GIS is used to show multi-dimensional analytical results of customer information geographically. When customer information is geographically presented, more valuable information appears. In this research we briefly introduce gCRM and show real examples of customer segmentation applied to company.