• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.6
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• pp.847-858
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• 2005

The purpose of this study was to examine consumer purchasing behavior of the online shoppers particularly using online clothing shopping mall and to analyze the key factors of both satisfaction and dissatisfaction of their purchase and to compare the both group by lifestyle segmentation in order to provide the e-CRM strategies. Focus group interviews and survey were conducted in December, 2003 with 30 online shoppers who have an experience of online clothing purchasing. The data analysis included the content analysis, descriptive statistics, K-means and factor analysis. Key findings of the study were as follows: First, online shoppers spent average 3.5 hours on internet and usually purchased clothing while surfing the web. Second, consumers were satisfied with reasonable price and customized service but dissatisfied with delayed delivery, limited product availability in both size and color and return policy. Third, according to the lifestyle segmentation, online shoppers could be characterized as 'Luxurious', 'Trendy' and 'Prudent' 'Luxury-oriented consumers', who value fashion, diet and social activity, tended to purchase basic yet high quality products. However, 'Trend-oriented consumers', to whom fashion trend was most important, purchased various latest fashion products with reasonable price and showed generally positive response to emails sent by e-retailers. And lastly 'Prudence-oriented consumers', whose buying decision was based solely on practicality, appeared to be reluctant to purchase clothing online while seeking more credible information and competitive price. In conclusion, this study has its significance in that it helps promote relationships between customers and e-retailers by providing differentiated e-CRM strategies through each customer groups 'lifestyle segmentation and consumer purchasing behavior analysis.

• The Journal of Economics, Marketing and Management
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• v.10 no.6
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• pp.27-37
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• 2022

Purpose: The purpose of this study is to derive various ways to realize customer satisfaction for the development of the service industry by exploring research trends related to customer satisfaction, which is presented as an important goal in the service industry. Research design, data and methodology: To this end, 1,456 papers with English abstracts using scienceON were used for analysis. Using Python 3.7, word frequency and co-occurrence analysis were confirmed, and topics related to research trends were classified through BERTopic and LDA. Results: As a result of word frequency and co-occurrence frequency analysis, words such as quality, intention, and loyalty appeared frequently. As a result of BERTopic and LDA, 11 topics such as 'catering service' and 'brand justice' were derived. As a result of trend analysis, it was confirmed that 'brand justice' and 'internet shopping' are emerging as relatively important research topics, but CRM is less interested. Conclusions: The results of this study showed that the 7P marketing strategy is working to some extent. Therefore, it is proposed to conduct research related to acquisition of good customers through service price, customer lifetime value application, and customer segmentation that are expected to be needed for the development of the service industry.

• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• Journal of Global Scholars of Marketing Science
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• v.14
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• pp.125-144
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• 2004

The purpose of this study is to classify customer bye-mailing responsiveness on time-series analysis and RFM module and testify the effectiveness of grouping by ROI analysis. RFM (Recency, Frequency, Monetary Value) analysis are used for customer classification that is fundamental process of e-CRM application. ROI analysis were consisted of open, click-through, duration time, conversion rate, personalization and e-mail loyalty index. Major findings are as follows; Customer segmentation were loyal customer, odds customer, dormant customer, secession customer and observation customer by Activity email module. And Loyal, dormant and secession customer are segregated by RFM module. Loyal customer group have higher point of all ROI index than other groups. These results indicated that customer responsiveness of e-mailing and RFM analysis were appropriate methods to grouping the customer. Mid-small Internet Biz adapted marketing strategy by optimization of consumer information.

• Journal of Korean Ophthalmic Optics Society
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• v.20 no.4
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• pp.405-414
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• 2015

Purpose: We analyse characteristics of optical shop customer's segmented market by using clustering analysis, and we expect it would be a useful indicator of marketing strategy for optical shops. Methods: Survey was conducted from March 10 to March 31, 2015. The survey asked customers who have visited optical shops in Seoul and Northern Gyeonggi-do regions, and analyzed by utilizing SPSS v.10.0 statistical package program. The analysing methods are frequency analysis, factor analysis about variable of values, clustering analysis for market segmentation, and crosstabs. Results: The market is segmented based on values. In the process of establishing marketing strategy, it is useful to establish strategy by classifying customers into 3 types of cluster; "middle level value oriented cluster", "high level value oriented cluster", "high level value oriented and non-religious cluster". In marketing strategy of progressive lenses, it turned out that the most important strategy is to target self-employed person in "middle level value oriented cluster". Conclusions: As a result of market segmentation by using clustering analysis, it was classified into 3 types of cluster, and we found that most important customer for progressive lenses is self-employed person in "middle level value oriented cluster" who is more than 41 years old.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

• Management & Information Systems Review
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• v.23
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• pp.25-40
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• 2007

In the highly competitive and divers world of financial market, customer is the single most important factor to company's survival. Especially, creating a relationship with valued customers is a key to success. CRM provides the mean to retain high value customers. It takes a prospect of what customers expect. Utilizing those knowledge can help the products and service meet the customers' needs, thereby maximizing customer satisfaction and company's profit. In this report, I am going to suggest a few ways to develop successful CRM in the life insurance industry. First, CRM should innovate the way of communication to keep pace with Web 2.0 era. In other words, the customer's needs should be caught by real-time communication than traditional off-line market research. Thus, the functionality and specification of products can be decided by customer's direct choice so that the customers are able to purchase the understanding and experience of the products. Second, CRM project should consider whether the initial strategy plan can promise the stable growth of customer at the first step. When planning strategy, the project needs to identify what customer wants and how to fulfill the needs with stable growth of the customer. In addition, the CRM should be developed by realizing that customer centric benefits ultimately guarantee the growth of the organization. Third, CRM systems should enhance the organization's ability to take the customer's insight in a 360 degree view and to capture the voice of the customer directly. In order to develop the best matched product package, more precise customer segmentation should be ahead of market segmentation strategy. Forth, the biggest reward from CRM will be a customer royalty program. Many successful banks are already planning and practicing customer royalty strategy. A comprehensive analysis of customers and their behavior allow organization to identify high value potential customers' needs and determine a strategy required to meet those needs. Even life insurance companies such as Prudential Korea are developing products designed for royal customers. Fifth, understanding and managing the experience of customer called Customer Experience Management also can increase customer satisfaction. Measuring only customers' experience and adapting it to marketing strategy make products position in the gap between the customers' expectation and experience not required by market. A key component of CEM is its application across all organizational functions. At last, the direction of change and development of CRM can be defined from the conceptualization of information technology represented by Ubiquitous and Web 2.0. Instead of just managing customer information, companies should take the initiative in personalized system with customer oriented strategy. Furthermore, with the regular communication between CRM stakeholders (Sales-Marketing-IT), customer's demand should be directly reflected to enterprise strategy in real time.

• Korean journal of food and cookery science
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• v.25 no.5
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• pp.531-544
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• 2009

Strategic alliance is increasingly becoming a popular strategy in the family restaurant industry. In general, strategic alliance can be defined as several brands collaborating in technology development, marketing, or production while keeping their independence as separate business entities. This study identified segments on the basis of sales promotion resulting from strategic alliances between family restaurants and card companies. This study further investigated how brand image, brand value, price fairness, customer loyalty and demographics are different among the segments. From the statistical analysis, three segments were found; 'short-period benefits oriented' segment, 'intangible and discount benefits oriented' segment and 'free benefits oriented' segment. Among the three segments, the 'free benefits oriented' and 'intangible and discount benefits oriented' segments had greater perceived brand image, brand value and customer loyalty than the 'short-period benefits oriented' segment.

• ETRI Journal
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• v.37 no.2
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• pp.317-327
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• 2015

A reliable analysis of consumer preference from a large amount of purchase data acquired in real time and an accurate customer characterization technique are essential for successful direct marketing campaigns. In this study, an optimal segmentation of post office customers in Korea is performed using a subspace projection-based clustering method to generate an accurate customer characterization from a high-dimensional census dataset. Moreover, a traditional temporal mining method is extended to an algorithm using the MapReduce framework for a consumer preference analysis. The experimental results show that it is possible to use parallel mining through a MapReduce-based algorithm and that the execution time of the algorithm is faster than that of a traditional method.

• Journal of the Korea Safety Management & Science
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• v.5 no.3
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• pp.133-143
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• 2003

Even in the present situation where any general criterion on CRM dose not exist, utilization of CRM is expected to be actively continued, which will cause many problems. In this regard, evaluating CRM counts. As the result, projects are being suspended and budgets cut, plans for introducing CRM suspended or cancelled and many CRM software vendors and technical consulting firms are facing serious management crisis. Yet, this phenomenon can be regarded as an interim one. In fact, some cases that successfully introduced CRM show that CRM is migrating from small scale which is typical when introduced to larger scale through various tests. Therefore, this study tries to segment customer for the sloving the problem. And it make efficient customer management. Using this model, SN ratio of taguchi method for each of subjective factors as well as values of weights are used in this comprehensive method for customer. A example is presented to illustrate the model and to show a rank reversal when compared to a model that does not eliminate extreme values and eliminates the highest and lowest experts' values allocating the weights and the subjective factors.