• Journal of Distribution Research
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• v.9 no.2
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• pp.29-43
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• 2004

This research analyzed multi-channel strategy from the viewpoint of the customer relationship management. We hypothesized that the purchase frequency, monetary, purchase quantity of the existing customers should have increased after they used multiple channels of a company for shopping, All the hypothesis were supported in an empirical test using the customer database of a Korean TV home-shopping company, The result showed that the multi-channel strategy can be used to increase the life-time value of existing customers. Still there were a lot of customers who insists using traditional channel, which calls for a new strategy inducing them to use multi- channel for shopping.

• Journal of the Korean Home Economics Association
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• v.41 no.5
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• pp.59-70
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• 2003

이 연구는 인터넷 소비자들의 행동에 관한 연구의 일부로, 인터넷 구매자들의 구매 빈도와 그들의 인터넷에 대한 태도, 구매 동기, 인터넷 사용, 인구통계적 특성과의 관계를 살펴보았다. Georgia Institute of Technology의 Graphic Visualization and Usability Center에서 실시된 설문조사를 통해 수집된 자료를 요인분석과 회귀분석을 이용하여 분석하였다. 분석 결과. 인터넷 쇼핑에 대한 소비자들의 태도(상대적 잇점. 안전성), 인터넷 판매자에 대한 소비자들의 태도(고객 서비스). 인터넷 브라우징 빈도, 소득, 교육이 인터넷 구매자들의 구매 빈도에 유의한 영향을 미치는 것으로 나타났다.

• Journal of Global Scholars of Marketing Science
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• v.8
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• pp.475-490
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• 2001

The purpose of this study were to identify the difference of demographic variables, motivation and dissatisfaction to purchase of groups who classified by frequency of purchase. Data were collected via intercept surveys conducted at nine regional branches of two major department stores situated in Seoul. Participants(n=1,120), who had previously purchased women's private brand apparel, were asked to complete a questionnaire. The results of this study were as follows; The subjects were classified into 3 groups by frequency of purchase and their demographic variables were analyzed. The customer groups of high frequency who were 18 to 39 years old had some college education, housewives and white collar workers. Their monthly household income is one to three million won and their monthly expenditure is 100,000 to 300,000 won on apparel shopping. The most important purchase motivation of lower frequency groups was design, whereas that of middle and high frequency groups was good quality over price.

• Asia Marketing Journal
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• v.10 no.2
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• pp.99-123
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• 2008

Consumer satisfaction/dissatisfaction is key determinant of brand loyalty and store patronage behavior. But the results of many customer satisfaction surveys implemented by department stores show that consumer satisfactions do not predict the actual patronage behaviors well. The main reason of these surprising results would be that the consumer satisfaction indexes do not include some important determinants of consumer satisfaction. Many customer satisfaction surveys mainly focus on the evaluation of functional benefits including product assortments, merchandise prices and locational convenience. Recent studies indicate that emotional/hedonic benefits strongly influence the consumer satisfaction, intention to repurchase and intention to revisit. Our study suggests that both functional values and hedonic values should be included in developing the index of consumer satisfactions. The purpose of our study is to investigate the relationship between shopping value and consumer satisfaction, and actual patronage behavior. Shopping values is defined as the difference between total benefits and total shopping costs. Total benefits include the dimensions of product quality, service quality, and hedonic benefits. Total costs are classified as the monetary costs and non-monetary cost. The conceptual framework developed for this empirical study is as follows.

• Journal of Distribution Research
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• v.15 no.4
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• pp.21-60
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• 2010

In recent retail environments, multichannel customer management increasingly has been considered a key element of successful CRM. Although customer's multichannel usage is believed to be potential cause of customer loyalty, the theoretical explanation about this causal relationship still remains unexamined and unanswered. In this paper, the authors present a systematic framework to test the postulated "multichannel usage-shopping experience-customer satisfaction" chain. To this end, we examine that the two core components of shopping experience(convenience and enjoyment) is a mediator of the direct causality of multichannel usage(based on both information search and product purchase stage) on customer satisfaction. Moreover, the authors examine that two types of customer-firm relationship characteristics(relationship age and purchase frequency) is a moderator of the multichannel usage-shopping experience relationship. Using integrating data with survey and customer database of multichannel retail company, the authors empirically test and substantiate shopping experience's mediating role in the multichannel usage-customer satisfaction relationship and customer-firm relationship characteristics' moderating role in the multichannel usage-customer experience relationship. These results suggest that multichannel retailers should deliver favorable shopping experience for building customer satisfaction and differentiate shopping experience according to customer-firm relationship characteristics.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2001.11a
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• pp.21-21
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• 2001

• 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.

• Annual Conference of KIPS
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• 2002.04a
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• pp.561-564
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• 2002

최근 전자상거래 시스템에서 구매 촉진을 위해 사용하고 있는 핵심기술은 고객들로부터 얻어진 구매정보를 기초로 고객이 좋아할 만한 제품을 예측하여 고객에게 정보를 제공하는 추천시스템이다. 이러한 추천시스템을 위한 추천알고리즘으로서 협동적 필터링(collaborative filtering) 알고리즘이 많이 사용되고 있다. 이 논문에서는 기존의 협동적 필터링 알고리즘의 성능을 향상시킨 동시출현 빈도 개념 알고리즘과 제한 피어슨 알고리즘을 접목시켜서, 사용자 선호도의 예측 정확도를 좀 더 향상시킬 수 있는 새로운 방법을 제안하고, 실험을 통해서 제안한 방법의 예측 정확도의 우수성을 증명하였다.

• Journal of Korea Technology Innovation Society
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• v.9 no.2
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• pp.325-349
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• 2006

This paper focuses to identify and analyze the influence of product/market characteristics on the performance of new ventures. An empirical result points that some factors playa role. High channel dependence and made-ta-order supply have negative impact on venture performance, while high service requirements have a positive influence. Summing up the findings of this study, we suggested the implications for defining the battle-grounds where new ventures have a better chance.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.23-39
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• 2020

The 4th Industrial Revolution and Covid 19 are moving the fashion industry from offline to online. Fashion shows that took place offline are being replaced by online. Online is greatly increasing consumers' digital customer experience based on digital technologies. In this study, we studied the effect of digital experience factors on digital customer satisfaction based on the Schmitt(1999)'s experience marketing. The effect of digital customer satisfaction on purchase, continuous use intention, and recommendation intention were also studied. In addition, the moderating effect of experience frequency was studied. We randomly sampled 180 individuals among fashion mall users.. SPSS 24, AMOS 23 and Process Macro 3.5 were used for statistical analysis. In the study in which digital experience factors influence digital customer satisfaction, all except the digital act showed positive influence. The impact of influence was digital sense (β = .366) > digital think (β = .225)> digital feel (β = .191) > digital relate(β = .163). Digital customer satisfaction have been positive impact on purchasing, continuance use and recommendation intention. In the moderating effect of digital experience frequency, between digital feel, digital act and digital customer experience showed a statistically effective relationship. Based on the this study, We suggested theoretical and practical implications.