• Analytical Science and Technology
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• v.28 no.6
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• pp.417-424
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• 2015

In this study, the relationship between selenoprotein concentrations in blood and stomach cancer have been searched for Korean. The concentration of each selenoprotein in blood serum was analyzed and the correlation between the concentration and stomach cancer was studied to find a potential for using Selenium as a biomarker. In concentration determination, a simple calibration curve method was used with the monitoring of m/z 78 without the use of solid phase extraction. This is a lot more simple than the method using SPE with post column isotope dilution. The result obtained from the analysis of CRM BCR-637, 72.20±3.35 ng·g⁻¹, showed similar value of reference value (81±7 ng·g⁻¹). The total concentration of Se for the controlled group, cardiovascular patients group, was 105.70±21.20 ng·g⁻¹. This value was the same as normal healthy person reported earlier. Each selenoprotein concentration of GPx, SelP and SeAlb was 26.12±7.84, 65.15±14.50, 14.43±6.99 ng·g⁻¹, respectively. The distribution of each selenoprotein was 24.7%, 61.6%, and 13.7%, which was similar to the normal person. The result of stomach cancer patients, the total concentration of Se was 76.11±28.12 ng·g⁻¹ and each concentration of GPx, SelP and SeAlb was 15.41±9.01, 50.83±17.91, and 9.87±5.21 ng·g⁻¹, respectively. The total and each selenoprotein concentration level showed significant decrease for the stomach cancer patients. The level of decrease was 41.0% for GPx, 22.0% for SelP, and 31.6% for SeAlb. However, the distribution of each selenoprotein was not much different. Either total Selenium or each selenoprotein could be used as a possible index for the diagnosis of cancer. However, in age group study, it is shown that young age group (30's-40's) did not show much difference.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Journal of Distribution Science
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• v.9 no.4
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• pp.5-13
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• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

• Fashion & Textile Research Journal
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• v.14 no.4
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• pp.614-628
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• 2012

The purpose of this study was to analyze research trends and make suggestions regarding the future of information technology (IT) in the fashion industry. In this study, 437 papers written regarding IT fashion from five major journals published between 2000 and 2011 were examined. The research areas were then organized by subject and keyword, and divided into 16 high-context categories. Two IT fashion maps were constructed, one from a fashion consumer's perspective, and the other based on the fashion industry's supply chain. This study identified important trends in IT fashion such as: 3D scanners, 3D digital renderings of the human form, 3D digital garments, smart garments, mass customization, production automation, online shopping, home shopping, online communities, e-commerce, digital media, virtual reality, e-tail, the digital generation, E-CRM, and education. Data from body scans was collected and applied to production, and research on smart textiles was also carried out. As for IT fashion's service areas, the majority of the research focused on online shopping or online communication. Additionally, research done on avatars and cyber space, and studies on social networking services are shown. The results of this study indicated that a new field of research has opened and that current research has been developing. Also, this study showed what is needed to expand and strengthen IT fashion.

• Culinary science and hospitality research
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• v.22 no.7
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• pp.267-280
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• 2016

Customer Experience Management (CEM) appeared as a complementary solution to overcome CRM limitations. CEM enhances profitability through building long-term relations with customers by understanding their experiences. This study aims at investigating the impact of customer experience quality on the willingness to pay more through customer satisfaction in the hotel businesses. The survey for this study was carried out on customers who had domestic hotel experience s within the last 6 months. Out of the 306 questionnaires retrieved, 225 valid responses were used for the empirical analysis that utilizied the statistical package programs SPSS 18.0 and AMOS 18.0. The research findings may be summarized as follows. First, as an outcome of the research hypothesis that each component of customer experience management would influence satisfaction, 'the peace of mind' & 'the moment of truth' were shown to have a significantly positive (+) impact on it. On the other hand, 'the product experience' was shown not to significantly influence it in a positive (+) way. Second, as an outcome of the research hypothesis that satisfaction would influence willingness to pay more. From the findings of the study, theoretical implications are as follows. It can be predicted that customer experience management will likely make customers more profitable because customers are willing to pay more with a sense of loyalty built through satisfaction of the hotel industry. In the practical implications, the dimension of experience quality examined by the study can be used as an index to measure and manage customer experience in the hotel industry.

• Information Systems Review
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• v.14 no.1
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• pp.37-65
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• 2012

This research aims to design and analyze a ubiquitous customer relationship management system based on near field communication which can be applied to stores in off-line environment. The existing customer relationship management system has been used mainly for stores in off-line environment to issue a royalty card, to stamp a seal on the purchase goods, and to manage the history of customers' visits and purchases. But, the existing system has two weaknesses; it makes difficult for a store manger not only to acquire a wealth of customer data but also to systematically manage the acquired data. In particular, the effectiveness and efficiency of the royalty card are questioned when a customer makes purchases in the store in that the customer frequently does not carry it or loses it. So, this research suggests a ubiquitous customer relationship management system where a tag for near field communications is attached to a store in off-line environment; a store manager can collect and manage easily customer's dada and customers can seamlessly acquire store's information. To do this, this research conducts the followings. First, we review the previous researches of customer relationship management to examine the concept of ubiquitous customer relationship management. Second, from the examination, we draw the factors to be considered in ubiquitous customer relationship management system based on near field communication. Third, we propose a scenario where the suggested system is used. Fourth, we analyze the participants' values and the process that will be used for the suggested system. Finally, we discuss the possibility of the application of this system to real business environment from various viewpoints.

• Analytical Science and Technology
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• v.5 no.4
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• pp.389-399
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• 1992

Liquid Chromatographic behavior of Pd(II) in Isonitrosoethylacetoacetate lmine, $Pd(IEAA-NR)_2$ (R=H, $CH_3$, $C_2H_5$, $n-C_3H_7$, $C_6H_5-CH_2$, $n-C_4H_9$) chelates were investigated by reversed-phase HPLC on Micropak MCH-5 column using methanol/water as mobile phase. The optimum conditions for the separation of $Pd(IEAA-NR)_2$ chelates were examined with respect to the effect of the flow rate, sample solvent, mobile phase strength and column temperature. It wass found that metal chelates were properly eluted in an acceptable range of capacity factor value($0{\leq}log\;k^{\prime}{\leq}1$). The dependence of the logarithm of capacity factor(k') on the volume fraction of water in the binary mobile phase was examined. Also, the dependence of k' on the liquid-liquid extration distribution ratio($D_c$) in methanol-water/n-alkane extration system was investigated. Both kinds of dependence are linear, which susggests that the retention of the electroneutral metal chelate is largely due to the solvophobic effect. Standard adsorption enthalpy changes (${\Delta}H^{\circ}$) and standard adsorption entropy changes (${\Delta}S^{\circ}$) of Pd(II) Isonitrosoethylacetoacetate imine chelates on Micropak MCH-5 column were calculated by measuring capacity factor with changing temperature of the column.

• Journal of Animal Science and Technology
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• v.57 no.5
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• pp.17.1-17.6
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• 2015

The present study investigated the nutritional and economic suitability of cashew reject meal (full fat and defatted) as replacement for groundnut cake (GNC) in the diets of laying chickens. A total of eighty four brown shavers at 25 weeks of age were randomly allotted into seven dietary treatments each containing 6 replicates of 2 birds each. The seven diets prepared included diet 1, a control with GNC at $220gkg^{-1}$ as main protein source in the diet. Diets 2, 3 and 4 consist of gradual replacement of GNC with defatted cashew reject meal (DCRM) at 50%, 75% and 100% on weight for weight basis respectively while diets 5, 6 and 7 consist of gradual inclusion of full fat cashew reject meal (FCRM) to replace 25%, 35% and 50% of GNC protein respectively. Each group was allotted a diet in a completely randomized design in a study that lasted eight weeks during which records of the chemical constituent of the test ingredients, performance characteristics, egg quality traits and economic indicators were measured. Results showed that the crude protein were 22.10 and 35.4% for FCRM and DCRM respectively. Gross energy of DCRM was 5035 kcal/kg compared to GNC, 4752 kcal/kg. Result of aflatoxin $B_1$ revealed moderate level between 10 and $17{\mu}g/Kg$ in DCRM and GNC samples respectively. Birds on control gained 10 g, while those on DCRM and FCRM gained about 35 g and 120 g respectively. Feed intake declined (P < 0.05) with increased level of FCRM. Hen day production was highest in birds fed DCRM, followed by control and lowest value (P < 0.05) was recorded for FCRM. No significant change (P > 0.05) was observed for egg weight and shell thickness. Fat deposition and cholesterol content increased (P > 0.05) with increasing level of FCRM. The cost of feed per kilogram decreased gradually with increased inclusion level of CRM. The prediction equation showed the relative worth of DCRM compared to GNC was 92.3% whereas the actual market price of GNC triples that of DCRM. It was recommended that GNC could be completely replaced by DCRM in layer's diets in regions where this by product is abundant. However, FCRM should be cautiously used in diets of laying chickens.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.247-256
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• 2000

오늘날의 기업은 상품을 판매하는 것 뿐만 아니라 기업의 신용과 이미지를 위해 그 상품에 대한 사후처리(After Service) 업무에 많은 투자를 하고 있다. 이러한 양질의 사후서비스를 고객에게 공급하기 위해서는 많은 인력을 합리적으로 관리해야 하고 요청되는 고장수리 서비스 업무를 빠르게 해결하기 위해서는 업무를 인력들에게 합리적으로 배정을 하고 회사의 비용을 최소화하면서 정해진 시간에 요청된 작업을 처리하기 위해서는 인력들에게 작업을 배정하고 스케줄링하는 문제가 발생된다. 본 논문에서는 이러한 문제를 해결하기 위해 화학계기의 A/S 작업을 인력에게 합리적으로 배정하는 스케줄링 시스템에 관한 연구이다. 먼저 스케줄링 모델을 HP 사의 화학분석 및 시스템을 판매, 유지보수 해 주는 "영진과학(주)"회사의 작업 스케줄을 분석하여 필요한 도메인과 고객서비스전략과 인력관리전략에서 제약조건을 추출하였고 여기에 스케줄링 문제를 해결하기 위한 방법으로 제약만족문제(CSP) 해결기법인 도메인 여과기법을 적용하였다. 도메인 여과기법은 제약조건에 의해 변수가 갖는 도메인의 불필요한 부분을 여과하는 것으로 제약조건과 관련되어 있는 변수의 도메인이 축소되는 것이다. 또한, 스케줄링을 하는데에 있어서 비용적인 측면에서의 스케줄링방법과 고객 만족도에서의 스케줄링 방법을 비교하여 가장 이상적인 해를 찾는데 트래이드오프(Trade-off)를 이용하여 최적의 해를 구했으며 실험을 통해 인력에게 더욱 효율적으로 작업들을 배정 할 수 있었고 또한, 정해진 시간에 많은 작업을 처리 할 수 있었으며 작업을 처리하는데 있어 소요되는 비용을 감소하는 결과를 얻을 수 있었다. 검증하였다.를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서의 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용가능하리라 여겨진다.선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity

• 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.