• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.71-76
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• 2004

The recent ERP and CRM is mostly focused on the conventional function performances. However, the recent business environment has brought the change in market due to the rapid progress of internet and e-commerce. It is mostly becoming e-business and spreading out as development of the relationship with other cooperating companies, the rapid progress of the relationship with customers, and intensification competitive power through the development of business progress in the organization. CRM(custom relationship management) is a kind of the marketing progress which forms, manages, and intensifies the relationship between the customers and companies to manage the acquired customers and increase the worth of customers for the company. It needs the system base which analyzes the information of customers since it functions on the basis of various information about customers and is linked to the business category such as producing, marketing, and decision making. Since ERP is extending its function to SCM, CRM, and SEM(strategic Enterprise Management), the 21 century s ERP develop as the strategy tool of e-business and, as the mediation for this, will subdivide the functions of CRM effectively by the analogic study of data. Also, to accomplish classification work of the file which in existing becomes accomplished with possibility work with an automatic movement with the user will be able to accomplish a more efficiently work the agent which in order leads the machine studying law, it is one thing with system feature.

• The Journal of Information Systems
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• v.26 no.4
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• pp.17-37
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• 2017

Purpose Customer Loyalty is the most important factor of customer relationship management (CRM). Especially in retailing industry, where customers have many options of where to spend their money. Classifying loyal customers through customers' data can help retailing companies build more efficient marketing strategies and gain competitive advantages. This study aims to construct classification models of distinguishing the loyal customers within a Korean retailing company using data mining techniques with R language. Design/methodology/approach In order to classify retailing customers, we used combination of support vector machines (SVMs) and other classification algorithms of machine learning (ML) with the support of recursive feature elimination (RFE). In particular, we first clean the dataset to remove outlier and impute the missing value. Then we used a RFE framework for electing most significant predictors. Finally, we construct models with classification algorithms, tune the best parameters and compare the performances among them. Findings The results reveal that ML classification techniques can work well with CRM data in Korean retailing industry. Moreover, customer loyalty is impacted by not only unique factor such as net promoter score but also other purchase habits such as expensive goods preferring or multi-branch visiting and so on. We also prove that with retailing customer's dataset the model constructed by SVMs algorithm has given better performance than others. We expect that the models in this study can be used by other retailing companies to classify their customers, then they can focus on giving services to these potential vip group. We also hope that the results of this ML algorithm using R language could be useful to other researchers for selecting appropriate ML algorithms.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.111-128
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• 2017

Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.73-90
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• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• 한국IT서비스학회:학술대회논문집
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• 2006.05a
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• pp.489-496
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• 2006

e-비즈니스 시스템 도입시 성공요인에 대한 많은 연구가 진행되어 왔다. 기존의 많은 연구에서 시스템 도입이전, 개발단계 혹은 도입초기의 핵심성공요인에 대한 제안은 많으나 시스템의 도입이후 사용 연한 즉 시스템 연령(system age)이 증가하는 과정에서 단계별 성공요인에 대한 연구는 많지 않은 실정이다. 본 연구는 e-비즈니스 시스템의 핵심성공요인이 시스템 연령의 증가에 따라 달라질 수 있다는 가정 하에 시스템 연령별 성공요인의 변화를 탐색하고자 하였다. 연구를 위한 표현으로 48개 기업을 대상으로 315명의 설문응답 결과를 분석하였다. 대상기업은 e-비즈니스시스템(ERP, SCM, CRM)을 사용 중인 기업으로 하였으며, 시스템 사용자와 개발자를 중심으로 조사를 실시하였다. 시스템 연령 구분은 시스템 도입 후 사용기간이 1년 미만(1기), 1-2년 미만(2기), 2-4년 미만(3기), 4년 이상(4기)으로 구분하여 분석하였다. 분석결과 시스템 연령별 조직의 성과에 유의한 영향을 미치는 요인의 종류가 달라지는 것을 확인하였다. 본 연구의 결과는 기업들이 시스템 도입이후 운영과정에서 시스템 성과를 극대화 하기 위해 역량을 집중해야할 분야가 무엇인지에 대한 시사점을 줄 수 있을 것이다.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.69-81
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• 2005

As the competition in business becomes severe, companies are focusing their capacity on customer relationship management (CRM) for survival. One of the important issues in CRM is to build a purchase prediction model, which classifies customers into either purchasing or non-purchasing groups. Until now, various techniques for building purchase prediction models have been proposed. However, they have been criticized because their performances are generally low, or it requires much effort to build and maintain them. Thus, in this study, we propose the support vector machine (SVM) a tool for building a purchase prediction model. The SVM is known as the technique that not only produces accurate prediction results but also enables training with the small sample size. To validate the usefulness of SVM, we apply it and some of other comparative techniques to a real-world purchase prediction case. Experimental results show that SVM outperforms all the comparative models including logistic regression and artificial neural networks.

• Structural Engineering and Mechanics
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• v.84 no.1
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• pp.17-33
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• 2022

An experimental study is carried out to investigate the performance of the cutting tool regarding the insert wear, surface roughness, cutting forces, cutting power and material removal rate of three coated carbides GC2015 (TiCN-Al₂O₃-TiN), GC4215 (Al₂O₃-Ti(C,N)) and GC1015 (TiN) during the dry turning of AISI4140 steel. For this purpose, a Taguchi design (L₉) was adopted for the planning of the experiments, the effects of cutting parameters on the surface roughness (R_a), tangential cutting force (F_z), the cutting power (P_c) and the material removal rate (MRR) were studied using analysis of variance (ANOVA), the response surface methodology (RSM) was used for mathematical modeling, with which linear mathematical models were developed for forecasting of R_a, F_z, P_c and MRR as a function of cutting parameters (V_c, f, and a_p). Then, Multi-Objective Ant Lion Optimizer (MOALO) has been implemented for multi-objective optimization which allows manufacturers to enhance the production performances of the machined parts. Furthermore, in order to characterize and quantify the flank wear of the tested tools, some machining experiments were performed for 5 minutes of turning under a depth of 0.5 mm, a feed rate of 0.08 mm/rev, and a cutting speed of 350 m/min. The wear results led to a ratio (V_B-GC4215/V_B-GC2015) of 2.03 and (V_B-GC1015/V_B-GC2015) of 4.43, thus demonstrating the efficiency of the cutting insert GC2015. Moreover, SEM analysis shows the main wear mechanisms represented by abrasion, adhesion and chipping.

• Agribusiness and Information Management
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• v.9 no.2
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• pp.22-26
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• 2017

This study aims to understand why public information systems in agricultural fields have shown lower performances than other industrialized fields and industries and how these problems would be fixed and overcame. To accomplish this research purpose, this study would overview the previous studies on developing agricultural information systems in public sectors and would find out meaningful and considerable factors. This study would accept the methodologies of literature review and systems thinking approaches to understand the relationships among the found factors and to suggest the conceptual research model. Agricultural information system should take care to reduce implement and maintenance costs to reduce the negative relationships between costs and expected service value and between expected service value to perceived service value. Also, it should be understood that impersonal response would reduce the eager to use the services, so the government sectors should consider positively to adopt the concept of CRM even though the government sectors traditionally have ignored its necessity. The failure of public information systems/services may be caused not only by lack of the contents but also by the failure of the persistent post management.

• CRM연구
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• v.3 no.1
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• pp.49-71
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• 2010

This study set out to shed more specific light onto sales managers' coaching and salespersons'organizational commitment and role perceptions, which have been proven to work as important variables in salespersons' performance. It thus conducted an in-depth investigation into the overall sale management activities of sales managers from five high-sales organizations and five low-sales organizations and analyzed differences between them. The interviews of the ten sales managers were combined and analyzed. As a result, the ones from the high-sales organizations demonstrated the following characteristics: first, the salespersons of the high-sales organizations were strongly committed to the goals and values of their organizations. Second, the salespersons of the high-sales organizations had clear perceptions of their roles and showed relatively fewer role conflicts than those of the low-sales organizations. Third, the sales managers of the high-sales organizations demonstrated coaching behavior strongly. They provided positive feedback and role models for the salespersons to follow, thus earning great respect from them and maintaining trust-based relationships with them. And finally, the sales managers' organizational commitment and role perceptions had positive impacts on the salespersons' organizational commitment and role perceptions. Those research findings indicate that sales managers' organizational commitment and role perceptions can be a positive role model to salespersons and that such a role model can have influences on salespersons' performances as part of the characteristics of coaching behavior.

• Information Systems Review
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• v.21 no.4
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• pp.31-45
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• 2019

Despite the expansion of the market of performing arts and culture, small and medium size theaters are still experiencing difficulties due to poor accessibility of information by consumers. This study proposes a machine learning based genre recommendation system as an alternative to enhance the marketing capability of small and medium sized theaters. We developed five recommendation systems that recommend three genres per customer using customer master DB and transaction history DB of domestic venues. We propose an optimal recommendation system by comparing performances of recommendation system. As a result, the recommendation system based on the ensemble model showed better performance than the single predictive model. This study applied the personalized recommendation technique which was scarce in the field of performing arts and culture, and suggests that it is worthy enough to use it in the field of performing arts and culture.