• Journal of the Korea Society of Computer and Information
• /
• v.15 no.12
• /
• pp.227-235
• /
• 2010

This paper proposes the recommendation system which is advanced using RFM method and Association Rules in e-Commerce. Using a implicit method which is not used user's profile for rating, it is necessary for user to keep the RFM score and Association Rules about users and items based on the whole purchased data in order to recommend the items. This proposing system is possible to advance recommendation system using RFM method and Association Rules for cross-selling, and also this system can avoid the duplicated recommendation by the cross comparison with having recommended items before. And also, it's efficient for them to build the strategy for marketing and crm(customer relationship management). It can be improved and evaluated according to the criteria of logicality through the experiment with dataset collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system for one to one web marketing in e-Commerce.

• Korean Management Science Review
• /
• v.19 no.1
• /
• pp.129-142
• /
• 2002

Data fusion Is a technique used for creating an Integrated database by combining two or more databases that include a different set of variables or attributes. This paper attempts to apply data fusion technique to customer relationships management (CRM), in that we can not only plan a database structure but also collect and manage customer data In a more efficient way In particular our study Is useful when no s1n91e database Is complete, i.e., each and every subject in the pre-integrated database contains somewhat missing observations. According to the way of treating the common variables, donors can be differently selected for the substitution of the missing attributes of recipients. One way is to find the donor that has the highest correlation coefficient with the recipient by. treating common variables metrically The other is based on the closest distance by the correspondence analysis in case of treating common variables nominally. The predictability of data fusion for CRM can be evaluated by measuring the correlation of the original database and the substituted one. A Monte Carlo Simulation analysis is used to examine the stability of the two substitution methods in building an integrated database.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.3
• /
• pp.121-128
• /
• 2010

Whenever and wherever, recognizing location of object such as human as well as things, ubiquitous Location-Based Service which provides useful service based on this are rising as an important service. In this paper, we implemented customized real time 1:1 e-CRM system that can communicate with user's mobile phone through using Location-Based Service of mobile phone. Among various scenarios that are applicable to designed system, development and test were conducted based on scenario that provides shopping information and discount information to customers through SMS as they approach to vicinity of large discount store and allow them to download discount coupons through Call Back. The suggested system will be applied to various service event and can be used as a customized real time marketing method according to user's personal activity area.

• Journal of Distribution Science
• /
• v.11 no.5
• /
• pp.43-53
• /
• 2013

Purpose - The purpose of this study is to analyze the effectiveness of customer relationship management (CRM) support policies for facilitating traditional markets, especially with respect to customer acquisition and maintenance, and to investigate the factors affecting CRM. Research design, data, and methodology - We analyzed the CRM efficiency of traditional markets in 16 cities and provinces in Korea on the basis of DEA analysis and Malmquist productivity analysis. The DEA model calculates a ratio of the weighted mean of various inputs to the weighted mean of various outputs and measures the efficiency of a specific decision making unit (DMU), which is compared to the reference group that has a similar input-output structure. The input variables are coupon, event, parcel service, premiums, while is the number of customers per day. Further, through regression analysis, we analyzed CRM-related factors affecting traditional markets' customer appeal and revenue growth. Results - We obtained the results of the efficiency of traditional markets in 16 provinces. The traditional markets in Seoul, Busan, and Jeju were found to be efficient in a model CCR that used the number of customers per day as an output variable, while Chungbuk, Jeonbuk Province, and According to the results of the DEA analysis and Malmquist productivity analysis, large cities such as Seoul, Busan, and Jeju showed efficiency in CRM-related investment businesses in traditional markets for attracting customers. The Malmquist analysis results confirmed that the productivity of traditional markets increased from 2008 to 2010. The results of the regression analysis revealed that the "customer acquisition/maintenance factor" and the "offering of customer convenience facility factor" were significant to the daily average number of customers, which is a dependent variable. The results of the test with the mediating variable, "number of customers," and the final dependent variable, "sales revenue," were rejected. However, the variable "customer acquisition /maintenance" was found to affect sales revenue positively. Conclusions - It is necessary to enhance the business not only for promotional activities to attract customers, but also to strengthen customer relationships among CRM businesses, such as through the management of key customers. The regression analysis results showed that CRM businesses have yet to produce an increase in sales revenues in traditional markets. Therefore, to help customers who visit traditional markets to keep buying products, it is necessary to prepare various investment methods and provide support to improve "customer loyalty." This study has a limitation in terms of CRM-related statistics. Therefore, in the future, it is necessary to conduct a survey of customers who use traditional markets to analyze the markets by type and size as well as the CRM-related factors. Based on the analysis, we will try to perform a variety of statistical analyses, including structural equations.

• Information Systems Review
• /
• v.10 no.2
• /
• pp.137-158
• /
• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of Digital Contents Society
• /
• v.13 no.2
• /
• pp.205-211
• /
• 2012

This paper presents research result of digital marketing model for improving enterprise marketing campaign performance. Recently, the enterprises which had completed projects such as ERP, CRM, and SCM for business value chain process transformation are working to improve enterprise marketing process. It is the trend for enterprises to use digital marketing tactics to overcome the limit of existing traditional marketing tactics. Especially, enterprises try to adopt digital marketing for marketing campaign performance. In this paper, digital marketing research model and hypothesis were established and statistically analyzed by marketing expert survey research. The research finding is that Web Analytics, Social Analytics, Personalized CRM, Campaign execution automation, Real-Time campaign management can be core influencers for marketing campaign performance improvement.

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

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.8
• /
• pp.127-136
• /
• 2009

Voice of the customer(VOC) is a term used in business and information technology(IT) to describe the process of capturing a customer's requirements in enterprises or various organizations. Recently, in order to satisfy customer's needs, enterprises try to utilize VOC at recurrence prevention of problems and their improvement activities, planning and development of product/service by processing, storing, and analyzing VOC. Until now, VOC management systems are introduced around service industries such as hotel business and insurance/financial business, etc. This paper proposes an integrated management scheme of VOC which are captured by various communication channels and describes a case of implementing an integrated VOC management system on the basis of the proposed scheme for the large-scale manufacturing company. By the implemented system, VOC are stored and utilized as the important knowledge assets of enterprises.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.1
• /
• pp.27-36
• /
• 2002

VoiceXML is a standard dialog mark-up language for the neat generation voice applications. The current VoiceXML 1.0 specification is silent on who place outbound calls for push-based voice applications. The push-barred voice applications become very important in modern information systems such as CRM. In this paper, we design and implement an extended VoiceXML platform that supports both inbound and outbound voice information services. We also extend the VoiceXML DTD so as to be able to inbound/outbound fax based on Call Control Requirements of W3C.

• Information Systems Review
• /
• v.7 no.2
• /
• pp.23-40
• /
• 2005

Discovering customers' behavioral patterns from large data set and providing them with corresponding services or products are critical components in managing a current business. However, the diversity of customer needs coupled with the limited resources suggests that companies should make more efforts on understanding and managing specific groups of customers, not the whole customers. The key issue of this paper is based on the fact that the behavioral patterns extracted from the specific groups of customers shall be different from those from the whole customers. This paper proposes the idea of pre-segmentation before developing customer classification models. We collected three customers' demographic and transactional data sets from a credit card, a tele-communication, and an insurance company in Korea, and then segmented customers by major variables. Different churn prediction models were developed from each segments and the whole data set, respectively, using the decision tree induction approach, and compared in terms of the hit ratio and the simplicity of generated rules.