• Korean Management Science Review
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• 제19권1호
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• pp.189-196
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• 2002

Data clustering is often one of the first steps in data mining analysis. It Identifies groups of related objects that can be used as a starling point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. This paper Purpose to present the development of two phase hierarchical clustering algorithm for group formation. Applications of the algorithm for product-customer group formation in customer relationahip management are also discussed. As a result of computer simulations, suggested algorithm outperforms single link method and k-means clustering.

• Journal of Information Technology Services
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• 제19권4호
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• pp.85-107
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• 2020

The Internet of Things market, a technology that connects the Internet to various things, is growing day by day. Besides, various smart home services using IoT and AI (Artificial Intelligence) are being launched in homes. Related to this, existing smart home-related studies focus primarily on ICT technology, not on what service improvements should be made in customer positions. In this study, we will use smart home application customer review data to classify functional and design elements of smart home usability value and examine the ways customers think of service improvement. For this, LG Electronics and Samsung Electronics" Smart Home application, the main provider of Smart Home in Korea, customer reviews were crawled to conduct a comparative analysis between them. In this study, the review of IoT home-applications was analyzed to find service improvement insights from customer perspective, and related analysis of text mining, social network analysis and Doc2vec was used to efficiently analyze data equivalent to about 16,000 user reviews. Through this research, we hope that related companies effectively seek ways to improve smart home services that reflect customer needs and are expected to help them establish competitive strategies by identifying weaknesses and strengths among competitors.

• Korean Management Science Review
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• 제21권3호
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• pp.55-69
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• 2004

This study analyzes customer buying-behavior patterns in a department store as time goes on, and predicts moving patterns of its customers. Through them, it suggests in this paper short-term and long-term marketing promotion strategies. RFM techniques are utilized for customer segmentation. Customers are clustered by using the Kohonen's Self Organizing Map as a method of data mining techniques. Then C5.0, a decision tree analysis technique, is used to predict moving patterns of customers. Using real world data, this study evaluates the prediction accuracy of predictive models.

• Journal of Korea Trade
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• 제25권2호
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• pp.22-45
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• 2021

Purpose - This study aims to identify new attributes by analyzing reviews conducted by global automaker customers and to examine the influence of these attributes on satisfaction ratings in the U.S. automobile sales market. The present study used J.D. Power for customer responses, which is the largest online review site in the USA. Design/methodology - Automobile customer reviews are valid data available to analyze the brand personality of the automaker. This study collected 2,998 survey responses from automobile companies in the U.S. automobile sales market. Keyword analysis, topic modeling, and the multiple regression analysis were used to analyze the data. Findings - Using topic modeling, the author analyzed 2,998 responses of the U.S. automobile brands. As a result, Topic 1 (Competence), Topic 5 (Sincerity), and Topic 6 (Prestige) attributes had positive effects, and Topic 2 (Sophistication) had a negative effect on overall customer responses. Topic 4 (Conspicuousness) did not have any statistical effect on this research. Topic 1, Topic 5, and Topic 6 factors also show the importance of buying factors. This present study has contributed to identifying a new attribute, personality. These findings will help global automakers better understand the impacts of Topic 1, Topic 5, and Topic 6 on purchasing a car. Originality/value - Contrary to a traditional approach to brand analysis using questionnaire survey methods, this study analyzed customer reviews using text mining. This study is timely research since a big data analysis is employed in order to identify direct responses to customers in the future.

• East Asian Journal of Business Economics (EAJBE)
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• 제9권4호
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• pp.93-102
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• 2021

Purpose - Among the various pricing approaches used to set fares for express trains, this study explores a method of utilizing a customer-based pricing approach. The purpose of this study is to figure out how to apply the customer-based pricing approach to fares of new railway services using express trains. Research design, data, and methodology - This study was conducted through a literature review and case studies. In the literature review, we examined three approaches, focusing on the customer-based pricing approach and its application. In the case studies, we show how a customer-based pricing approach can be applied to determining the fares for railway services. Result - Some studies have used a customer-based pricing approach to set railway service rates, adapting the concepts of customer-based pricing such as demand, elasticity, value and willingness to pay. When setting fares of new railway services, it is recommended to use the customer-based approach in conjunction with other pricing approaches. Conclusion - This study demonstrates that a customer-based pricing approach is a promising tool in making decisions on railway fares. By applying a customer-based pricing approach to fares for new railway services using express trains, railway operators can utilize new service rates and increase the profitability of the railway business.

• Korean Management Science Review
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• 제19권2호
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• pp.205-220
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• 2002

As the business structure of many industries changes under IT progress and internet economy, the customer information has emerged a key factor in setting up the management policy. The customer has come to replace the product as a central figure in business competition. The domestic life insurance market has also experienced the rapid structural changes in IT time. The competition in the insurance industry to maintain the existing membership and to attract the new members gets stronger under such a new business circumstance. Accordingly, it is necessary for an individual insurance company to develop a systematic marketing plan, based on the customer information, to be competitive in the market. Unlike other studies in which customer characteristics are neglected, this study attempts to utilize the customer information by applying the data mining technique, and then suggests an efficient marketing strategy that could prevail in the competitive business environment.

• Journal of Distribution Science
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• 제17권5호
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• pp.85-94
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• 2019

Purpose - The purpose of this study is to analyze the impact of customer's communication on sales performance in the online market. Research design, data, and methodology - This study uses linear regression analysis to examine the effects of product review characteristics which are the result of customer's communication, on sales performance by using product reviews of online marketplace Amazon. Result - The increase in the number of product reviews positively affected sales performance. An increase in extreme opinions in the product review has a positive effect on sales performance. The product review length has a negative effect on sales performance. Conclusions - This study has shown the online marketplace customers' communication can influence sales performance using product review big data. This study contributed to the theoretical completeness by analyzing all the products of the book category in Amazon online market. This research will complement the theories regard to the customer behavior affecting sales performance. We expect the empirical analysis result will provide empirical help to sellers, online marketplace operators, and customers. In particular, the number of letters in the product may negatively affect sales performance, so sellers need to consider this effect carefully when exposing product reviews.

• Korean Management Science Review
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• 제20권2호
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• pp.135-149
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• 2003

Developing a proper program for customer evaluation is one of the most imminent tasks to implement CRM (Customer Relationship Management). Design of the Customer Value model is an important key to the customer evaluation progrgm. This paper proposes two models for estimating Customer Value. The first one is a Description Model for Customer Value based on customer CSI (Customer Satisfaction Index) data. This model represents as quantitative numbers what customers feel from the company or the service. The second one is a Prediction Model which employs factor analysis and regression to predict customer value. This paper exploits the two models to evaluate Customer Value as well as for customer behavior prediction.

• Journal of Intelligence and Information Systems
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• 제29권1호
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• The Journal of Information Systems
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• 제31권2호
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• pp.1-18
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• 2022

Purpose Customer review is a major factor in choosing certain restaurants. This study investigates the key factors affecting customer's evaluation about restaurants. With the recent intensification of competition among restaurants in the service industry, the analysis results are expected to provide in-depth insights for enhancing customer experiences. Design/methodology/approach We collected information and reviews provided at the restaurants in the Kakao Map platform. The information collected is based on the information of 3,785 restaurants in Daegu registered on Kakao Map. Based on the information collected, seven independent variables, including number of rating registered, number of reviews, presence or absence of safe restaurants, presence or absence of a posting about holding facilities, presence or absence of a posting about business hours, presence or absence of a posting about hashtags, and presence or absence of break times, were used. Dependent variable is restaurant rating. Multiple regression between independent variables and restaurant rating was carried out. Findings The results of the study confirmed that number of rating registered, presence or absence of a posting about business hours, and presence or absence of a posting about hash tags have an positive effects on the restaurant rating. The number of reviews had a negative effect on the restaurant rating. In addition, in order to confirm the role of customer's reviews, we carried out LDA topic modeling. We divided the topics into the positive review and the negative reviews.