• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.313-322
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• 2009

Recently, According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer patterns in automotive market with data mining using association rule and basic statics methods. With 4he help of information technology.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.372-380
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• 2010

Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is one of the fastest growing field in the computer industry. Because of According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies. Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer buying patterns in automotive market with data mining using decision tree as a quinlan C4.5 and basic statics methods.

• Journal of Korean Society for Quality Management
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• v.48 no.3
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• pp.421-431
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• 2020

Purpose: The purpose of this study is to compare and analyze the tokenizer in natural language processing for customer satisfaction in sentiment analysis. Methods: In this study, a supervised learning-based tokenizer Mecab-Ko and an unsupervised learning-based tokenizer SentencePiece were used for comparison. Three algorithms: Naïve Bayes, k-Nearest Neighbor, and Decision Tree were selected to compare the performance of each tokenizer. For performance comparison, three metrics: accuracy, precision, and recall were used in the study. Results: The results of this study are as follows; Through performance evaluation and verification, it was confirmed that SentencePiece shows better classification performance than Mecab-Ko. In order to confirm the robustness of the derived results, independent t-tests were conducted on the evaluation results for the two types of the tokenizer. As a result of the study, it was confirmed that the classification performance of the SentencePiece tokenizer was high in the k-Nearest Neighbor and Decision Tree algorithms. In addition, the Decision Tree showed slightly higher accuracy among the three classification algorithms. Conclusion: The SentencePiece tokenizer can be used to classify and interpret customer sentiment based on online reviews in Korean more accurately. In addition, it seems that it is possible to give a specific meaning to a short word or a jargon, which is often used by users when evaluating products but is not defined in advance.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.249-255
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• 2017

The domestic tourism industry mostly relies on quantitative surveys for customer satisfaction. However, customer participation of the questionnaires is extremely low and the improvement of the dissatisfactory factors is not being performed promptly. In this paper, we propose a new topic map system and prove its empirical effectiveness to improve the accuracy of customer feedback information and the efficiency of the analysis process. The topic map system is a system for analyzing large amounts of customer feedback data in real time. It uses text mining and ontology techniques by integrating data collected over a certain period from real-time SNS and quantitative data obtained from existing survey systems. The effect after improving the analyzed factors of dissatisfaction is also a new and innovative evaluation system for monitoring customer satisfaction in real time. The classification based on this integrated data is a classification system that is specific to the product or the customer. According to this classification, it is possible to measure the effect of the recognition and improvement of the complaint factor in real time on the topic map system. This provides a sophisticated prioritization of the improvement factors and enables customer satisfaction quality control as a PDCA feedback system. In addition, the survey period and costs are greatly shortened, and responses can be more precise to the existing survey method. As a practical application, this system is applied to the largest H travel agency in Korea to prove the accuracy and efficiency of the proposed system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1499-1508
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• 2018

Although fast-growing e-commerce markets gave a lot of companies opportunities to expand their customer bases, it is also the case that there are growing number of cases in which the so-called 'black consumers' cause much damage on many companies. In this study, we will implement and optimize a machine learning model that detects black consumers using customer data from e-commerce store. Using filter method for feature selection and 4 different algorithms for classification, we could get the best-performing machine learning model that detects black consumer with F-measure 0.667 and could also yield improvements in performance which are 11.44% in F-measure, 10.51% in AURC, and 22.87% in TPR.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.107-123
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• 2021

In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.91-94
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• 2001

The exponential growth of the Internet usage has motivated the launching of many commercial business web sites. Internet as a purchasing medium shows several unique characteristics because of its customer- driven technologies and absence of physical products. Thus, new commercial medium provokes a reclassification of products. Twenty five types of commercial Products are empirically tested in the Internet retailing and found to be grouped into four categories. This classification framework is investigated in the view of involvement and web technology Furthermore, this paper proposes four business web implementation strategies - impressive, simple, sensory, and semantic - based on the product classification. Proposed guidelines on business web might increase customer satisfaction.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.175-184
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• 2000

Recently Venture business in the area of information and communication industry is booming. Though Technology classification chart helps the potential entrepreneur through Survey paper and Internet Web Page, its service does not meet the customer demand. Hence Technology Classification system, which is proposed in this paper, will solve this problem by using virtual network among venture, technology experts and potential entrepreneurs. This system supports potential entrepreneurs' decision making for choice of venture business items by using dual client technology, and provides better services than existing systems by linking expert client and customer client, .

• Asia pacific journal of information systems
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• v.12 no.3
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• pp.169-189
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• 2002

In this study, we newly classify services and service products in EC into Mass Service, Interactive Service, Ordering Service, and Professional Service. For classification, five selected variables and two factorized dimensions such as 1) ease of on-line interaction and 2) needs of on-line interactions are used. On the result of classification, we investigate the relationships with customer purchase intentions in EC. For this purpose, customer surveys were conducted on the respondent groups who are known to be frequent purchasers in EC and have advanced knowledge on services and service products. The survey is analyzed by statistical methods such as factor score plot, cluster analysis, analysis of variance, etc.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.197-210
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• 2016

With the advent of text analytics, VOC (Voice of Customer) data become an important resource which provides the managers and marketing practitioners with consumer's veiled opinion and requirements. In other words, making relevant use of VOC data potentially improves the customer responsiveness and satisfaction, each of which eventually improves business performance. However, unstructured data set such as customers' complaints in VOC data have seldom used in marketing practices such as predicting service time as an index of service quality. Because the VOC data which contains unstructured data is too complicated form. Also that needs convert unstructured data from structure data which difficult process. Hence, this study aims to propose a prediction model to improve the estimation accuracy of the level of customer satisfaction by combining unstructured from textmining with structured data features in VOC. Also the relationship between the unstructured, structured data and service processing time through the regression analysis. Text mining techniques, sentiment analysis, keyword extraction, classification algorithms, decision tree and multiple regression are considered and compared. For the experiment, we used actual VOC data in a company.