• Journal of Information Management
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• v.40 no.1
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• pp.183-197
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• 2009

With development of the internet, internet shopping is taking its place as one of digitalization industries transcending time and space beyond the scope of commercial activities as the means of goods sales and purchase. We studied about the relations of customer value, environment of internet shopping mall, customer satisfaction and loyalty. Customer value is customers' subjective evaluation, which is formed after their purchasing and consuming. Customer satisfaction can be characterized as post-purchase evaluation of product quality given pre-purchase expectations. Customer loyalty is a potentiality or ensure of durative relationship between customer and enterprises. Customer satisfaction functions as an antecedent of customer loyalty, while customer value does customer satisfaction. It prevents customer churn and consolidates retention, thereby constituting an important cause of customer loyalty. This study shows that customer value, environment of internet shopping mall and customer satisfaction are each found to have a direct effect on customer loyalty. The results provide empirical support for relation between customer satisfaction and loyalty. To increase customer satisfaction and customer loyalty in internet shopping mall is the primary purpose of this study. We believe that only high quality based customer programs accompanied by well designed loyalty programs can be effective in increasing customer retention.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1007-1018
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• 2008

The purpose of this case study is to demonstrate database-marketing management. First, we explore original variables for insurance customer's data, modify them if necessary, and go through variable selection process before analysis. Then, we develop churn prediction models using logistic regression, neural network and SVM analysis. We also compare these three data mining models in terms of misclassification rate.

• Korean Management Science Review
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• v.25 no.2
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• pp.57-71
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• 2008

We study factors that affect consumers' switching behaviors among service providers in Korean mobile telecommunications service market. For empirical analysis, quarterly time series data from the first quarter of 2004 through the second quarter of 2007 were used. We chose the number of switchers to each mobile service provider in each quarter as dependent variables. Independent variables include acquisition costs per subscriber, which play the role of subsidy to mobile handset, switching costs, time trend, structural change effect, and waiting demand effects. Through the empirical analysis, we found that each provider's churn-in customers are affected by different factors. Specifically, the number of churn-in customers into SK Telecom is explained mainly by SK Telecom's customer acquisition costs and waiting demand from KTF, while the number of customers switching into KTF is better explained by switching costs from the previous service provider and waiting demand from SK Telecom. Those who chose LG Telecom as their new provider, on the other hand, were mainly attracted by LG Telecom's high subscriber acquisition cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4706-4724
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• 2020

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

• Journal of the Korea Society of Computer and Information
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• v.6 no.3
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• pp.29-34
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• 2001

Competition is increasing in telecommunication service market. Effective customer retention strategies are based on a clear understanding of customer defection. Data mining offers service providers great opportunities to get closer to customer. In this paper, we propose an efficient data mining algorithm using neural network. Especially Analysis of CRM Using Neural Networks in Telecommunication service Market and a practical application of neural network is described telco, churn management This paper builds model of customer defection management and analyzes customer defection with data mining

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1431-1439
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• 2017

In the security field, log analysis is important to detect malware or abnormal behavior. Recently, image visualization techniques for malware dectection becomes to a major part of security. These techniques can also be used in online games. Users can leave a game when they felt bad experience from game bot, automatic hunting programs, malicious code, etc. This churning can damage online game's profit and longevity of service if game operators cannot detect this kind of events in time. In this paper, we propose a new technique of PNG image conversion based churn prediction to improve the efficiency of data analysis for the first. By using this log compression technique, we can reduce the size of log files by 52,849 times smaller and increase the analysis speed without features analysis. Second, we apply data mining technique to predict user's churn with a real dataset from Blade & Soul developed by NCSoft. As a result, we can identify potential churners with a high accuracy of 97%.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.167-183
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• 2006

Since data mining attempts to find unknown facts or rules by dealing with also vaguely-known data sets, it always suffers from high error rate. In order to reduce the error rate, many researchers have employed multiple models in solving a problem. In this research, we present a new type of multiple models, called DyMoS, whose unique feature is that it classifies the input data and applies the different model developed appropriately for each class of data. In order to evaluate the performance of DyMoS, we applied it to a real customer churn problem of an automobile insurance company, The result shows that the DyMoS outperformed any model which employed only one data mining technique such as artificial neural network, decision tree and case-based reasoning.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1237-1248
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• 2011

Social network analysis(SNA) has been recently applied to business areas such as social network services (such as Facebook and Twitter). In addition, the mobile telecommunication field attempts to analyze CDR(call detail record) data and apply customer relationship management and customer churn management through the use of social network analysis. The paper analyzes links between ego and alter based on ego-network and discovers four kinds of customer roles and then provides insights as a tool for customer relationship management or customer management.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.115-136
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• 2014

The game industry has grown steadily and the online game has become one of the most attractive game segments for its remarkable growth. Customer management in the online game industry, however, has received little attention from the academic field. The purpose of this study is to analyze the drivers of customer defection in the online game setting and suggest not only theoretical but also managerial insights into increasing customer retention rates. Prior to empirical analysis, the authors hypothesized that 3 variables of interests (Learning, Playing, Achievement) would explain the customer defection according to preceeding researches. To demonstrate these hypotheses, the authors obtained data from one of the biggest game publishers in Korea, and the empirical analysis model was developed considering context of research settings. The results of analyses provide the following insights. First, the key behavioral variables of Learning, Playing, and Achievement play substantial roles in explaining the customer defection. Next, the effects of these variables vary between customer types: novice and experienced customers. The defection decisions by novice customers are predicted by all key behavioral variables and Playing serves as the most influential indicator of the defection decisions. However, experienced customers are influenced by Playing and Achievement, while Learning has no impact on the defection decisions. Finally, the authors investigated hypothetical customer retention strategies, using the empirical results. The market outcomes indicate that the customer retention strategies work well with novice customers and it is hard-to-impossible to prevent experienced customers from defection using their behavioral data. These findings together deliver several meaningful insights to management as follow. First, the management should support customers to get involved in Learning activities at the very first stage. Second, customer's Achievement and appropriate compensation for it would work as defection barriers. Last, to optimize the outcomes of firm's marketing investments, it is better to focus on retention of novice users not experienced ones.

• The Journal of Information Systems
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• v.18 no.3
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• pp.375-399
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• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.