• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.245-252
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• 2009

The electrical power industry has been recognized as a natural monopoly industry for its technological and industrial characteristics. However, a competitive market system has been introduced to that industry in Europe, North America and Australia to overcome the inefficiencies originated from the monopolistic system for decades. In Korea, the power industry is expected to be placed in a competitive market system within several years after separation and privatization of vertically integrated industry in progress. Hence, there is a need for a research on the increase of customer value in that industry, however, existing studies have little dealt with that problem and there is no research on the price policy to consider churn and retention of customers. Therefore, this study provides a methodology for increasing customer loyalty and lifetime value by presenting the lowest pricing plan which leads to diminishing customers' cost. It is verified through an empirical examination that firms can enhance customer loyalty using a price element in that industry and maximize their profit by finding out customers whose lifetime values would increase.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.341-356
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• 2007

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 standby demand effects. Through the empirical analysis, we found that different providers' churn-in customers are affected by different factors. Specifically, the number of chum-in customers into SK Telecom is explained mainly by SK Telecom's customer acquisition costs and standby demand from KTF, while the number of customers switching into KTF is better explained by switching costs from the previous service provider and standby 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.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.85-91
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• 2018

Most banks use only demographic information such as gender, age, occupation and address to segment customers, but they do not reflect financial behavior patterns of customers. In this study, we aim to solve the problems by using various big data in a bank and to develop customer segmentation method which can be widely used in many banks in the future. In this paper, we propose an approach of segmenting clustering blocks with bottom-up method. This method has an advantage that it can accurately reflect various financial needs of customers based on various transaction patterns, channel contact patterns, and existing demographic information. Based on this, we will develop various marketing models such as product recommendation, financial need rating calculation, and customer churn-out prediction based on this, and we will adapt this models for the marketing strategy of NH Bank.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.141-158
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• 2017

Along with IT development, customers are getting more easily to express their opinions using various IT channels. In this situation, complaint management is a pressing issue for companies to acquire and maintain loyal customers with low cost. Most of previous studies have investigated customer complaint information by quantitative variables such as demographic information, transaction information, or complaint frequency, but studies focusing on qualitative aspects of complaint information are limited. Therefore, this paper considers the possibility for customers to leave even when they complain occasionally or briefly. This paper analyzes the quantitive aspects as well as the qualitative aspects using sentiment analysis with Exit-voice theory. The dataset contains 268,364 inquiries of 46,235 customers obtained from a contact center of a private security company in Korea. This paper carries out logistic regression and the results imply that the customers's explicit response and their implicit sentiment have different effect on customers leave. This study is expected to provide useful suggestions for the effective complaint management.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.99-113
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• 2023

In a customer-based industry, customer retention is the competitiveness of a company, and improving customer retention improves the competitiveness of the company. Therefore, accurate prediction and management of potential dormant customers is paramount to increasing the competitiveness of the enterprise. In particular, there are numerous competitors in the domestic card industry, and the government is introducing an automatic closing system for dormant card management. As a result of these social changes, the card industry must focus on better predicting and managing potential dormant cards, and better predicting dormant customers is emerging as an important challenge. In this study, the Recurrent Neural Network (RNN) methodology was used to predict potential dormant customers in the card industry, and in particular, Long-Short Term Memory (LSTM) was used to efficiently learn data for a long time. In addition, to redefine the variables needed to predict dormant customers in the card industry, Unified Theory of Technology (UTAUT), an integrated technology acceptance theory, was applied to redefine and group the variables used in the model. As a result, stable model accuracy and F-1 score were obtained, and Hit-Ratio proved that models using LSTM can produce stable results compared to other algorithms. It was also found that there was no moderating effect of demographic information that could occur in UTAUT, which was pointed out in previous studies. Therefore, among variable selection models using UTAUT, dormant customer prediction models using LSTM are proven to have non-biased stable results. This study revealed that there may be academic contributions to the prediction of dormant customers using LSTM algorithms that can learn well from previously untried time series data. In addition, it is a good example to show that it is possible to respond to customers who are preemptively dormant in terms of customer management because it is predicted at a time difference with the actual dormant capture, and it is expected to contribute greatly to the industry.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.346-349
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• 2001

As intensifying competition alters the dynamics of the wireless industry and carriers begin to tap out traditional subscribers-business users and early adopters-wireless operators have come under pressure to begin expanding their target subscriber base. While alternative market segments such as youths, senior citizens, and lower-income or credit-challenged customers often offer less compelling fundamentals (i.e., lower average revenue per user and higher churn rates), carriers, in their race to increase market share, can no longer overlook these potential market segments. In particular, the youth market is a very appealing market segment for carriers to focus on for several reasons. Carriers in many parts of the world have already begun recognizing the compelling advantages of concentrating on youths and teens. This paper will examine the dynamics of the youth/teen population and what attributes make this group an appealing market for wireless carriers. In addition, it will take a look at new emerging technologies that may help carriers attract the youth market especially mobile data, entertainment applications, and wireless messaging. This paper also studies the sensation that carriers in Europe and Japan are achieving with the youth population and suggests how carriers in Korea can emulate that success.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.489-503
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• 2000

Data mining means data analysis and model selection using various types of data in order to explore useful information and knowledge for making decisions. Examples of data mining include scoring for credit analysis of a new customer and scoring for churn management, where the customers with high scores are given special attention. In this paper, scoring is interpreted as a modeling process of the conditional probability and polyclass scoring method is described. German credit data, a PC communication company data and a mobile communication company data are used to compare the performance of polyclass scoring method with that of the scoring method based on a tree model.

• 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.