• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.799-812
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• 2010

The public banking market (the main eld of the second banking sector) faces increased competition du to the expansion of the rst banking sector. In this situation, Customer Satisfaction Management(CSM is emerging as a core business factor to create continuous growth without competitive exclusion because it is possible to churn management and draw an advocate customer. In this pa- per, with Suhyup mutuality bank as a sample for research, I have looked for necessary Customer Satisfaction(CS) factors and deduced a Customer Satisfaction Index(CSI), Customer Loyalty and Net Promoter Score(NPS) of detail factors in CS through a survey. Based on these result, the strategic factors required to improve CS were found and strategic directions for CS were proposed through a CS portfolio analysis.

• Journal of Service Research and Studies
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• v.6 no.3
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• pp.57-78
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• 2016

The purpose of this research is to study how service recovery activities influence customers' commodity churning in the media telecommunication industry(CATV industry). Put it differently, we tried to identify this change of commodity churning rates by the stages of service failures, by which we intend to emphasize the importance of service recoveries. Korean media telecommunication market has already been saturated; customers tend to move to bigger major companies with better customer care increasingly. As once customers gone never returns, CRM functions are being reinforced over the time. We were able to have the following results. First, turning rates, for those experienced service failure, who were dissatisfied with service recovery activities are 2~5 times (monthly average turning rates are 1.3%) higher than those satisfied. Secondly, active service recovery activities at the customer's service request after experiencing service failure lowered churning rates significantly. The most effective timing is service recovery activities pre-recovery stage. Thirdly, reward activities after service recovery activities at the immediate recovery stage is more effective than service recovery at the arranged recovery schedule and reward activities after customer's expressing churning intension. The implications of this study are that firms should engage in service recovery activities at the time of identifying service failures, prior to customer's expressing churning intention, which means relatively lower ROI for the service recovery activities than the times of customers' expressing churning intention.

• 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.

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

• 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.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.225-234
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• 2013

The purpose of this study is to evaluate the impact of airline's retention equity on customer positive exclusive behavior intention and to minimize customer churn based maintenance is aimed to derive key variables in air transport market. A survey was conducted Incheon and Gimpo airport to use in the national carrier of domestic air travelers. A total of 480 respondents completed a survey. The result reveal that loyalty program, preferential treatment & acknowledgement program, and community program have significantly effect on positive behavior intention. However, preferential treatment & acknowledgement program, and community program have significantly effect on exclusive behavior intention. It showed that they are more influence than loyalty program as a switching barrier of airlines. The academic and practical implication of this study has been identified in the competitive market to maximize customer retention factors of maintaining retention equity to derive empirical strategic priorities.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.45-48
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• 2016

오늘날의 고객은 다양한 정보를 통해 넓은 선택의 기회를 가진다. 이러한 상황에서 기업들은 고객과의 지속적인 관계를 유지하기 어려워짐에 따라 고객 유지와 신규 고객 유치를 위한 마케팅 비용을 천문학적으로 지출하고 있다. 기업들이 이탈하는 고객의 속성을 분석하고 이탈 시점을 예측할 수 있다면 마케팅에 사용되는 비용과 노력을 최소화할 수 있을 것으로 예측된다. 이를 위해 본 논문에서는 효과적인 고객 이탈 예측을 위한 딥러닝 기반의 이탈 예측 모델을 제안한다. 이 모델은 모바일 RPG 게임 고객의 시계열적인 행동 패턴을 이용하여 이탈을 예측하는 모델로, 예측을 위한 학습을 할 때 모델링된 고객 데이터를 분석하여 이탈 고객의 특성을 파악할 수 있게 한다. 실험을 통해 이탈 고객과 미 이탈 고객의 모델링된 값이 각각 특정 속성에 치중되어 있는 것을 확인하였고, 제안 모델이 합리적으로 고객의 이탈을 예측하는 것을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.109-110
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• 2013

국내 이동통신 서비스 시장 사업자들은 신규고객 유치에 집중하기 보다는 기존고객 유지에 더 관심을 가지고 있다. 이러한 배경에는 새로운 신규고객의 창출에 소요되는 비용이 기존고객을 유지하는 비용이 적게 들기 때문이다. 따라서 고객이탈을 발생시키는 요인이 무엇인지를 본 연구에서 알아보고자 한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.280-284
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• 2003

효과적인 CRM(고객관계관리)을 위해서 고객의 가치를 파악하는 것은 매우 중요하다. 모든 고객이 같은 가치를 제공하지 않기 때문에 차별적인 서비스를 통해 장기적인 관점에서 기업의 수익을 최대화한다는 것이 CRM의 기본전제가 되기 때문이다. 기존의 고객가치를 측정하는 것은 LTV(고객생애가치)를 중심으로 연구되어 왔다. 그러나 이러한 LTV는 환경의 변동성, 고객의 변동성 등 많은 불확실성을 안고 측정될 수밖에 없고 단순히 과거 기업에게 제공된 과거 수익의 추이를 고려하기 때문에 실제 세분화 시 간과하게 되는 점이 많다. 따라서 본 논문에서는 단기적인 활용관점에서 고객의 이탈률(Churn Rate)을 고려하여 고객의 가치를 현재가치, 잠재가치로 나누어 측정하고 이를 통해 고객 세분화의 방안을 제시해 본다.