• Journal of Global Scholars of Marketing Science
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• v.17 no.4
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• pp.91-111
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• 2007

Marketing is said to be a zero sum game i.e. each gain of customer for a firm is always at the expense of some other firm's customers. Therefore in a marketplace churn is a natural occurrence. Churn in Indian telecom market is among the highest in growing telecom markets. By using binary logistics regression analysis based models based covering a sample of 822 Indian telecom subscribers; this paper attempts to examine the determinants of churn. The future churn is found to be dependent on satisfaction level of the customer with the service provider, attitude and loyalty of the customer variables, intended churn (i.e. intention to churn) and current loyalty (defined as intention to recommend) and distraction (i.e. intention to experiment).

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.23-36
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• 2013

In this study, we investigate how churn customers (who play a central connector or broker role) affect other customers' churn in their call networks with ego-network analysis using call data of a mobile telecom company in Korea. As a result of investigating Reciprocal Network, we found a relationship of attrition among churn customers. Churn provokers who influence other customers' attrition exist in customer churn networks. The characteristics of churn provokers is that they play a central connector and broker role in their groups. The proportion of churn provokers increases and the churn provoker's influence increases because the network is a reciprocal one.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.1-10
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• 2007

In this paper, we focus on churn analysis for the first successful candidates in the entrance examination on 2006 year using Clementine, data mining tool. The goal of this study is to apply decision tree including C5.0 and CART algorithms, neural network and logistic regression techniques to predict a successful candidate churn. And we analyze the churning and nochurning successful candidates and why the successful candidates churn and which successful candidates are most likely to churn in the future using data from entrance examination data of K university on 2006 year.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.1-10
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• 2024

For the purpose of predicting credit card customer churn accurately through data analysis, a model can be constructed with various machine learning algorithms, including decision tree. And feature importance has been utilized in selecting better input features that can improve performance of data analysis models for several application areas. In this paper, a method of utilizing feature importance calculated from the MDI method and its effects are investigated in the credit card customer churn prediction problem with classification trees. Compared with several random feature selections from case data, a set of input features selected from higher value of feature importance shows higher predictive power. It can be an efficient method for classifying and choosing input features necessary for improving prediction performance. The method organized in this paper can be an alternative to the selection of input features using feature importance in composing and using classification trees, including credit card customer churn prediction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2B
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• pp.157-169
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• 2003

The customer retention is one of major goals for telecommunication service providers as MNP(Mobile Number Portability) would be enforced soon. The purpose of this study is to construct a customer retention model by (1) extracting the statistically significant determinants that influence on the Out-bounding churn and In-bounding churn separately and (2) ranking the importance of sub-factors to minimize Out-bounding churn and to satisfy In-bounding churn. This model applies to domestic wireless telecommunication service providers and customer retention strategies are suggested based on the result.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.55-75
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• 2009

Price discounts presupposing churn-in are important tools of competition in many industries such as mobile telecommunication services and newspaper. In this case, consumers can get discount only through changing the provider. For analyzing this kind of competitions, we should consider the incentive of utilizing iterative switching. Hence, in this paper, we consider multi-stage equilibrium and can find that equilibria are different from one stage discount. In particular, when consumers' decisions are for maximizing multi-stage utility, discounts can bring about churn-out as well as churn-in and firms lose the incentive of discounts.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.454-462
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• 2014

Retention of possible churning customer is one of the most important issues in customer relationship management, so companies try to predict churn customers using their large-scale high-dimensional data. This study focuses on dealing with large data sets by reducing the dimensionality. By using six different dimension reduction methods-Principal Component Analysis (PCA), factor analysis (FA), locally linear embedding (LLE), local tangent space alignment (LTSA), locally preserving projections (LPP), and deep auto-encoder-our experiments apply each dimension reduction method to the training data, build a classification model using the mapped data and then measure the performance using hit rate to compare the dimension reduction methods. In the result, PCA shows good performance despite its simplicity, and the deep auto-encoder gives the best overall performance. These results can be explained by the characteristics of the churn prediction data that is highly correlated and overlapped over the classes. We also proposed a simple out-of-sample extension method for the nonlinear dimension reduction methods, LLE and LTSA, utilizing the characteristic of the data.

• 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 Information Technology Services
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• v.20 no.4
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• pp.35-52
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• 2021

Nowadays data-based decision making is emerging as the center of the business environment paradigm, but many companies do not have data-driven decision-making systems. It has also been studied that using an expert's intuition in decision making can be more efficient in terms of speed and cost, compared to analytical decision making. The goal of this study is to analyze customer churn factors using a group of experts within a financial company from the viewpoint of decision-making efficiency. We applied a debit card 'A', product of the National Credit Union Federation of Korea. The churn factors of all the financial expert groups were examined. Also. the difference in each group (management support, card recruitment, customer service group) was analyzed. We expect that this study will be helpful in the practical aspects of managers whose environments is lack data-oriented infrastructure and culture.

• Journal of KIISE
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• v.44 no.3
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• pp.314-322
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• 2017

In MMORPG (Massively Multiplayer Online Role-Playing Game), users advance their own characters to get to the maximum (max) level by performing given tasks in the game scenario. Although it is crucial to retain users with high levels for running online games successfully, little efforts have been paid to investigate them. In this study, by analyzing approximately 60 million in-game logs of over 50,000 users, we aimed to investigate the process through which users achieve the max level and churn of such users since the moment of achieving the max level, and determine possible indicators related to churn after the max level. Based on the result, we can predict churn of the max level users by employing behavioral patterns before the max level. Moreover, we found users who are socially active and communicate with many people before the max level are less likely to leave the service (p<0.05). This study supports that communication patterns are important factors for persistent usage of the users who achieve the max level, which has practical implications to guide elite users on enjoying online games in the long run.