• Journal of Korean Society for Quality Management
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• v.42 no.2
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• pp.221-234
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• 2014

Purpose: The Purpose of this study is to develop a model for predicting agent churn group in the cosmetics industry. We develope two models, pattern model and matrix model, which are compared regarding the prediction accuracy of churn agents. Finally, we try to conclude if there is statistically significant difference between two models by empirical study. Methods: We develop two models using the part of RFM(Recency, Frequency, Monetary) method which is one of customer segmentation method in traditional CRM study. In order to ensure which model can predict churn agents more precisely between two models, we used CRM data of cosmetics company A in China. Results: Pattern model and matrix model have been developed. we find out that there is statistically significant differences between two models regarding the prediction accuracy. Conclusion: Pattern model and matrix model predict churn agents. Although pattern model employed the trend of monetary mount for six months, matrix model that used the amount of sales per month and the duration of the employment is better than pattern model in prediction accuracy.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.187-190
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• 2003

Since the early 1980s, the concept of relationship management in marketing area has gained its importance. Acquiring and retaining the most profitable customers are serious concerns of a company to perform more targeted marketing campaigns. For effective CRM (Customer Relationship Management), it is important to gather information on customer value. Many researches have been performed to calculate customer value based on CLV (Customer Lifetime Value). It, however, has some limitations. It is difficult to consider the churn of customers, because the previous prediction models have focused mainly on expected future cash flow derived from customers'past profit contribution. In this paper we suggest a CLV model considering past profit contribution, potential benefit, and churn probability of a customer. We also cover a framework for analyzing customer value and segmenting customers based on their value. Customer value is classified into three categories: current value, potential value and customer loyalty. Customers are segmented according to the three categories of customer value. A case study on calculating customer value of a wireless communication company will be illustrated.

• 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 Safety Management & Science
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• v.9 no.3
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• pp.111-118
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• 2007

What is CRM(Customer Relationship Management) means that planning, executing, and re-accessing the marketing strategy based on the customer character by analyzing the material related to customers. That is CRM is a strategy of customer service on the base of data. In the case of the telecommunications and a newspaper, there are restricted application of CRM, because they are provided services by paying a given amount of money within a given period of time. This paper develops CRM model(chum prediction model) that can apply to a newspaper. For model-building, real data were used which were collected from one of the major a newspaper company in Korea. Also, this paper verifies the efficient result.

• Asia pacific journal of information systems
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• v.12 no.1
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• pp.139-158
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• 2002

As the telecommunication market becomes mature in Korea, severe competition has already begun on the market. While service providers struggled for the last couple of years to acquire as many new customers as possible, nowadays they are making more efforts on retaining the current customers. The churn management by analyzing customers' demographic and transactional data becomes one of the key customer retention strategies which most companies pursue. However, the customer data analysis has still remained at the basic level in the industry, even though it has considerable potential as a tool for understanding customer behavior. This paper develops several churn prediction models using data mining techniques such as logistic regression, decision trees, and neural networks. For model-building, real data were used which were collected from one of the major telecommunication companies in Korea. This paper explores various ways of comparing model performance, while the hit ratio was mainly focused in the previous research. The comparison criteria used in this study include gain ratio, Kolmogorov-Smirnov statistics, distribution of the predicted values, and explanation ability. This paper also suggest some guidance for model selection in applying data mining techniques.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.109-126
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• 2006

An effective customer classification has been essential for the successful customer relationship management. The typical customer rating is carried out by the proportionally allocating the customers into classes in terms of their life time values. However, since this method does not accurately reflect the homogeneity within a class along with the heterogeneity between classes, there would be many problems incurred due to the misclassification. This paper suggests a new method of rating customer using Holistic profit technique, and validates the new method using the customer data provided by an insurance company. Holistic profit is one of the methods used for deciding the cutoff score in screening the loan application. By rating customers using the proposed techniques, insurance companies could effectively perform customer relationship management and diverse marketing activities.

• Information Systems Review
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• v.23 no.3
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• pp.159-176
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• 2021

Obviating user churn is a prominent strategy to capitalize on online games, eluding the initial investments required for the development of another. Extant literature has examined factors that may induce user churn, mainly from perspectives of motives to play and game as a virtual society. However, such works largely dismiss the service aspects of online games. Dissatisfaction of user needs constitutes a crucial aspect for user churn, especially with online services where users expect a continuous improvement in service quality via software updates. Hence, we examine the relationship between a game's quality management and its user base. With text mining and survival analysis, we identify complaint factors that act as key predictors of user churn. Additionally, we find that enjoyment-related factors are greater threats to user base than usability-related ones. Furthermore, subsequent quasi-experiment shows that improvements in the complaint factors (i.e., via game patches) curb churn and foster user retention. Our results shed light on the responsive role of developers in retaining the user base of online games. Moreover, we provide practical insights for game operators, i.e., to identify and prioritize more perilous complaint factors in planning successive game patches.

• Management Science and Financial Engineering
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• v.16 no.3
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• pp.49-70
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• 2010

Customer population management models can be classified into three categories: the first category includes the models that analyze the customer population at cohort level; the second one deals with the customer population at aggregate level; the third one has interest in the interactions among the customer populations in the competitive market. Our study proposes a model that can analyze the dynamics of customer population in consumer-durables market at aggregate level. The dynamics of customer population includes the retention curves from the purchase or at a specific duration time, the duration time expectancy at a specific duration time, and customer population growth or decline including net replacement rate, intrinsic rate of increase, and the generation time of customer population. For this study, we adopt mathematical ecology models, redefine them, and restructure interdisciplinary models to analyze the dynamics of customer population at aggregate level. We use the data of previous research on dynamic customer population management at cohort level to compare its results with those of ours and to demonstrate the useful analytical effects which the precious research cannot provide for marketers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.69-77
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• 2019

As information technology advances, the penetration of smart devices connected to the Internet, such as smart phone and tablet PC, has rapidly expanded, and as sensor prices have fallen the Internet of Things has begun to be introduced in the industry. Today's industry is rapidly changing and evolving, requiring companies to respond to the new paradigm of business. In this situation, companies need to actively manage and maintain customer relationships in order to acquire loyal customers who bring them a high return. The purpose of this study is to suggest a method to manage customer relationship using real time IoT data including IoT product usage data, customer characteristics and transaction data. This study proposes a method of segmenting customers through RFM analysis and transition index analysis. In addition, a real-time monitoring through control charts is used to identify abnormalities in product use and suggest ways of differentiating marketing for each group. In the study, 44 samples were classified as 9 churn customers, 10 potential customers, and 25 active customers. This study suggested ways to induce active customers by providing after-sales benefit for product reuse to a group of churn customers and to promote the advantages or necessity of using the product by setting the goal of increasing the frequency of use to a group of potential customers. Finally, since the active customer group is a loyal customer, this study proposed an one-on-one marketing to improve product satisfaction.