• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.271-280
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• 2002

Recently, many companies have applied data mining techniques to promote competitive power in the field of their business market. In this study, we address how data mining, that is a technique to enable to discover knowledge from a deluge of data, Is used in an executed project in order to support decision making of an enterprise. Also, we develope scoring model on customer attrition probability for automobile-insurance company using data mining techniques. The development of scoring model in domestic insurance is given as an example concretely.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.227-233
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• 2004

This paper presents our work on constructing a model that is intended to predict the probability of attrition at specified points in time among customers of an insurance company. There are some difficulties in building a data-based model because a data set may contain possibly censored observations. In an effort to avoid such kind of problem, we performed logistic regression over specified time intervals while using explanatory variables to construct the proposed model. Then, we developed a Cox-type regression model for estimating the probability of attrition over a specified period of time using time-dependent explanatory variables subject to changes in value over the course of the observations.

• Journal of Global Scholars of Marketing Science
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• v.16 no.3
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• pp.1-16
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• 2006

Many financial service organizations use various types of penalties (e.g., late payment fee, overdraft fee), often inflicting customer complaints and, in extreme cases, attrition. This study examines how customers evaluate penalties using concepts from attribution theory and literatures of social justice and customer satisfaction/dissatisfaction. The study hypothesizes that both cognitive (i.e., attribution, perceived fairness, disconfirmation) and affective (i.e., emotion) responses influence customer's penalty judgment and tests the effect of moderation between attribution and perceived fairness on penalty judgment. The study uses a cross sectional survey design and collects data using the critical incident technique. The results show that attributions have significant moderating effects on the relationship between perceived fairness and dissatisfaction with the penalty and that perceived fairness, emotion, and attribution have a significant influence on penalty evaluation. The study provides discussion of the findings and managerial implications.

• 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.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.687-696
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• 2011

Customer Lifetime or Customer Lifetime Value is a essential metric of differentiated CRM marketing and differentiated marketing strategy as a company core competency. However, customer lifetime used in companies is easily obtained from a confined simple customer attrition rate at some specific time point regardless of customer characteristics. In this study, in order to overcome the constraints of previous simple methods and to make practical use of it in industries, we suggest a method that estimates a customer lifetime using a customer segment based survival analysis with the censored data of customers; in addition, we apply this method to A mobile telecom company data. A method using customer segment based survival analysis is suggested in this study 1) includes all customers having different subscription dates, 2) reduces individual error, 3) can reflect trends after the observed time point and is more realistic.

• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.