• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.3
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• pp.63-82
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• 2014

The main objective of this research is to investigate whether the RFM (recency-frequency-monetary value) information of a customer's redemption behavior of loyalty points can improve the prediction of future value of the customer. The conventional measurement of customer value has been primarily based on purchase transactions behavior although a customer's future behavior can be also influenced by other interactions between the customer and the firm such as redemption of rewards in a loyalty program. We theorize why a customer's redemption behavior can influence her future purchases and thereby the customer's total value based on operant learning theory, goal gradient hypothesis, and lock-in effect. Using a dataset from a major book store in Korea spanning three years between 2008 and 2010, we analyze both purchase transactions and redemption records of over 10,000 customers. The results show that the redemption-based RFM information does improve the prediction accuracy of the customer's future purchases. Based on this result, we also propose an improved estimate of customer lifetime value (CLV) by combining purchase transactions and loyalty points redemption data. Managerial implications will be also discussed for firms managing loyalty programs to maximize the total value customers.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.109-115
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• 2009

Many internet companies are holding marketing activity through customer relationship management to satisfy complicated and diversified consumer demands. Use of customer lifetime value as a marketing metric tends to place greater emphasis on customer service and long-term customer satisfaction, rather than on maximizing short term sales. And so many internet companies have been interested in customer lifetime value, which is a primary key for discovery customer values to promote the competitive power in their business fields. In this paper, we apply a life table technique to lifetime analysis of internet site customers by membership pattern and provide the opportunity using revised life tables in several kinds of internet companies.

• 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.

• Journal of the Korea Safety Management & Science
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• v.6 no.1
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• pp.219-228
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• 2004

We can be acquire the conformation about on the due date of supplier by using the A TP(A vail able to Promise) function of management about real and concurrent access on the supply chain, also decide the affect about product availability due to forecasting or customer's orders through the A TP. Under the these environments, defines the A TP rule that can improve the customer value and data flow related the LTV(Life Time Value) and builds on a algorithm. In this paper, It consolidates the necessity on a LTV (Life Time Value) and analyzes data which is concerned of Customer Value. Under the these environments, defines the LTV rule that can improve the customer value. And then, Scheduling plays an important role in shop floor planning. Therefore, this study tries to proposed that Scheduling by customer needs group for minimizing the problem.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.259-263
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• 2002

CLV(Customer Lifetime Value; 고객생애가치)는 한 고객이 기업의 고객으로 존재하는 전체 기간동안 그 기업에게 제공할 것으로 추정되는 잠재적인 수익의 합계로 정의할 수 있으며, CRM(Customer Relationship Management)이 기업의 경쟁력 강화를 위한 핵심 수단으로 등장하면서 고객가치에 대한 측정 및 분석의 필요성이 증가하고 있다. 본 연구에서는 CLV를 측정하기 위해서 제안된 여러 모형들을 소개, 비교하고 몇 가지 측면에서 기존 모형들의 단점을 보안할 수 있는 모형들을 제안하고자 한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.26-33
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• 2006

The potential needs as well as visible needs of customer should be considered in order to research and analyze of the customer data. The methods to analyze customer data is classified into customer segmentation, clustering analysis model, forecasting customer response probability model, analysis of the customer break rate model and new customer analysis model by the purpose. In this study, we developed the CW-CLV (Correlation Weight Customer Lifetime Value)method that used AHP(Analytic Hierarchy Process)rule for enhance the reliability of customer data and quantitative analysis of the customer segmentation, based on CLV(Customer Lifetime Value). We suggest to new variables and methodology from determined CW-CLV coefficients, because all of companies respect to the diversified customers classification and complexity of consumers needs. Finally, we unfolded any company's scheduling added new methodology using simulation and leaded conclusion about the new methodology.

• 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.

• Korean Management Science Review
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• v.20 no.2
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• pp.135-149
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• 2003

Developing a proper program for customer evaluation is one of the most imminent tasks to implement CRM (Customer Relationship Management). Design of the Customer Value model is an important key to the customer evaluation progrgm. This paper proposes two models for estimating Customer Value. The first one is a Description Model for Customer Value based on customer CSI (Customer Satisfaction Index) data. This model represents as quantitative numbers what customers feel from the company or the service. The second one is a Prediction Model which employs factor analysis and regression to predict customer value. This paper exploits the two models to evaluate Customer Value as well as for customer behavior prediction.

• Asia Marketing Journal
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• v.10 no.4
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• pp.99-121
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• 2009

Social networks can be a powerful force in marketing because they provide new ways to market to young generation. Though many studies on evaluation of customer lifetime value have been conducted, it is not clear how to assess the value of members within social networks. The purpose of this study is to evaluate members based on customer intangible value as well as customer lifetime value. Customer network value in terms of the power and influence within a network is analyzed through network structure analysis. Using Cyworld log file data, this study have shown that high percentage of members are very influential in terms of spreading or withholding information even though their CLV is low. It is expected that the findings of this research contribute to understand the interactive behaviors of members within networks and to provide valuable implications on new product launching and customer management strategies to marketers.