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A Comparative Study of RFM-Based Clustering Methods in Customer Segmentation
Drug Addiction is one of the growing threats all
over the world. According to Dhaka Tribune, more than 7.5
million people are addicted to drugs in Bangladesh. There
are a lot of differences between a drug-addicted and a non�addicted person on health condition, social life, personal life, and
familial life behaviors. So, steps should be taken to prevent drug
addiction with proper curative issues. In this paper, we dig for the
influential factors behind drug addiction and possible solutions
to reduce the drug addiction rate. The research is held on the
people of Dhaka, Bangladesh. Most of the data of drug-addicted
people are collected from ‘Drug Rehab’ and for non-addicted
person data we have collected from different schools, colleges, and
universities in Dhaka, Bangladesh. All are male and the age group
of 17 to 45 years. Our primary data set is constructed including
only 188 qualitative data. A total of 5 algorithms have been
employed including Logistic Regression, Decision Tree, Random
Forest, Naive Bayes, Support Vector Machine (SVM) and their
results are compared. Among the algorithms Random Forest
comes up with the highest accuracy of 97.3484%, XGBoost &
Decision Tree Classifier delivers the accuracy of 96.2768% and
94.68%
In this paper, we base our research by dealing with a real-world problem in an enterprise. A RFM (recency, frequency, and monetary) model and K-means clustering algorithm are utilized to conduct customer segmentation and value analysis by using online sales data. Customers are classified into four groups based on their purchase behaviors. On this basis, different CRM (customer relationship management) strategies are brought forward to gain a high level of customer satisfaction. The effectiveness of our method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount.
A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.
Telecom Companies logs customer’s actions which generate a huge amount of data that can bring important findings related to customer’s behavior and needs. The main characteristics of such data are the large number of features and the high sparsity that impose challenges to the analytics steps. This paper aims to explore dimensionality reduction on a real telecom dataset and evaluate customers’ clustering in reduced and latent space, compared to original space in order to achieve better quality clustering results. The original dataset contains 220 features that belonging to 100,000 customers. However, dimensionality reduction is an important data preprocessing step in the data mining process specially with the presence of curse of dimensionality. In particular, the aim of data reduction techniques is to filter out irrelevant features and noisy data samples. To reduce the high dimensional data, we projected it down to a subspace using well known Principal Component Analysis (PCA) decomposition and a novel approach based on Autoencoder Neural Network, performing in this way dimensionality reduction of original data. Then K-Means Clustering is applied on both-original and reduced data set. Different internal measures were performed to evaluate clustering for different numbers of dimensions and then we evaluated how the reduction method impacts the clustering task.
Purpose
The purpose of this paper is to determine the best approach to customer segmentation and to extrapolate associated rules for this based on recency, frequency and monetary (RFM) considerations as well as demographic factors. In this study, the impacts of RFM and demographic attributes have been challenged in order to enrich factors that lend comprehension to customer segmentation. Different types of scenario were designed, performed and evaluated meticulously under uniform test conditions. The data for this study were extracted from the database of a global pizza restaurant chain in Turkey. This paper summarizes the findings of the study and also provides evidence of its empirical implications to improve the performance of customer segmentation as well as achieving extracted rule perfection via effective model factors and variations. Accordingly, marketing and service processes will work more effectively and efficiently for customers and society. The implication of this study is that it explains a clear concept for interaction between producers and consumers.
Design/methodology/approach
Customer relationship management, which aims to manage record and evaluate customer interactions, is generally regarded as a vital tool for companies that wish to be successful in the rapidly changing global market. The prediction of customer behaviors is a strategically important and difficult issue because of the high variance and wide range of customer orders and preferences. So to have an effective tool for extracting rules based on customer purchasing behavior, considering tangible and intangible criteria is highly important. To overcome the challenges imposed by the multifaceted nature of this problem, the authors utilized artificial intelligence methods, including k-means clustering, Apriori association rule mining (ARM) and neural networks. The main idea was that customer clusters are better enhanced when segmentation processes are based on RFM analysis accompanied by demographic data. Weighted RFM (WRFM) and unweighted RFM values/scores were applied with and without demographic factors and utilized to compose different types and numbers of clusters. The Apriori algorithm was used to extract rules of association. The performance analyses of scenarios have been conducted based on these extracted rules. The number of rules, elapsed time and prediction accuracy were used to evaluate the different scenarios. The results of evaluations were compared with the outputs of another available technique.
Findings
The results showed that having an appropriate segmentation approach is vital if there are to be strong association rules. Also, it has been determined from the results that the weights of RFM attributes affect rule association performance positively. Moreover, to capture more accurate customer segments, a combination of RFM and demographic attributes is recommended for clustering. The results’ analyses indicate the undeniable importance of demographic data merged with WRFM. Above all, this challenge introduced the best possible sequence of factors for an analysis of clustering and ARM based on RFM and demographic data.
Originality/value
The work compared k-means and Kohonen clustering methods in its segmentation phase to prove the superiority of adopted segmentation techniques. In addition, this study indicated that customer segments containing WRFM scores and demographic data in the same clusters brought about stronger and more accurate association rules for the understanding of customer behavior. These so-called achievements were compared with the results of classical approaches in order to support the credibility of the proposed methodology. Based on previous works, classical methods for customer segmentation have overlooked any combination of demographic data with WRFM during clustering before proceeding to their rule extraction stages.
Today, the ability to identify the profitable customers, creating a long-term loyalty in them and expanding the existing relationships are considered as the key and competitive factors for a customer-oriented organization. The prerequisite for having such competitive factors is the presence of a very powerful customer relationship management (CRM). The accurate evaluation of customers’ profitability is considered as one of the fundamental reasons that lead to a successful customer relationship management. RFM is a method that scrutinizes three properties, namely recency, frequency and monetary for each customer and scores customers based on these properties. In this paper, a method is introduced that obtains the behavioral traits of customers using the extended RFM approach and having the information related to the customers of an organization; it then classifies the customers using the K-means algorithm and finally scores the customers in terms of their loyalty in each cluster. In the suggested approach, first the customers’ records will be clustered and then the RFM model items will be specified through selecting the effective properties on the customers’ loyalty rate using the multipurpose genetic algorithm. Next, they will be scored in each cluster based on the effect that they have on the loyalty rate. The influence rate each property has on loyalty is calculated using the Spearman’s correlation coefficient.
Purpose-The goal of this study is to identify the main clusters of bank customers in order to help commercial banks to better identify their customers and design more efficient marketing strategies.Design/methodology/approach–Data from 250 bank customers were analyzed by using two-step scalable clustering.Findings-Five different clusters of bank customers were identified, namely, favorite customers, creditworthy customers, non-creditworthy customers, passers, and friends. The findings indicated that disparate clusters of bank customers are extremely different based on their loan amount, default risk, account balance, degree of loyalty and profitability for the bank.Practical implications-The differences which were observed between these five clusters of bank customers accentuate the importance of customer clustering and market segmentation in the financial services industry. Customer clustering can help financial institutions to augment their competitiveness by shifting from traditional marketing strategies to target marketing and segmentation-based marketing approaches.Originality/value-The most important contribution of this study is the incorporation of a wide range of factors that can potentially affect customer clustering in the analysis, whereas, the majority of previous studies only focused on a limited number of variables in order to determine the customer clusters. Specifically, the customer clustering in this study was performed by using demographic variables, profitability, loan amount, default risk, account balance, loyalty, account type, account closure history, customer location, and account currency.
This paper proposes a new method using clustering of item preference based on Recency, Frequency, Monetary (RFM) for recommendation system in u-commerce under fixed mobile convergence service environment which is required by real time accessibility and agility. In this paper, using an implicit method without onerous question and answer to the users, not used user’s profile for rating to reduce customers’ search effort, it is necessary for us to keep the scoring of RFM to be able to reflect the attributes of the item and clustering in order to improve the accuracy of recommendation with high purchasability. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.
A method for identifying clusters of points in a multidimensional Euclidean space is described and its application to taxonomy considered. It reconciles, in a sense, two different approaches to the investigation of the spatial relationships between the points, viz., the agglomerative and the divisive methods. A graph, the shortest dendrite of Florek etal. (1951a), is constructed on a nearest neighbour basis and then divided into clusters by applying the criterion of minimum within cluster sum of squares. This procedure ensures an effective reduction of the number of possible splits. The method may be applied to a dichotomous division, but is perfectly suitable also for a global division into any number of clusters. An informal indicator of the "best number" of clusters is suggested. It is a"variance ratio criterion" giving some insight into the structure of the points. The method is illustrated by three examples, one of which is original. The results obtained by the dendrite method are compared with those obtained by using the agglomerative method or Ward (1963) and the divisive method of Edwards and Cavalli-Sforza (1965).
Purpose
In the retailing sector, consumers typically patronize multiple outlets, which leaves outlets striving to earn a greater portion of consumer expenditures. The purpose of this paper is to improve theoretical and empirical knowledge about the impact of retailing loyalty programmes on customer purchasing behaviour.
Design/methodology/approach
The effects of two loyalty programmes on customer behaviour are studied through marketwide panel data on supermarket purchases.
Findings
The impact of loyalty programme membership on customer purchase behaviour is significant.
Research limitations/implications
All behavioural indicators show that members and non‐members of loyalty programmes demonstrate significantly different purchase behaviours, irrespective of other factors. The purchase intensity of cardholders, in terms of total and average shopping baskets, share of purchases, purchase frequency and inter‐purchase time, is significantly higher than that of non‐members throughout the entire three‐year period and the trading areas. The findings require confirmation in other retailing sectors before they may be considered fully generalisable.
Practical implications
Retailers may apply the findings in their attempts to segment their target market, which enables them to allocate their marketing expenditures more effectively.
Originality/value
The study contributes to more “generalisable” knowledge by investigating marketwide scanner panel data about competitive purchasing, loyalty programmes and store locations.
A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.
Because the cost of attracting new customers is much higher than the cost of retaining old customers, keeping customers loyal is a crucial issue for service firms. This research explores how relationship quality and switching barriers influence customer loyalty. Relationship quality consists of two aspects: satisfaction and trust. Antecedents of satisfaction and trust are explored. To test the proposed research model, a survey research methodology was used. Paper survey was distributed to mobile phone users in Taiwan. A total of 311 valid questionnaires were returned. Structural equation modeling was used to test hypotheses. Satisfaction, trust, and switching barriers have positive effects on loyalty. In terms of antecedents, it was found that playfulness and service quality impact satisfaction while service quality and intimacy affect trust. From a managerial perspective, this research suggests that practitioner should not only keep improving service quality, but also provide playfulness to ensure customer satisfaction. Service providers should also build relationship quality and switching barriers to reduce the possibility of defection and enhance customer loyalty.
A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.
Direct marketing has become more efficient in recent years because of the use of data-mining techniques that allow marketers to better segment their customer databases. RFM (recency, frequency, and monetary value) has been available for many years as an analytical technique. In recent years, more sophisticated methods have been developed; however, RFM continues to be used because of its simplicity. This study investigates RFM, CHAID, and logistic regression as analytical methods for direct marketing segmentation, using two different datasets. It is found that CHAID tends to be superior to RFM when the response rate to a mailing is low and the mailing would be to a relatively small portion of the database, however, RFM is an acceptable procedure in other circumstances. The present article addresses the broader issue that RFM may focus too much attention on transaction information and ignore individual difference information (e.g., values, motivations, lifestyles) that may help a firm to better market to their customers.
Machine learning methods are powerful tools for data mining with large noisy databases and give researchers the opportunity to gain new insights into consumer behavior and to improve the performance of marketing operations. To model consumer responses to direct marketing, this study proposes Bayesian networks learned by evolutionary programming. Using a large direct marketing data set, we tested the endogeneity bias in the recency, frequency, monetary value (RFM) variables using the control function approach; compared the results of Bayesian networks with those of neural networks, classification and regression tree (CART), and latent class regression; and applied a tenfold cross-validation. The results suggest that Bayesian networks have distinct advantages over the other methods in accuracy of prediction, transparency of procedures, interpretability of results, and explanatory insight. Our findings lend strong support to Bayesian networks as a robust tool for modeling consumer response and other marketing problems and for assisting management decision making.
Direct marketing firms want to transfer their message as efficiently as possible in order to obtain a profitable long-term relationship with individual customers. Much attention has been paid to address selection of existing customers and on identifying new profitable prospects. Less attention has been paid to the optimal frequency of the contacts with customers. We provide a decision support system that helps the direct mailer to determine mailing frequency for active customers. The system observes the mailing pattern of these customers in terms of the well known R(ecency), F(requency) and M(onetary) variables. The underlying model is based on an optimization model for the frequency of direct mailings. The system provides the direct mailer with tools to define preferred response behavior and advises the direct mailer on the mailing strategy that will steer the customers towards this preferred response behavior.
Loyalty of a firm’s customer has been recognised as the dominant factor in a business organization’s success. This study helps us extend our understanding of the relationship between customer loyalty, customer satisfaction, and image. This is of considerable interest to both practitioners and academics in the field of hospitality management. The objective of this research is to identify the factors of image and customer satisfaction that are positively related to customer loyalty in the hotel industry. Using data collected from chain hotels in New Zealand, the findings indicate that hotel image and customer satisfaction with the performance of housekeeping, reception, food and beverage, and price are positively correlated to customer loyalty.
For a ‘discrete choice experiment’ to gauge consumer preferences for alternative electric service plans, surveys were administered to over 1000 residences in 12 electricity markets. The resulting estimated choice function parameters provide insights into the importance consumers place on individual features. Several demographic effects were identified that associate preferences with customers, providing powerful and actionable market segmentation tools.
This study focuses on profiling profitable hotel customers by RFM analysis, which is a data mining technique. In RFM analysis, Recency, Frequency and Monetary indicators are employed for discovering the nature of the customers. In this study, the actual CRM data belong to three five-star hotels operating in Antalya, Turkey were used. Analysis results showed that 369 profitable hotel customers were divided into eight groups: ‘Loyal Customers’, ‘Loyal Summer Season Customers’, ‘Collective Buying Customers’, ‘Winter Season Customers’, ‘Lost Customers’, ‘High Potential Customers’, ‘New Customers’, and ‘Winter Season High Potential Customers’. Majority of the customers (36%) were positioned at ‘Lost Customers’ segment, who stay for shorter periods, spend less than other groups and tend to come to the hotels in the summer season. Results indicated that RFM effectively clusters the customers, which may lead hotel top managers to generate new strategies for increasing their abilities in CRM.
Mobile TelephonyThe Fixed Telephony CaseSummary
RFM analysis and market basket analysis (i.e., frequent pattern mining) are two most important tasks in database marketing. Based on customers’ historical purchasing behavior, RFM analysis can identify a valuable customer group, while market basket analysis can find interesting purchasing patterns. Previous studies reveal that recency, frequency and monetary (RFM) analysis and frequent pattern mining can be successfully integrated to discover valuable patterns, denoted as RFM-customer-patterns. However, since many retailers record transactions without collecting customer information, the RFM-customer-patterns cannot be discovered by existing approaches. Therefore, the aim of this study was to define the RFM-pattern and develop a novel algorithm to discover complete set of RFM-patterns that can approximate the set of RFM-customer-patterns without customer identification information. Instead of evaluating values of patterns from a customer point of view, this study directly measures pattern ratings by considering RFM features. An RFM-pattern is defined as a pattern that is not only occurs frequently, but involves a recent purchase and a higher percentage of revenue. This study also proposes a tree structure, called an RFM-pattern-tree, to compress and store entire transactional database, and develops a pattern growth-based algorithm, called RFMP-growth, to discover all the RFM-patterns in an RFM-pattern-tree. Experimental results show that the proposed approach is efficient and can effectively discover the greater part of RFM-customer-patterns.
Companies greatly benefit from knowing how problems with data quality influence the performance of segmentation techniques and which techniques are more robust to these problems than others. This study investigates the influence of problems with data accuracy – an important dimension of data quality – on three prominent segmentation techniques for direct marketing: RFM (recency, frequency, and monetary value) analysis, logistic regression, and decision trees. For two real-life direct marketing data sets analyzed, the results demonstrate that (1) under optimal data accuracy, decision trees are preferred over RFM analysis and logistic regression; (2) the introduction of data accuracy problems deteriorates the performance of all three segmentation techniques; and (3) as data becomes less accurate, decision trees retain superior to logistic regression and RFM analysis. Overall, this study recommends the use of decision trees in the context of customer segmentation for direct marketing, even under the suspicion of data accuracy problems.
This paper introduces the concept of the Customer Value Matrix, a customer segmentation approach that is especially well-suited for small retail and service businesses. The discussion offers insights into the reasons for the development of this practical approach, a concrete methodology for its implementation, and strategic and tactical applications of the concept. The material is supported with strong evidence from “real-world” examples featuring a variety of small retail and service businesses. The paper concludes with a discussion of the managerial implications for companies that manage chains of small retail or service businesses as to how they can take advantage of local relationship marketing.
Despite the prevalent use of loyalty programs, there is limited evidence on the long-term effects of such programs, and their effectiveness is not well established. The current research examines the long-term impact of a loyalty program on consumers' usage levels and their exclusive loyalty to the firm. Using longitudinal data from a convenience store franchise, the study shows that consumers who were heavy buyers at the beginning of a loyalty program were most likely to claim their qualified rewards, but the program did not prompt them to change their purchase behavior. In contrast, consumers whose initial patronage levels were low or moderate gradually purchased more and became more loyal to the firm. For light buyers, the loyalty program broadened their relationship with the firm into other business areas. The findings suggest a need to consider consumer idiosyncrasies when studying loyalty programs and illustrate consumers' cocreation of value in the marketing process.
With the advent of one-to-one marketing media, e.g. targeted direct mail or internet marketing, the opportunities to develop targeted marketing (customer relationship management) campaigns are enhanced in such a way that it is now both organizationally and economically feasible to profitably support a substantially larger number of marketing segments. However, the problem of what segments to distinguish, and what actions to take towards the different segments increases substantially in such an environment. A systematic analytic procedure optimizing both steps would be very welcome.In this study, we present a joint optimization approach addressing two issues: (1) the segmentation of customers into homogeneous groups of customers, (2) determining the optimal policy (i.e. what action to take from a set of available actions) towards each segment. We implement this joint optimization framework in a direct-mail setting for a charitable organization. Many previous studies in this area highlighted the importance of the following variables: R(ecency), F(requency), and M(onetary value). We use these variables to segment customers. In a second step, we determine which marketing policy is optimal using markov decision processes, following similar previous applications. The attractiveness of this stochastic dynamic programming procedure is based on the long-run maximization of expected average profit. Our contribution lies in the combination of both steps into one optimization framework to obtain an optimal allocation of marketing expenditures. Moreover, we control segment stability and policy performance by a bootstrap procedure. Our framework is illustrated by a real-life application. The results show that the proposed model outperforms a CHAID segmentation.
The more a marketing paradigm evolves, the more long-term relationship with customers gains its importance. CRM, a recent marketing paradigm, pursues long-term relationship with profitable customers. It can be a starting point of relationship management to understand and measure the true value of customers since marketing management as a whole is to be deployed toward the targeted customers and profitable customers, to foster customers' full profit potential. Corporate success depends on an organization's ability to build and maintain loyal and valued customer relationships. Therefore, it is essential to build refined strategies for customers based on their value.In this paper, we propose a framework for analyzing customer value and segmenting customers based on their value. After segmenting customers based on their value, strategies building according to customer segment will be illustrated through a case study on a wireless telecommunication company.
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 customer relationship management, it is important to gather information on customer value. Many researches have been performed to calculate customer value based on Customer lifetime value (LTV). It, however, has some limitations. It is difficult to consider the defection of customers. Prediction models have focused mainly on expected future cash flow derived from customers' past profit contribution.In this paper we suggest an LTV model considering past profit contribution, potential benefit, and defection 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 three types of customer value. A case study on calculating customer value and segmenting customers of a wireless communication company will be illustrated.
Most marketers have difficulty in identifying the right customers to engage in successful campaigns. So far, customer segmentation is a popular method that is used for selecting appropriate customers for a launch campaign. Unfortunately, the link between customer segmentation and marketing campaign is missing. Another problem is that database marketers generally use different models to conduct customer segmentation and customer targeting. This study presents a novel approach that combines customer targeting and customer segmentation for campaign strategies. This investigation identifies customer behavior using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers. Additionally, this work proposes using generic algorithm (GA) to select more appropriate customers for each campaign strategy. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a Nissan automobile retailer to segment over 4000 customers. The experimental results demonstrate that the proposed method can more effectively target valuable customers than random selection.
Identifying patients in a Target Customer Segment (TCS) is important to determine the demand for, and to appropriately allocate resources for, health care services. The purpose of this study is to propose a two-stage clustering-classification model through (1) initially integrating the RFM attribute and K-means algorithm for clustering the TCS patients and (2) then integrating the global discretization method and the rough set theory for classifying hospitalized departments and optimizing health care services. To assess the performance of the proposed model, a dataset was used from a representative hospital (termed Hospital-A) that was extracted from a database from an empirical study in Taiwan comprised of 183,947 samples that were characterized by 44 attributes during 2008. The proposed model was compared with three techniques, Decision Tree, Naive Bayes, and Multilayer Perceptron, and the empirical results showed significant promise of its accuracy. The generated knowledge-based rules provide useful information to maximize resource utilization and support the development of a strategy for decision-making in hospitals. From the findings, 75 patients in the TCS, three hospital departments, and specific diagnostic items were discovered in the data for Hospital-A. A potential determinant for gender differences was found, and the age attribute was not significant to the hospital departments.
Even though market segmentation is one of the most established concepts in marketing, there are still some shortfalls in the body of research, which create a gap between theory and practice and lead to failure in the implementation of segmentation. The concept of strategic segmentation is specified as key in resolving these issues. It is shown that in any form of strategic segmentation, the following two questions need to be answered consistently: What is the objective of performing market segmentation? Which unit of analysis will be selected for the segmentation? Based on empirical findings, a taxonomy of four market segmentation strategies is developed that addresses these shortfalls. The findings show that segmentation can be induced from the customer as well as from the market; but most importantly, there has to be consistency between the objective and the unit of analysis of a market segmentation. These findings provide both useful managerial implications as well as a framework for further research.
E-commerce companies understand that customers are their most important asset and that it is imperative to estimate the potential value of this asset. Hence, a model for calculating customer's value is essential in these domains. We describe a general modeling approach, based on Markov chain models, for calculating customer value in the e-commerce domain. This model extends existing models from the field of direct marketing, by taking into account a new set of variables required for evaluating customers value in an e-commerce environment. In addition, we present an algorithm for generating this model from historical data, as well as an application of this modeling approach for the creation of a model for online auctions. We also describe a case study that demonstrates how our model provides more accurate predictions than existing models regarding the future income generated by customers.
A summarization of customer segmentation methods
- Y.-Z Liu
- H Wu
Y.-z. Liu and H. Wu, "A summarization of customer segmentation methods," Journal of Industrial Engineering and Engineering Management,
vol. 1, pp. 53-57, 2006.