A Dominance-based Rough Set Approach to customer behavior in the airline market (original) (raw)
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A novel decision rules approach for customer relationship management of the airline market
Expert Systems with Applications, 2009
Customer churn means the loss of existing customers to a competitor. Accurately predicting customer behavior may help firms to minimize this loss by proactively building a lasting relationship with their customers. In this paper, the application of the factor analysis and the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) in the customer relationship management (CRM) of the airline market is introduced. A set of ''if. . .then. . ." decision rules are used as the preference model to classify customers by a set of criteria and regular attributes. The proposed method can determine the competitive position of an airline by understanding the behavior of its customers based on their perception of choice, and so develop the appropriate marketing strategies. A large sample of customers from an international airline is used to derive a set of rules and to evaluate its prediction ability.
Customer Behavior Analysis Using Rough Set Approach
Journal of theoretical and applied electronic commerce research, 2013
The customer relationship management (CRM) is a business methodology used to build long term profitable customers by analyzing customer needs and behaviors. The customer behavior is analyzed by choosing important attributes in the customer database. The customers are then segmented into groups according to their attribute values. The rules are generated using rule induction algorithms to describe the customers in each group. These rules can be used by the entrepreneur to predict the behavior of their new customers and to vary the attraction process for existing customers. In this paper a new rule algorithm has been proposed based on the concepts of rough set theory. Its performance has been compared with LEM2 (Learning from Examples Module, version 2) algorithm, an existing rough set based rule induction algorithm. Real data set of the customer transaction is used for analysis. Recency(R), Frequency (F), Monetary (M) and Payment (P) are the attributes chosen for analyzing customer data. The proposed algorithm on average achieves 0.439% increase in sensitivity, 0.007% increase in specificity, 0.151% increase in accuracy, 0.014% increase in positive predictive value, 0.218% increase in negative predictive value and 0.228% increase in F-measure when compared to LEM2 algorithm.
EFFECTIVE CUSTOMER CLASSIFICATION USING ROUGH SET THEORY
IAEME PUBLICATION, 2020
Customers are the centermost part of making marketing strategies. Customer classification is a matter of enormous interest among the strategy makers for the large scale publicity and marketing of the products. In the present work, we give the data-driven model for customer classification for decision making. The discretization of continuous attributes is used to minimize the non-linearity and the noise in the classification. We find the attributes preserving full and more specific information of the customers using the rough set. The prediction accuracy is done using a support vector machine. We obtained an accuracy of 75.08% using the selected features, which is greater than the 72.33% of the complete attribute set. The model is useful for the classification of the customers in decision making in any marketing strategy
A ROUGH SET APPROACH FOR CUSTOMER SEGMENTATION
Customer segmentation is a process that divides a business's total customers into groups according to their diversity of purchasing behavior and characteristics. The data mining clustering technique can be used to accomplish this customer segmentation. This technique clusters the customers in such a way that the customers in one group behave similarly when compared to the customers in other groups. The customer related data are categorical in nature. However, the clustering algorithms for categorical data are few and are unable to handle uncertainty. Rough set theory (RST) is a mathematical approach that handles uncertainty and is capable of discovering knowledge from a database. This paper proposes a new clustering technique called MADO (Minimum Average Dissimilarity between Objects) for categorical data based on elements of RST. The proposed algorithm is compared with other RST based clustering algorithms, such as MMR (Min-Min Roughness), MMeR (Min Mean Roughness), SDR (Standard Deviation Roughness), SSDR (Standard deviation of Standard Deviation Roughness), and MADE (Maximal Attributes DEpendency). The results show that for the real customer data considered, the MADO algorithm achieves clusters with higher cohesion, lower coupling, and less computational complexity when compared to the above mentioned algorithms. The proposed algorithm has also been tested on a synthetic data set to prove that it is also suitable for high dimensional data.
Customer churn classification in telecommunication company using rough set theory
2016
Churn is perceived as the behaviour of a customer to leave or to terminate a service. This behaviour causes the loss of profit to companies because acquiring new customer incurred high investment for advertisements and promotions compared to retaining existing ones. Thus, it is necessary to consider an efficient classification model to reduce the rate of churn. In the traditional approach of classification modelling, it do not produce straightforward result interpretation. Therefore, identifying the best classification model to reduce the rate of churn is indeed a challenging task. The main objective of this thesis is to propose a new classification model based on the Rough Set Theory to classify customer churn. This research utilized the Knowledge Discovery in Database (KDD) process involving data pre-processing, data discretization, attribute reduction, rule generation, classification process, as well as data analysis, using the Rough Set toolkit. The Rough Set theory elements con...
Using decision rules to achieve mass customization of airline services
European Journal of Operational Research, 2010
This paper uses the Dominance-based Rough Set Approach (DRSA) to formulate airline service strategies by generating decision rules that model passenger preference for airline service quality. DRSA could help airlines eliminate some services associated with dispensable attributes without affecting passenger perception of service quality. DRSA could also help airlines achieve mass customization of airline services and generate additional revenues by active or passive targeting of quality services to passengers.
Dominance-based rough set approach in business intelligence
Business intelligence (BI) technologies provide historical, current, and predictive views of business operations. Data mining is the core BI. This study uses data mining techniques to analyses historical data of banking system. These techniques including K-means method, fuzzy c-means clustering method, self-organizing map and expected maximization clustering algorithm are used to choose the best clustering algorithm to segment customers into groups. Then the Dominance-Based Rough Set Approach is applied to provide a set of rules to classify customer in bank system. The induced rules can provide recommendations of behaviors that increase the risk in financial processes.
African Journal of Business Management, 2012
The databases of real world contain a huge volume of information; however, part of these data is not interesting for the knowledge extraction. So, data are preprocessed for reducing the amount of information and selecting more relevant attributes. This paper addresses a contrastive study between rough sets and principal components analysis on customer database attributes selection. The experiments were carried out using the insurance company database to evaluate a k-means clustering. The objective is to investigate the capacity of these techniques to improve the identification of customers segments in databases, what appears as an important tool to increase the effectiveness of business communication.
Attribute Selection in Marketing: A Rough Set Approach
Iimb Management Review, 2010
Using an illustrative case study on the Indian cosmetic industry, this paper illustrates the advantages of the rough set approach over conventional techniques for the extraction of decision rules from data sets, which can be useful in various marketing applications. The rule generated through the methodology can act as an 'expert', which may be referred to in future strategic decision-making. The approach gives results similar to the results obtained through statistical methods but without making any assumption. IIMB a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i i m b IIMB Management Review (2010) 22, 16e24