Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

A Multi-objective Exploratory Procedure for Regression Model Selection

Variable selection is recognized as one of the most critical steps in statistical modeling. The problems encountered in engineering and social sciences are commonly characterized by over-abundance of explanatory variables, non-linearities and unknown interdependencies between the regressors. An added difficulty is that the analysts may have little or no prior knowledge on the relative importance of the variables. To provide a robust method for model selection, this paper introduces the Multi-objective Genetic Algorithm for Variable Selection (MOGA-VS) that provides the user with an optimal set of regression models for a given data-set. The algorithm considers the regression problem as a two objective task, and explores the Pareto-optimal (best subset) models by preferring those models over the other which have less number of regression coefficients and better goodness of fit. The model exploration can be performed based on in-sample or generalization error minimization. The model selection is proposed to be performed in two steps. First, we generate the frontier of Pareto-optimal regression models by eliminating the dominated models without any user intervention. Second, a decision making process is executed which allows the user to choose the most preferred model using visualisations and simple metrics. The method has been evaluated on a recently published real dataset on Communities and Crime within United States.Comment: in Journal of Computational and Graphical Statistics, Vol. 24, Iss. 1, 201

Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing

The larger Bank‘s electronic data customer provides difficulty a marketing campaign. An efficient marketing campaign is needed to promote a product and services. The predictive data mining techniques use to help a marketing analyst provide more value to their customers by the right offer because of decreasing in responses to a direct marketing campaign. Distribution of customer data record in marketing response data are often found issue of imbalanced dataset. This study proposed hybrid Neural Network (NN) methods in data mining to support direct marketing analysis and forecast. Backpropagation NN is supervised learning methods that analyze data and recognize to solve many problems in the real world by building a model that is trained to perform well in some non-linear problems. K-means algorithm grouping process by minimizing the distance between the data and designed can handle very large dataset also continuous and categorical variable for handling imbalanced dataset. This research concerns on binary classification which is classified into two classes. Those classes are yes and no. The data was collected from the Machine Learning Repository Dataset in the University of California Irvine (UCI).This experiment compares hybrid K-Means + NN with basic NN. The result shows the improvement of accuracy from 91.53% to 91.59%, recall 22.15% to 27.7% and F-Measure 44.23% but not to precision from 61.69% to 60.75%