Data Mining in Market Segmentation: A Literature Review and Suggestions

Abstract

The importance of data mining techniques for market segmentation is becoming indispensable in the field of marketing research. This is the first identified academic literature review of the available data mining techniques related to market segmentation. This research paper provides surveys of the available literature on data mining techniques in market segmentation. A categorization has been provided based on the available data mining techniques used in market segmentation. Eight online journal databases were used for searching, and finally, 103 articles were selected and categorized into 13 groups based on data mining techniques. The utility of data mining techniques and suggestions are also discussed. The findings of this study show that neural networks is the most used method, and kernel-based method is the most promising data mining techniques. Our research work provides a comprehensive understanding of past, present as well as future research trend on data mining techniques in market segmentation. We hope this paper provides reasonable insight and clear understating to both industry as well as academic researchers.

Customer profiling, segmentation, and sales prediction using AI in direct marketing

“…This modern approach to market research involves processing vast datasets from databases using intelligent solutions, such as neural networks, evolutionary algorithms (EA), fuzzy theory, RFM, hierarchical clustering, K-means, bagged clustering, kernel methods, Taguchi method, multidimensional scaling, model-based clustering, and rough sets, among others. These techniques offer highly effective and time-efficient means of segmenting the market [42].…”

A novel model for product bundling and direct marketing in e-commerce based on market segmentation

“…Hsu et al (2012) used the idea of the hierarchy of the items consumed to segment customers. Dutta et al (2015) categorized the data mining techniques employed in market segmentation into thirteen methods, such as neural network, RFM analysis, hierarchical clustering, and K-means.…”

A customer segmentation framework for targeted marketing in telecommunication

“…Various data mining techniques such as Decision Tree classification rules [1], Bayesian modeling [1], RFM analysis [13], K-Means clustering, K-medoids, Fuzzy Clustering [4], Hierarchical clustering, Self-Organization Map (SOM) [4,14], Logistic regression, Support Vector Machine and metaheuristics methods have focused on segmenting customer in an efficient way [15]. For example, Dutta [16] grouped different data mining methods which adopted in market segmentation field of study, such as RFM, K-means, hierarchical clustering. A great number of researchers considered RFM variables for customer segmentation [17][18][19], and loyalty based segmentation [14].…”