Abstract

The global economy is improving every year and during the forthcoming decades, marketers need to enter new national markets towards an understanding of how data mining techniques influences consumer behavior, which will be vital for consumer researches. The comprehension of available data mining methods to the presence of outlying measurements in the observed data is discussed as a major drawback of existing data mining methods. The psychological and social processes involved in consumer behaviour forms the subject matter of this study. The objective in accordance with an optimistic approach in terms of studying cause and effect in consumer behaviour will be combined with interpretative prominence on trying to understand the emotional, non-rational aspects of the process. The scope of this paper is to: (1) provide knowledge discovery in consumer behavior, (2) provide experience in the application of K-means data mining techniques in consumer behavior concepts to marketing management decisions. The methodology involves through systematic sampling method and prepared questionnaire which helps to discover knowledge from consumer behaviour predominantly through data mining for the extraction of hidden predictive information from large databases organizations can recognize valuable customers, predict future behaviors, and enable firms to make practical, knowledge-driven decisions. The study will be based on market segmentation wherein, retailers will realize they could no longer sell whatever they bought but had to begin competing for their businesses. This paper proposes k means clustering methods and dendograms suitable for the analysis of data in management applications.

Key words: Data mining, knowledge discovery, segmentation, consumer behaviour, marketing.