Clickstream data mining and market segmentation

https://doi.org/10.31295/ijecs.v1n1.22

Authors

  • Jay Gandhi Huntington Park, California, United States
  • Kushal Dalal Huntington Park, California, United States
  • Hitesh Ravani Huntington Park, California, United States

Keywords:

K-means, Clustering, Clickstream, Association rules, Customer profiles

Abstract

Clickstream data is one of the most important sources of information in websites usage and customers' behavior in Banks e-services. A number of web usage mining scenarios are possible depending on the available information. While simple traffic analysis based on clickstream data may easily be performed to improve the e-banks services. The banks need data mining techniques to substantially improve Banks e-services activities. The relationships between data mining techniques and the Web usage mining are studied. Web structure mining has three types of these types are web usage structure, mining data streams, and web content. The integration between the Web usage mining and data mining techniques are presented for processes at different stages, including the pattern discovery phases, and introduces banks cases, that have analytical mining technique. A general framework for fully integrating domain Web usage mining and data mining techniques are represented for processes at different stages. Data Mining techniques can be very helpful to the banks for better performance, acquiring new customers, fraud detection in real time, providing segment based products, and analysis of the customers purchase patterns over time. And in Market Segmentation 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. 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.

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Published

2018-05-17

How to Cite

Gandhi, J., Dalal, K., & Ravani, H. (2018). Clickstream data mining and market segmentation. International Journal of Engineering and Computer Science, 1(1), 41-45. https://doi.org/10.31295/ijecs.v1n1.22