Title: Multi process prediction model for customer behaviour analysis

Authors: D. Kalaivani; T. Arunkumar

Addresses: Department of Computer Technology, Dr.SNS Rajalakshmi College of Arts and Science, Coimbatore, Tamilnadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

Abstract: Online purchase is one of the big changes to the retail marketing. As the lifestyle changed, the people are not going to shop for purchasing required items like gifts, accessories and any electronic items. Everyone started to use online and saving their time and money by getting a good offer through online shopping. Online shopping helps the customer to know the price of the item in advance and able to compare the price with different vendors. It helps the customer to buy the item from the vendor who offers the item with low-cost and good quality. The customer behaviour analysis always depends upon the usage of the internet and service provided by the multi vendor for the various products. Customer behaviour analysis is very much needed to help the vendors to define their strategy for online shopping, advertising, market segmentation and so on. The idea behind this work is to predict the customer behaviour based on their internet usage for various online shopping activities. Multi process prediction model is proposed to analyse customer behaviour using logistic regression method. The proposed model result is validated and compared with many existing online shopping customer models.

Keywords: online shopping; data mining; market segmentation; advertising; multivariate analysis; customer behaviour analysis; advertisement; retail marketing.

DOI: 10.1504/IJWBC.2018.090918

International Journal of Web Based Communities, 2018 Vol.14 No.1, pp.54 - 63

Received: 26 Sep 2016
Accepted: 19 Apr 2017

Published online: 16 Mar 2018 *

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