Data Analytics for Consumer Purchasing Pattern with Behavioral Segmentation
Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018 held at Malaviya National Institute of Technology, Jaipur (India) on March 26-27, 2018
6 Pages Posted: 7 May 2018
Date Written: May 3, 2018
Abstract
Customer Segmentation plays an important role for the marketing of products and services in any industry. Target specific marketing and product offerings to specific customers on the basis of their purchasing behavior helps to increase profitability by increasing the response rate of the customers. This idea of market segmentation is implemented in the project with the help of algorithms that are used in Big Data analytics and other similar domains. The project aims to find segments of customers in a grocery store who buy different types of products including FMCG, domestic products and other products used in homes for daily purposes. The segmentation of the customers is mainly performed with the help of K-means clustering algorithm. The data set of the grocery store is composed of different csv files which contain different data including the orders, the products and the departments. The data set has different features which are used to find parameters on the basis of which clustering is performed. The order csv file contains the departments of the product which is then turned into an adjacency matrix on which PCA is used for dimensionality reduction.
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