Customer Analytics Using K-Means Clustering And ElbowModelling With Product Associative Analysis Using Unsupervised Machine Learning

Document Type : Primary Research paper

Authors

1 Sr.Asst.Professor, Department of Electronics and communication Engineering, Lakireddy Bali Reddy College of Engineering, AP, India, 521230

2 UG Students, Department of Electronics and communication Engineering, , Lakireddy Bali Reddy College of Engineering, AP, India, 521230.

Abstract

The feel of contemporary era is innovation, where everyone is involved into competition to be higher than others. Today’s business run on the premise of such innovation having ability to enrapture the purchasers with the merchandise, however with such an oversized raft of merchandise leave the purchasers mazed, what to shop for and what to not and conjointly the businesses are puzzled regarding what section of shoppers to focus on to sell their products. This is often wherever machine learning comes into play, varied algorithms are applied for unravelling the hidden patterns within the knowledge for higher deciding for the long run. This elude idea of that phase to target is created unequivocal by applying segmentation. The process of segmenting the purchasers with similar behaviors into an equivalent phase and with totally different patterns into totally different segments and analyzing their purchasing patterns can be treated as customer analytics. Customer segmentation is carried out based on the RFM value. With RFM a firm can divide its customers into three segments such low, mid, high with subsequent implication of elbow modeling, and k-means clustering clubbed which product associative analysis to track the combination of products that the customers buy frequently.

Keywords

Int. J. of Aquatic Science
Volume 12, Issue 3 - Serial Number 3
ICMMNT-2021 International Virtual Conference on Materials, Manufacturing and Nanotechnology, 30th June, 2021.
June 2021
Pages 156-164

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