Customer Segmentation Based on E-Commerce using K-Mean Clustering

Harshita Bhimarapu · Sakshi Bhelkar · Nekita A. Chavhan · Chetan Dhule · Rahul Agrawal

7 citations0/0 files storedInternational Conference on Recent Advances in Science and Engineering Technology (ICRASET)

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Customer churn and segmentation Advanced Clustering Algorithms Research Data Mining and Machine Learning Applications

Abstract

Due to the increasing growth of customers in market, there is fierce competition in business. Relationships between businesses and customer will be strengthened by understanding customer needs. The importance of giving users a comprehensive experience increases as a result of the digital market's rapid growth. The dataset from an ecommerce website is used in this study to identify all the factors for analysis, including date, customer id, and product; with the help of k-Mean clustering and decision tree. This research work will determine the most effective technique for Customer Segmentation. This facilitates the identification and resolution of distinct requirements and preferences among diverse customer segments. Customer Segmentation is a multifaceted strategy that can positively impact various aspects businesses, from revenue and profitability to customer satisfaction and long-term growth.

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2019RFM model for customer purchase behavior using K-Means algorithm 2020A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA 2020The practical approach in Customers segmentation by using the K-Means Algorithm 2018Segmenting Retail Customers with an Enhanced RFM and a Hybrid Regression/Clustering Method 2021Development of Employee Performance Management System Using Web Based Application 2021Customer Segmentation Techniques on E-Commerce 2017DATA MINING ON CUSTOMER SEGMENTATION: A REVIEW

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