Implementation of customer segmentation using machine learning

In today’s data driven world, competitive business tends to collect more information of their customer to know about their preferences, choices, behaviours etc. To survive among competitors and grow exponentially they provide customer with customized ads and services, based on the data they have of...

Full description

Saved in:
Bibliographic Details
Main Authors: Kumari, Sulekha, Jha, Pooja
Format: Conference Proceeding
Language:English
Subjects:
Business competition
Cluster analysis
Clustering
Customer satisfaction
Customer services
Machine learning
Market segmentation
Marketing
Segmentation
Vector quantization
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In today’s data driven world, competitive business tends to collect more information of their customer to know about their preferences, choices, behaviours etc. To survive among competitors and grow exponentially they provide customer with customized ads and services, based on the data they have of their customers. Collection, pre-processing and segmentation of data in traditional way is bit hassle to handle as data are in enormous amount. To tackle this problem, machine learning techniques are being utilized. Businesses can optimise operations and strategies for marketing strategies through customer segmentation using RFM analysis and K-means clustering. Businesses can categorise customers effectively by using K-means clustering and RFM to analyse the recency, frequency, and monetary value of each customer. By using this strategy, businesses can better understand customer behaviour, personalise marketing campaigns, improve product recommendations, and raise customer satisfaction. The study shows a useful application to assist companies run more efficiently in today’s cutthroat business environment.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0215409