 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: March 2026
Volume 11 | Issue 3
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21504
Article Published : 8513
Total Authors : 22399
Total Reviewer : 805
Total Countries : 158
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | A Review of various Machine Learning Techniques for Customer Segmentation in Ecommerce |
| Authors Name: | Priyanka V Galagali , Dr.Jalaja G , Raghavendra CK |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_182606
|
| Published Paper Id: | IJRTI2206308 |
| Published In: | Volume 7 Issue 6, June-2022 |
| DOI: | |
| Abstract: | The Ecommerce field has been a rapidly emerging technology and has been playing an important role in digital life. Customer segmentation mainly refers to the division of customers into certain groups and categories . This involves dividing the customers who visit ecommerce website depending on various characteristics or traits of customers into various or multiple variety of subdivisions of communities according to the characteristics of customers based on their shopping behavior or their online habits. In the previous studies, the many algorithms have been used for clustering and classification of data and the problem of segmenting of customers. what so ever it is very hard in these times in division for segmenting customers clearly with clarity while facing a real-time customer data that have the habits and lifestyles of the customers that purchased activities and much more. This paper mainly focuses on the different types of clustering algorithms are the different types of techniques which have been used in creating an algorithm for customer segmentation. by comparing all the techniques which have been used in the e-commerce field to analyze the real-time data of the customers that shopping habits have been taken into consideration to segment customers and to increase or maximize the profits of the company. Finally, the customer segmentation can be derived from the results of clustering result. types of experiments have been conducted for real-time data of the customers who have been shopping in varied e-commerce websites and the result taken an output by various methods is in accordant with the customer segmentation. The robustness and efficiency of every algorithm is discussed along with the limitations of the algorithms. |
| Keywords: | Ecommerce, RFMT, Customer Segmentation, Customer Analysis, k-Means algorithm ,Clustering methods,Online Retal,E-commercerce system, Mean shift algorithm, Agglomerative algorithm, Machine learning, Python, customer Relationship Management, Customer Segmentation, Data Mining, RFM. |
| Cite Article: | "A Review of various Machine Learning Techniques for Customer Segmentation in Ecommerce", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 6, page no.2055 - 2060, June-2022, Available :http://www.ijrti.org/papers/IJRTI2206308.pdf |
| Downloads: | 000205205 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2206308
Registration ID:182606
Published In: Volume 7 Issue 6, June-2022
DOI (Digital Object Identifier):
Page No: 2055 - 2060 Country: Bangalore, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2206308 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2206308 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||