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Opportunity Finder & Keyword Trend Analysis in E-commerce
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
E-Commerce has become increasingly popular in recent years and is now an essential part of daily life for many people. It offers customers convenience, the ability to compare prices, and the option to shop without physically travelling to stores. Consequently, for businesses and sellers, the need for a reliable method to organize the customer, group the ones with similar characteristics to satisfy their demands, and form new business strategies accordingly is much needed. Using content-based filtering for shortlisting products helps in the market analysis of a particular. Based on that analysis and the trends observed, the customers can be segmented in two ways: manually using RFM analysis or using K-means clustering, a machine learning algorithm. Segmenting customers helps to understand them better and increases a company's revenue. It is a valuable tool for businesses to understand their customer base better and tailor their marketing and product development strategies. (Eg. R. Punhani, et al. 2021)
Keywords
E-commerce, Cluster analysis, Customer Segmentation, RFM analysis, K-means algorithm, business decisions.
How to cite this paper
@article{1704152,
author = {Nikhil Jha, Kanishk Bhadauria, Aparna Jha},
title = {Opportunity Finder & Keyword Trend Analysis in E-commerce},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {9},
pages = {186-199},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704152.pdf},
abstract = {E-Commerce has become increasingly popular in recent years and is now an essential part of daily life for many people. It offers customers convenience, the ability to compare prices, and the option to shop without physically travelling to stores. Consequently, for businesses and sellers, the need for a reliable method to organize the customer, group the ones with similar characteristics to satisfy their demands, and form new business strategies accordingly is much needed. Using content-based filtering for shortlisting products helps in the market analysis of a particular. Based on that analysis and the trends observed, the customers can be segmented in two ways: manually using RFM analysis or using K-means clustering, a machine learning algorithm. Segmenting customers helps to understand them better and increases a company's revenue. It is a valuable tool for businesses to understand their customer base better and tailor their marketing and product development strategies. (Eg. R. Punhani, et al. 2021)},
keywords = {E-commerce, Cluster analysis, Customer Segmentation, RFM analysis, K-means algorithm, business decisions.},
month = {March},
}