Business Intelligence for the Evaluation of Customer Satisfaction in E-Commerce Websites- A Case Study

Main Article Content

Priyadarshini P.
K. T. Veeramanju


Background/Purpose: Advancement of technology has been proven in every field. To establish a good business or any organization is very crucial nowadays. Businesses are moving tremendously and competitors also high in number. It is very important to take a good decision based on reviews and feedback of customer, etc. By combining the strength of technology and business knowledge, Business Intelligence allows you to make good decisions that are fully informed and stay one step ahead of the competition.  With the help of business intelligence tools businesses can use their data in a better way. Business intelligence incorporates Data mining, Data Analytic, Data Visualization and Machine learning to help organization for the analysis of data. This article provides the analysis of Business intelligence and machine learning techniques used in e-commerce website and ABCD framework to inspect the key factors. 

Objective: Analyses the business intelligence technology and sentiment analysis on E-commerce website.

Design/Methodology/Approach: The information and details for this case study is obtained from different scholarly articles published in various journals and company websites.

Findings/Result: The study of this paper delivers the importance of customer behavior and how it helps in growth of the industry using machine learning and business intelligence.

Originality/Value/Novelty: The result of this paper gives an explanation of business intelligence and machine learning approach for customer feedback in e-commerce companies and advantages of e-commerce websites.

Paper Type: Case study paper to study the advantages of business intelligence and machine learning for customer satisfaction.


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How to Cite
Priyadarshini P., & K. T. Veeramanju. (2022). Business Intelligence for the Evaluation of Customer Satisfaction in E-Commerce Websites- A Case Study. International Journal of Management, Technology and Social Sciences (IJMTS), 7(2), 660–668.