E-COMMERCE DATA ANALYSIS

-COMMERCE DATA ANALYSIS

MIRIYALA AJAY KUMAR , GOKARAJU VINOD GOPI , CHATAPARTHI CHARAN GANESH

VARMA , YAMA MOHAN REDDY , Mrs.V.Ragavarthini

 1,2,3,4 UG Student, 6Associate Professor

Department of CSE

KALASALINGAM ACDAEMY OF RESEARCH AND EDUCATION

Krishnankoil, Virudhunagar, Tamilnadu-626126

9920004789@klu.ac.in, 9920004805@klu.ac.in, 9920004803@klu.ac.in, 9920004551@klu.ac.in,

5

 V.Raghavarthini@klu.ac.in

ABSTRACT:

In an increasingly digital and data-driven business landscape, e-commerce has become a critical domain for companies seeking to understand customer behaviour, enhance operational efficiency, and boost profitability. This project embarks on an exploration of e-commerce data analysis, focusing on its application in business optimization.

In today's digital landscape, e-commerce platforms have revolutionized the way businesses operate and consumers shop. With the exponential growth of online transactions, the need to understand consumer behaviour and optimize business strategies has become paramount. This project aims to delve into the realm of e-commerce data analysis, leveraging diverse datasets to extract meaningful insights and drive informed decision-making. The study commences with data collection from multiple sources, including user interactions, purchase histories, demographics, and website traffic. Various analytical techniques such as descriptive analytics, predictive modelling, and machine learning algorithms are employed to extract valuable patterns, trends, and correlations from the amassed data. The findings from this analysis are crucial for ecommerce businesses to streamline operations, enhance customer satisfaction, and drive revenue growth. The project aims to contribute actionable insights that can be utilized by e-commerce stakeholders to make data driven decisions and stay ahead in a highly competitive market.

The objectives of this study are to investigate and synthesize the existing literature on various facets of e-commerce data analysis, encompassing frameworks, data sources, metrics, customer behaviour analysis, personalization, recommendation systems, fraud detection, security measures, market basket analysis, cross-selling strategies, supply chain management, inventory control, user experience enhancement, A/B testing, and ethical considerations. By assessing these key areas, this project seeks to provide a comprehensive understanding of how data analysis techniques can be harnessed to improve e-commerce businesses' decision-making, customer satisfaction, and security.

With the exponential growth of e-commerce platforms in recent years, there is a wealth of data available that offers valuable insights into consumer behaviour, market trends, and business performance. This study aims to conduct a thorough analysis of e-commerce data to uncover meaningful patterns and trends, providing businesses with actionable intelligence to enhance decision-making processes.

The research methodology combines descriptive statistics, machine learning