__STYLES__
Introduction:
In the fast-changing world of online shopping, I embarked on an exciting journey to explore the vast e-commerce dataset and unlock interesting insights with SQL as my best friend to work with, along with Tableau for visualization.
This project was about helping e-commerce thrive in the competitive world. It provides a deep insight into customer behaviour and loyalty via cohort analysis, and I was also able to find key trends that help in marketing and profit maximization.
About the dataset:
The dataset contains the customer IDs, transaction and product IDs, the product lines, the transactions made and the date, the order status, available brands, the list price and the standard cost along with the first date of the product sold. There were 13 fields and 20000 records.
Tools Used:
SQL (Data Cleaning and Analysis) - Code here!
Tableau (Data Visualization)
Steps followed:
The overall exploration was divided into 3 key phases.
Phase 1: Data Cleaning
I cleaned up the data to fix errors and to ensure it was ready for the next step of analysis.
Phase 2: Cohort Analysis
This was performed to study a group of customers based on their shopping habits and timelines. This revealed amazing insights about customer loyalty and their behaviour, like deciphering a hidden code.
Phase 3: Insights
I used SQL to find the answers to some important questions, which will provide the key trends and help in decision-making on various factors.
Key Data Insights:
Actionable Insights:
Data Visualization:
A dashboard was created in Tableau to showcase the key trends.
Conclusion:
This project was a learning experience and helped me with building up my skills to work with SQL to gain insights and Tableau to visualize. It also helped me learn to analyze the e-commerce data effectively and to perform meaningful cohort analysis.