Project Report: E-Commerce Sales Performance Analysis and Dashboarding
1. Executive Summary
This project analyzed 4,000+ rows of e-commerce sales data to identify key growth drivers, regional performance, and product trends. Using Python, SQL, and Tableau, I transformed raw data into an interactive dashboard to help businesses make data-driven decisions.
2. Problem Statement (Bayer’s Problem)
The client faced challenges in understanding their sales patterns due to the vast volume of unstructured data. Specifically:
Lack of visibility: They could not identify which regions of Bangladesh (e.g., Dhaka, Sylhet) were performing the best.
Product performance: They were not sure which products (e.g., sneakers or laptops) were contributing the most to their revenue.
Timing problem: They did not know the specific month or season when sales peaked.
3. Client Requirements (What the buyer wanted)
The client requested a comprehensive solution that included:
Data Processing: Creating and cleaning 4,000+ transaction records.
Database Management: Storing data in a structured SQL environment.
Actionable Insights: Identifying the top 5 products and underperforming regions.
Visual Dashboard: An interactive Tableau dashboard for real-time monitoring.
4. Process (How I completed the project)
Step 1: Data Creation and Cleaning (Python)
Used Python (Pandas and NumPy) to create 4,000 rows of realistic sales data, including order ID, date, region, and product category.
Calculated total sales by multiplying price and quantity.
Step 2: Database Management (MySQL)
A database named eCommerce_Project was created in MySQL Workbench.The
CSV file was imported using the Table Data Import Wizard.
SQL queries were run to verify data integrity and calculate total revenue.
Step 3: Data Visualization (Tableau)
The processed data was connected to Tableau Public.
Three main visualizations were created:
Map/Dot Plot: Visualizing regional sales distribution.
Bar Chart: Ranking products by revenue.
Line Chart: Tracking sales trends over 12 months.
5. Final Deliverable (what I delivered to the buyer)
Clean Dataset: A CSV file with 4,000 optimized records.
SQL Script: A set of queries for deep-dive analysis.
Interactive Tableau Dashboard: A visual representation of all KPIs (Key Performance Indicators).
Insights Report: A summary of the results (e.g., identifying July and December as the highest sales months).
6. Key insights found
Top products: Sneakers and laptops are the highest revenue generators.
Peak season: Sales show a significant upward trend during the middle of the year (July) and the end of the year (December).
Regional dominance: High sales concentration is observed in the Dhaka and Chittagong regions.
