E-Commerce Revenue Management

Course: Python for Data Science

Project Overview

This project analyzes the data of an E-Commerce company to identify sales volumes, product categories, revenues, and brands using the portal. The analysis includes exploratory data analysis (EDA) to answer specific business questions.

Skills & Tools Covered

• NumPy
• Pandas
• Data Visualization (Matplotlib/Seaborn)
• User Defined Functions

Problem Statement

As a business analyst, you are tasked with preparing a report analyzing different aspects of the E-Commerce company. The key questions to explore include:

• Which region has the maximum sales volume?
• Which type of products are sold the most?
• How much revenue does the company generate?
• What brands are available on the platform?

Dataset

The dataset used for this analysis is ecommerce_data.csv, which contains details about orders, products, customer regions, and sales metrics. The data structure includes information on:

• Order IDs and Dates
• Product Categories and Names
• Sales and Revenue Values
• Region and Brand Information

Key Findings

• Unique Brands: There are 2,484 unique brands in the dataset.
• Product Categories: The maximum orders were placed for Clothing, while the minimum orders were placed for Wearable Smart Devices.
• Total Revenue: The total revenue generated by the E-Commerce company across all orders is 2,217,486.85 rupees.
• Regional Performance:
  • North region outperformed in December 2020 but generated the least revenue in July 2021.
  • East region outperformed in December 2020 but generated the least revenue in October 2021.
  • West region outperformed in December 2020 but generated the least revenue in October 2021.
  • South region outperformed in December 2020 but generated the least revenue in May 2021.

Reflection

Working on this project has deepened my understanding of data analysis in the context of an E-Commerce company. Key takeaways include:

• Data Familiarization: Gaining hands-on experience with data manipulation and exploration.
• Data Cleaning: Learning the importance of cleaning data for accurate analysis.
• Visualization Skills: Enhancing skills in data visualization to communicate insights effectively.
• Business Insights: Developing the ability to derive actionable insights from data analysis.

This project has solidified my skills in using Python for data analysis and visualization, equipping me with practical experience for future roles as a data analyst.