E-Commerce Analysis

E-commerce has become an essential part of modern business operations, providing a platform for businesses to sell their products and services to customers worldwide. The growth of E-commerce has been fueled by the increasing number of internet users and the convenience it offers. E-commerce data analysis has become an essential tool for businesses to gain insights into customer behavior and preferences, identify trends, and optimize their operations.

About The Project

Purpose

Analyzing sales data to identify the most successful city, popular products, and optimal selling times can help businesses to target their marketing efforts, adjust product offerings, and increase sales. Understanding customer buying patterns through e-commerce data analysis is crucial for businesses to optimize their sales and marketing strategies and improve customer satisfaction.

Questions need to be answered

  • Which cities have the highest sales revenue? Is there any trend in sales revenue in these cities over time?

  • What is the maximum number of orders? What are the peak hours for order placement?

  • Which products are most commonly sold together? Are there any patterns in the combination of products that customers buy together?

  • Which products are the bestsellers? And Why?

    Objectives

  • Collect and prepare sale data in 12 months

  • Combine data files, and clean, and process datasets to get ready for analysis.

  • Analyze dataset using Python

  • Share the conclusion of this analysis with the business owner to help them identify opportunities to improve their operations, and help them stay competitive in a rapidly evolving market.

    Visualization

Purpose

Analyzing sales data to identify the most successful city, popular products, and optimal selling times can help businesses to target their marketing efforts, adjust product offerings, and increase sales. Understanding customer buying patterns through e-commerce data analysis is crucial for businesses to optimize their sales and marketing strategies and improve customer satisfaction.

Questions need to be answered

  • Which cities have the highest sales revenue? Is there any trend in sales revenue

  • What is the maximum number of orders? What are the peak hours for order placement?

  • Which products are most commonly sold together? Are there any patterns in the combination of products that customers buy together?

  • Which products are the bestsellers? And Why?

    Objectives

  • Collect and prepare sale       

  • Combine data files, and clean, and process datasets to get ready for analysis.

  • Analyze dataset using Python, including libraries Pandas, Numpy, Matplotlib

  • Share the conclusion of this analysis with the business owner to help them identify opportunities to improve their operations, and help them stay competitive in a rapidly evolving market.

    Visualization

Key Findings 

  • Sales witnessed an increase during the last quarter of the year, specifically in October, November, and December, with December recording the highest peak in sales.

  • San Francisco leads the list of cities with the highest sales volume, followed by Los Angeles and New York.

  • The maximum number of orders recorded during the day was approximately 12,000 units, with the peak times being at 11 AM and 7 PM.

  • The product with the lowest price point garnered the highest sales figures.

  • The Google Phone and USB-C Charging Cable are commonly sold together and constitute a popular bundle purchase.