Sales Analysis: Electronics Store
Data Analysis
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DQ
Daniel Querales
A comprehensive analysis of sales records from an electronics store to identify revenue growth opportunities, evaluating performance by product, city, and time using Python and Flourish.
Project Overview
- Analysis of sales records from an electronics store.
- Sales evaluated by product, city, and time dimensions.
- Educational Purpose: Demonstration of data analysis techniques.
- Tools Used: Python (Pandas, Numpy) and Flourish Studio.
- Code available on GitHub Repository.
Objectives
The goal is to build a report that analyzes sales performance to understand what strategies can be implemented to increase profits.
Data Transformation
Data was obtained from multiple CSV files (one for each month), imported into a Jupyter Notebook, and then cleaned and prepared for analysis.
Key Business Questions
a) How much was the total sales amount?
b) Which months had the highest sales?
c) Which day of the week was the best performer?
d) What is the best hour to sell our products?
e) How were sales distributed by city?
f) Which are our most popular items sold?
g) Which items are often sold together?
Conclusions
- Total Sales: Approximately $35 Million USD.
- Best Months: October and December drove the highest revenue.
- Best Day: Tuesday showed the strongest sales performance.
- Peak Hours: 12:00 PM and 7:00 PM (19:00) are optimal times for sales.
- Top Region: Sales in California significantly outperformed other states.
- Top Product: The MacBook Pro Laptop was the most popular item.
- Bundle Trends: Phones, headphones, and USB-C cables are frequently purchased together.