Electrifing insights with BI.

#webdev #python #career #datascience

Introduction.

Equipments such as computers and smartphones, are widely available today, and the global consumer electronics market generated an impressive $950 billion in revenue in 2024.
The dataset is from GlobalElec, a leading global electronics retailer.

⭐ Project overview.

The goal of this analysis was to uncover electronic sales patterns of across three continents.
I analyzed and visualize the company’s sales data, covering customers, products, sales, stores, and international exchange rates.

Dataset.

💾 The data

Group	Column name	Dataset	Definition
Customers	CustomerKey	Customers.csv	Primary key to identify customers
Customers	Gender	Customers.csv	Customer gender
Customers	Name	Customers.csv	Customer full name
Customers	City	Customers.csv	Customer city
Customers	State Code	Customers.csv	Customer state (abbreviated)
Customers	State	Customers.csv	Customer state (full)
Customers	Zip Code	Customers.csv	Customer zip code
Customers	Country	Customers.csv	Customer country
Customers	Continent	Customers.csv	Customer continent
Customers	Birthday	Customers.csv	Customer date of birth
Products	ProductKey	Products.csv	Primary key to identify products
Products	Product Name	Products.csv	Product name
Products	Brand	Products.csv	Product brand
Products	Color	Products.csv	Product color
Products	Unit Cost USD	Products.csv	Cost to produce the product in USD
Products	Unit Price USD	Products.csv	Product list price in USD
Products	SubcategoryKey	Products.csv	Key to identify product subcategories
Products	Subcategory	Products.csv	Product subcategory name
Products	CategoryKey	Products.csv	Key to identify product categories
Products	Category	Products.csv	Product category name
Sales	Order Number	Sales.csv	Unique ID for each order
Sales	Line Item	Sales.csv	Identifies individual products purchased
Sales	Order Date	Sales.csv	Date the order was placed
Sales	Delivery Date	Sales.csv	Date the order was delivered
Sales	CustomerKey	Sales.csv	Unique key identifying which customer ordered
Sales	StoreKey	Sales.csv	Unique key identifying which store processed
Sales	ProductKey	Sales.csv	Unique key identifying which product purchased
Sales	Quantity	Sales.csv	Number of items purchased
Sales	Currency Code	Sales.csv	Currency used to process the order
Stores	StoreKey	Stores.csv	Primary key to identify stores
Stores	Country	Stores.csv	Store country
Stores	State	Stores.csv	Store state
Stores	Square Meters	Stores.csv	Store footprint in square meters
Stores	Open Date	Stores.csv	Store open date
Exchange Rates	Date	Exchange_Rates.csv	Date
Exchange Rates	Currency	Exchange_Rates.csv	Currency code
Exchange Rates	Exchange	Exchange_Rates.csv	Exchange rate compared to USD

Data Model.

Questions to answer ?

1️⃣:Which countries or regions contribute the most to overall revenue?
✅ Toronto.
🌎 North America
2️⃣:Which product categories and regions yield the highest profit margins?
💻 Computer
3️⃣: Are there patterns or changes in delivery times across different regions or product categories?

Dashboards.

The product dashboard

📌 After data cleaning, the dataset comprised 11 product brands,16 products varieties, 8 categories of products, and 32 sub categories.

Insights

🔹 White products were the most sold.

🔹 Computers were the most profitable products.

sales dashboard

📌 Total revenue was 55.76M, total profit 33.66M, 26K orders made and the sales were done in 5 currencies.

Insights.

🔹 North America had the highest sales, February recorded the highest profit .
🔹 Female purchased more products than there counterparts male.
🔹 Clients transacted more frequently using USD.