E-commerce Sales Analysis & Dashboard in Microsoft Excel Designed and developed an end-to-end Exc...

E-commerce Sales Analysis & Dashboard in Microsoft Excel Designed and developed an end-to-end Exc...E-commerce Sales Analysis & Dashboard in Microsoft Excel Designed and developed an end-to-end Exc...

The network for creativity

Join 1.25M professional creatives like you

Connect with clients, get discovered, and run your business 100% commission-free

Creatives on Contra have earned over $150M and we are just getting started

Back to feedPost

Muhammad Muneeb

• Jul 18

E-commerce Sales Analysis & Dashboard in Microsoft Excel

Designed and developed an end-to-end Excel analytics solution to transform raw e-commerce sales data into actionable business insights.

The project involved cleaning and organizing transactional data, creating Pivot Tables, and building interactive charts to analyze customer behavior, payment preferences, and regional sales performance.

What I Delivered

Data Cleaning & Preparation

Excel Table Design

Pivot Tables & Pivot Charts

Customer Distribution Analysis

Revenue by Region

Payment Method Analysis

Business Reporting Dashboard

Tools Used

Microsoft Excel

Pivot Tables

Pivot Charts

Data Cleaning

Excel Functions

Business Analytics

Data Visualization

This dashboard enables businesses to quickly identify sales trends, customer preferences, and regional performance, supporting faster and more informed decision-making.

Data Analysis Microsoft Excel Bookkeeping Business Analysis

Humrahi Teaching hub

• Jul 16

:📊 Hello everyone! I'm a beginner Data Analyst with skills in Microsoft Excel and PDF to Excel conversion.

I can help with: • Excel data cleaning • PDF to Excel conversion • Data formatting data analysis

Microsoft Excel pdfconverting Data Analysis

sheet pilot

• Jul 16

Inventory Data Cleaning and Formatting in Microsoft Excel

Microsoft Excel Education

Advik Rathee

• Jul 14

Built this one for an e-commerce customer behavior project - 18,000+ customers, segmented using RFM (Recency, Frequency, Monetary) analysis.

The interesting part wasn't the segmentation itself, it was what it revealed: nearly 22% of customers were "at risk" of churning, and most businesses wouldn't know that until those customers had already stopped buying.

This is the kind of insight that's usually sitting in your order data already, it just needs the right lens. Happy to take a look at your numbers if you're curious what's hiding in yours.

Tools used: Power BI, SQL, Python (Pandas, Scikit-learn for clustering)