E-Commerce Sales Analytics & Financial Forecasting Dashboard
E-Commerce Sales Analytics & Financial Forecasting Dashboard
This project was developed to transform raw e-commerce transaction data into actionable business insights through an interactive analytics dashboard and financial forecasting model.
Using Excel, Power Query, Power Pivot, and DAX, I built a complete end-to-end solution that enables users to analyze sales performance, identify top-performing products, track key business metrics, and forecast future revenue and profitability.
E-commerce businesses generate large volumes of transactional data, but raw data alone does not provide meaningful insights.
The objective of this project was to:
Consolidate and clean sales data
Track key performance indicators (KPIs)
Analyze revenue trends and product performance
Create an interactive dashboard for decision-making
Build a financial forecasting model for future planning
Microsoft Excel
Power Query
Power Pivot
DAX (Data Analysis Expressions)
Pivot Tables
Pivot Charts
Slicers & Interactive Filters
The project began with cleaning and transforming raw transactional data using Power Query.
Removed duplicates and errors
Standardized data formats
Converted data types
Created a structured data model
Built a Date Dimension table for time intelligence analysis
This process ensured accurate reporting and scalable dashboard performance.
A relational data model was built using Power Pivot.
Total Revenue
Total Revenue =
SUM(Sales[Revenue])
Total Orders
Total Orders =
DISTINCTCOUNT(Sales[Order ID])
Total Customers
Total Customers =
DISTINCTCOUNT(Sales[Customer ID])
Average Order Value
AOV =
DIVIDE([Total Revenue],[Total Orders])
These measures enabled dynamic calculations across the entire dashboard.
The dashboard tracks:
Total Revenue
Total Orders
Total Customers
Average Order Value
Total Quantity Sold
Users can analyze:
Revenue trends over time
Product category performance
Top-selling products
Sales distribution across dimensions
Monthly growth patterns
Interactive slicers allow users to filter data by:
Date
Category
Product
Region
All visualizations update automatically based on user selections.
To extend beyond descriptive analytics, I developed a financial forecasting model connected to historical sales performance.
Starting Revenue
Revenue Growth Assumptions
Cost of Goods Sold (COGS)
Operating Expenses
EBIT
Taxes
Net Profit
Revenue forecasts are linked to historical performance using DAX calculations and business assumptions.
This allows users to simulate future financial outcomes and evaluate business growth potential.
The dashboard enables decision-makers to:
Identify top-performing products
Monitor revenue growth trends
Understand customer purchasing behavior
Track profitability drivers
Forecast future business performance
Built a complete end-to-end analytics workflow
Implemented Power Query ETL processes
Created a relational data model
Developed reusable DAX measures
Designed an interactive executive dashboard
Integrated financial forecasting into the analytics solution
The solution transforms raw transactional data into a decision-support tool that helps stakeholders monitor performance and plan future growth.
Data Cleaning
Data Transformation
Data Modeling
Business Intelligence
DAX Development
Dashboard Design
Financial Analysis
Forecasting
Excel Automation
KPI Reporting
This project demonstrates my ability to take raw business data through the entire analytics lifecycle—from data preparation and modeling to dashboard development and financial forecasting—while delivering insights that support data-driven decision-making.