E-commerce businesses generate large volumes of transaction data, but without structured analysis it can be difficult to identify key revenue drivers and sales patterns.
This project analyzes 100,000 e-commerce transactions to answer several important business questions:
- Which markets generate the highest revenue?
- Which product categories drive business performance?
- Are there seasonal trends in sales?
- Which products contribute most to revenue?
- How do different order sizes affect overall sales?
The goal of this analysis is to build an interactive analytics dashboard that helps stakeholders quickly understand sales performance and explore trends across multiple dimensions.
The dataset contains 100,000 transaction records representing sales activity across multiple countries and product categories.
| Column | Description |
|---|---|
| Order_ID | Unique identifier for each order |
| Order_Date | Date of the transaction |
| Country | Customer country |
| Category | Product category |
| Product | Product name |
| Quantity | Number of items purchased |
| Unit_Price | Price per item |
| Revenue | Total transaction value |
The dataset provides sufficient scale and diversity to simulate real-world e-commerce analysis.
To prepare the dataset for analysis, several transformations were performed:
Additional columns were created to improve analytical flexibility:
| Feature | Purpose |
|---|---|
| Year | Enables yearly analysis |
| Month_No | Maintains chronological month sorting |
| Month | Used for trend visualization |
| Revenue_Segment | Categorizes orders by revenue value |
| Order_Size | Categorizes transactions by quantity |
These engineered features enable segmentation analysis and improved time-series visualization.
The analysis was conducted using Microsoft Excel with Pivot Tables and Pivot Charts.
Key analytical techniques included:
- Data aggregation using Pivot Tables
- Time-series analysis of revenue trends
- Product-level performance analysis
- Market segmentation by country
- Order value segmentation
Interactive slicers were added to enable dynamic filtering by:
- Country
- Category
- Month
This allows users to explore the data from multiple perspectives.
The final dashboard presents key business metrics and insights in an interactive format.
The dashboard highlights four core metrics:
- Total Revenue
- Total Orders
- Units Sold
- Average Order Value
These KPIs provide a high-level overview of business performance.
The dashboard includes the following visualizations:
| Visualization | Purpose |
|---|---|
| Revenue by Country | Identifies top-performing markets |
| Revenue by Category | Shows product category contribution |
| Monthly Revenue Trend | Reveals seasonal sales patterns |
| Top 10 Products | Highlights highest revenue products |
| Revenue by Segment | Analyzes order value distribution |
All visualizations update dynamically when filters are applied.
Several insights emerged from the analysis:
A small number of products contribute a significant share of total revenue, demonstrating the importance of maintaining inventory and marketing focus on high-performing products.
Certain product categories generate significantly more revenue than others, suggesting that strategic investment in these segments could improve overall business performance.
Monthly revenue trends reveal fluctuations in sales performance, indicating potential seasonal demand cycles or promotional effects.
Revenue distribution across countries shows that some markets outperform others, highlighting opportunities for targeted marketing and expansion strategies.
Revenue segmentation shows how different order sizes contribute to total sales, providing insights into customer purchasing behavior and opportunities to increase average order value.
This dashboard enables decision-makers to:
- Identify high-performing products and categories
- Understand regional revenue performance
- Detect seasonal sales patterns
- Explore customer purchasing behavior
By consolidating these insights into a single interactive dashboard, stakeholders can quickly analyze business performance and make data-driven decisions.
- Microsoft Excel
- Pivot Tables
- Pivot Charts
- Slicers
- Excel Dashboard Design