This project presents a business-focused e-commerce sales performance dashboard built in Power BI.
The objective was to translate transactional sales data into clear, decision-ready insights to support commercial planning, product prioritisation, and customer value management.
The dashboard was designed to balance analytical rigour with usability, ensuring that KPIs remain transparent, reproducible, and aligned with business logic.
E-commerce businesses rely on regular performance reporting to understand which products, customers, and markets generate the most value.
This dashboard was created to help commercial and management teams:
- monitor overall sales performance
- identify high-value products and customers
- understand seasonal patterns
- support pricing, marketing, and stock decisions
without requiring advanced technical knowledge.
- Source: Kaggle – E-Commerce / Online Retail Dataset
- Period: December 2010 – December 2011
- Data type: transactional sales data
- Geography: United Kingdom (with international customers)
- What are the total revenue and average order value?
- Which products generate the highest revenue?
- How does revenue change over time?
- Which countries contribute most to revenue?
- Who are the top customers by revenue?
- KPI cards: Total Revenue, Average Order Value, Orders, Customers
- Top Products by Revenue
- Top 10 Customers by Revenue
- Revenue by Country
- Revenue Over Time (monthly)
- Interactive slicer for Invoice Date (Year)
- Power BI Desktop
- Power Query (data cleaning and transformation)
- DAX measures
- Data modelling
- Data visualisation and dashboard design
- GitHub documentation
- Power BI dashboard (.pbix)
- Cleaned and transformed dataset
- Dashboard screenshots
- Project documentation (README)
- Data cleaning and transformation in Power Query
- Building analytical data models
- Creating DAX measures and KPIs
- Commercial and customer performance analysis
- Translating data into business insights
- Designing dashboards for non-technical users
The project followed a structured analytical process:
- Reviewing the business context and reporting requirements
- Cleaning and transforming raw transactional data in Power Query
- Building relationships and calculated measures in the data model
- Creating KPIs and core performance indicators using DAX
- Designing an intuitive dashboard layout
- Validating insights against business logic
This ensured that the dashboard supports both exploration and decision-making.
e-commerce_dashboard.pbix– Power BI dashboard file/screenshots– dashboard previewsREADME.md– project documentation
- Revenue shows a clear upward trend towards the end of 2011, indicating strong seasonality in Q4.
- The United Kingdom is the dominant market, contributing the majority of total revenue.
- A small group of products generates a disproportionate share of total revenue (Pareto effect).
- High-value customers are responsible for a significant portion of sales, highlighting opportunities for segmentation and retention strategies.
- Add customer segmentation (e.g. RFM analysis)
- Analyse return and cancellation behaviour
- Build a separate page focused on customer lifetime value
- Publish the dashboard to Power BI Service with role-level security
e-commerce-sales-powerbi/
│
├── screenshots/
│ └── dashboard_overview.jpg
│
├── e-commerce_dashboard.pbix
└── README.md
This project is part of my portfolio as a Junior Data & Business Analyst, focused on practical commercial reporting and decision support using Power BI.