Build an E-commerce Analytics Dashboard in Power BI

Tool

Power BI

Recommended length

Up to 3 pages

Including drill-through pages

Canvas size

16:9 or 1920 × 1080

ZoomCharts visuals

Use a minimum of 2 Drill Down Visuals

Within a single report page

Sales and profitability

How are gross sales, net sales, contribution revenue, and contribution margin changing over time?

Products and categories

Which departments, categories, subcategories, products, brands, and price tiers generate the strongest revenue and margin?

Returns and financial impact

Which products, customer groups, and return reasons drive refunds, reverse-logistics costs, processing costs, and return losses?

Promotions and discounts

Which campaigns and discount levels support profitable growth, and where do promotions increase sales while weakening contribution margin?

Customers and cohorts

How do customer segments, loyalty tiers, acquisition channels, age groups, cohorts, and lifecycle stages differ in sales and profitability?

Sales channels and markets

How do the website, mobile app, marketplace, and social commerce channels perform across European countries, regions, and cities?

Fulfillment and delivery

How do fulfillment models, centers, carriers, and service levels compare by cost, delivery speed, and on-time performance?

Inventory and lost sales

Where are low stock, partial availability, and backorders causing cancelled orders, unfulfilled demand, and lost sales value?

How visually clear, modern, and purposeful the report looks.

• Visually Appealing Design: Is the design harmonious, with a modern color palette, consistent formatting, and a clear presentation of information?
• Clear and Focused Visualizations: Are only the necessary visualizations used, avoiding clutter and keeping focus on key insights?
• Readability: Are color contrasts appropriate and text elements easy to read across different devices and screen sizes?

How clear and easy it is to understand the report at a glance.

• Logical Navigation: Is the report organized with intuitive navigation paths, reset options, and a structure that allows users to explore at their own pace?
• Storytelling through Data: Does the report present data in a logical and compelling narrative that supports decision-making?
• Streamlined Information: Is the report focused on key data without overwhelming the user with unnecessary details?
• Tooltips and Labeling: Are tooltips, titles, and legends clear, helpful, and supportive of understanding without cluttering the design?

How engaging and seamless the report exploration and interaction with data feels.

• Dynamic User Controls: Are slicers and interactive elements designed so users can easily adjust and explore different data views?
• Cross-Chart Integration: Are charts connected in a way that filtering and interactions feel smooth and unified across the report?
• Drill-Down Functionality: Can users intuitively dive deeper into data layers to explore additional details and insights?
• Guided Learning Aids: Are there helpful tutorials, hints, or onboarding elements that support users in understanding and navigating the report?

How accurate, complete, and meaningful the data is in driving decisions.

• Depth of Analysis: Does the analysis provide meaningful insights and fully address the problem using effective methods?
• Data Accuracy: Is the data consistent across visuals, and are calculations and aggregations applied correctly?
• Performance Optimization: Is the report optimized for fast loading and smooth performance, even with larger datasets?
• Data Cleanliness: Is the data well-prepared, free from redundancies or gaps, and structured to support reliable analysis?