📊 Online Retail Purchase Patterns Dashboard

By Prince Uwagboe

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Table of Contents

1. Introduction
2. Key Features
3. Technologies Used
4. Data Sources
5. Data Cleaning and Preparation
6. Data Modeling
7. Data Transformation
8. Dashboard Design and Visualization
9. DAX Calculations
10. How to Use
11. Results and Insights
12. Skills Demonstrated
13. Visual Gallery
14. Contact

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1️⃣ Introduction

The Online Retail Purchase Patterns Dashboard provides a detailed, interactive analysis of retail sales from 2010–2011. It empowers business users to explore revenue drivers, customer behavior, product trends, and geographic sales performance.

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2️⃣ Key Features

• KPI cards summarizing total sales, customer count, and invoices.
• Top-selling product ranking with conditional formatting.
• Geographic sales mapping.
• Year and month slicers for dynamic filtering.
• Advanced DAX calculations including % of total sales and ranking.
• Interactive tooltips displaying key insights.
• Clean, professional dark theme with optimized layout.

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3️⃣ Technologies Used

• Tools: Power BI, Microsoft Excel, Power Query
• Languages: DAX (Data Analysis Expressions)
• Data Source: Kaggle Online Retail dataset (2010–2011)

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4️⃣ Data Sources

Primary Source

• Excel workbook (uploaded manually to Power BI)

Extraction Method

• Imported directly into Power BI Desktop.

Access

• File included in this repository: online retail purchase patterns dashboard.pbix
• Raw Dataset: datasets/online_retail_raw.xlsx
• Cleaned Dataset: datasets/online_retail_cleaned.xlsx

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5️⃣ Data Cleaning and Preparation

• Removed ~5,231 duplicate rows.
• Removed rows with null Customer IDs or invalid dates.
• Filtered out negative quantities and non-standard entries.
• Converted data types for accuracy (dates, numeric fields).
• Created year, month, and day columns for time analysis.

Tools Used: Excel, Power Query

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6️⃣ Data Modeling

• Model: Flat, single-table model for performance and simplicity.
• Relationships: Single fact table (Year 2010–2011).
• Calculated Columns:
  • YearOnly (extracted from InvoiceDate).
• Hierarchies: Year > Quarter > Month > Day.

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7️⃣ Data Transformation

Performed in Power Query:

• Removed empty and duplicate rows.
• Filtered invalid quantities.
• Validated data types.
• Created date hierarchy columns for time-based filtering.

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8️⃣ Dashboard Design and Visualization

Layout Principles

• KPI Summary Row: Total Sales, Customers, Invoices.
• Main Charts:
  • Bar chart for top products.
  • Area chart for monthly sales.
  • Map visual for geographic sales.
  • Line chart for customer sales trends.
• Slicer Panel: Year and month filters.
• Interactivity: Tooltips, cross-filtering, dynamic slicers.

Design Best Practices

• Dark theme for high contrast.
• Minimal clutter and consistent color scheme.
• Tooltips to enhance user experience.

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9️⃣ DAX Calculations

Measure	Formula	Purpose
% of Total Sales	DIVIDE(SUM([TotalPrice]), CALCULATE(SUM([TotalPrice]), ALL('Year 2010-2011')))	Product’s share of total revenue.
Product Sales Rank	RANKX(ALL('Year 2010-2011'[Description]), CALCULATE(SUM([TotalPrice])), , DESC)	Rank products by revenue.
Average Order Value	DIVIDE(SUM([TotalPrice]), DISTINCTCOUNT([Invoice]))	Average value per order.
Avg Quantity per Order	DIVIDE(SUM([Quantity]), DISTINCTCOUNT([Invoice]))	Average items per order.
YearOnly	YEAR([InvoiceDate])	Extracted year for filtering.

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🔟 How to Use

1. Clone or download this repository.
2. Open online retail purchase patterns dashboard.pbix in Power BI Desktop.
3. Use slicers to filter data by year or month.
4. Hover over visuals to view detailed tooltips including % of total sales and product rank.

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1️⃣1️⃣ Results and Insights

• Top-selling product: DOTCOM POSTAGE (0.02% of total revenue).
• Peak sales months: November and December.
• Largest market: Canada.
• Sales trends showed significant seasonal increases in Q4.

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1️⃣2️⃣ Skills Demonstrated

• Data cleaning and preparation (Excel, Power Query).
• Data modeling and DAX calculations.
• Advanced visual design and conditional formatting.
• Interactive dashboard development.
• Comprehensive project documentation.
• Problem-solving and troubleshooting.

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1️⃣3️⃣ Visual Gallery

Visual	Description	Image
Dashboard Overview	Overview of the entire dashboard.
Total Price KPI	Total revenue overview.
Customer Count KPI	Total unique customers.
Invoice Count KPI	Total invoices/orders.
Average Order Value	Average revenue per order.
Avg Quantity per Order	Average items per order.
Top Products	Bar chart with conditional formatting.
Sales by Month	Area chart showing monthly sales trend.
Customer Sales	Line chart of customer sales.
Sales by Country	Map showing geographic sales distribution.
Year/Month Slicer	Dynamic filtering by year and month.

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1️⃣4️⃣ Contact

LinkedIn: Prince Uwagboe
Email: [email protected]

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