Online Retail Dataset Dashboards for Power BI

The Online Retail Dataset from the UCI Machine Learning Repository, with fields such as InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, and Country, is ideal for creating dashboards focused on sales, customer behavior, and inventory analysis. Below are some dashboard ideas you can create in Power BI:

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1. Sales Performance Dashboard

Objective: Monitor overall sales performance, identify trends, and evaluate revenue contributions from different dimensions.

Key Visuals:

• Total Revenue (KPI): A KPI card showing total sales (Quantity * UnitPrice).
• Sales Over Time: A line chart displaying revenue trends by month, week, or day based on InvoiceDate.
• Top Selling Products: A bar chart showing products (Description or StockCode) ranked by total sales quantity or revenue.
• Sales by Country: A map visualization highlighting revenue contribution by country.
• Average Revenue Per Customer: KPI card to show the average sales per customer.

Filters:

• Date range (InvoiceDate).
• Country.
• Product (StockCode or Description).

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2. Customer Insights Dashboard

Objective: Understand customer purchasing patterns and segment customers for targeted marketing.

Key Visuals:

• Customer Count: KPI card showing the total number of unique customers (CustomerID).
• Customer Segmentation: A pie chart dividing customers into segments based on revenue contribution (e.g., top 20%, mid 50%, bottom 30%).
• Customer Lifetime Value (CLV): A table showing CustomerID, total purchases, and average revenue per invoice.
• Repeat vs New Customers: A bar or doughnut chart showing the proportion of repeat customers versus new customers.
• Customer Activity: Line chart showing active customers (based on InvoiceDate) over time.

To create a Customer Segmentation Pie Chart based on revenue contribution, you’ll primarily use the following fields:

1. CustomerID: To uniquely identify each customer.
2. Quantity: To calculate the number of items purchased.
3. UnitPrice: To determine the price per unit of the items purchased.
4. Revenue Calculation: You need to calculate the Revenue per Customer using the formula:

Steps in Power BI:

1. Create Revenue per Customer:

• Create a measure or calculated column: Revenue = SUMX(OnlineRetail, OnlineRetail[Quantity] * OnlineRetail[UnitPrice])
• Group the revenue by CustomerID to get total revenue per customer:
  DAX RevenuePerCustomer = SUMMARIZE( OnlineRetail, OnlineRetail[CustomerID], "Total Revenue", SUM(OnlineRetail[Quantity] * OnlineRetail[UnitPrice]) )

2. Sort Customers by Revenue:

• Rank customers based on their revenue:
  DAX CustomerRank = RANKX( ALL(OnlineRetail[CustomerID]), CALCULATE(SUM(OnlineRetail[Quantity] * OnlineRetail[UnitPrice])), , DESC, DENSE )

3. Segment Customers:

• Use a calculated column or measure to segment customers into tiers (e.g., top 20%, mid 50%, bottom 30%):
  DAX CustomerSegment = SWITCH( TRUE(), CustomerRank <= ROUND(COUNTROWS(OnlineRetail[CustomerID]) * 0.2, 0), "Top 20%", CustomerRank <= ROUND(COUNTROWS(OnlineRetail[CustomerID]) * 0.7, 0), "Mid 50%", "Bottom 30%" )

1. Create the Pie Chart:

• Use CustomerSegment as the category.
• Use the sum of total revenue per segment as the value.

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Result:

• The Top 20% segment represents the customers contributing the highest revenue.
• The Mid 50% includes average spenders.
• The Bottom 30% comprises low-revenue customers.

This segmentation helps visualize revenue distribution among customer groups and aids in targeted marketing and customer relationship strategies.

Filters:

• Country.
• Invoice date range.
• Purchase category (StockCode or Description).

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3. Inventory & Stock Analysis Dashboard

Objective: Track product inventory levels, identify popular and slow-moving items, and analyze product performance.

Key Visuals:

• Top Products by Sales: Bar chart showing products ranked by total quantity sold.
• Low-Stock Products: Table showing products that require restocking (based on inventory thresholds, if available).
• Product Return Rate: Bar chart showing the percentage of returns (Quantity as negative values) for each product.
• Stock Turnover Rate: KPI card to display how quickly products are sold.
• Product Sales by Category: Treemap or bar chart grouping products into categories (if categorizable by StockCode or Description).

Filters:

• Product category or stock code.
• Country.
• Date range.

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4. Revenue and Profit Analysis Dashboard

Objective: Assess profitability and revenue trends across different segments.

Key Visuals:

• Revenue vs Quantity Sold: Scatter plot showing products’ revenue vs. total quantity sold to identify high-performing or low-performing products.
• Revenue Contribution by Country: Bar chart showing revenue split by country.
• Gross Margin Analysis: If cost data is available, show gross profit (Revenue - Cost) by product or category.
• Daily or Monthly Revenue Trend: Line chart showing revenue over time to track seasonality or growth.
• Invoice Size Distribution: Histogram showing the number of invoices by total value.

Filters:

• Product (StockCode or Description).
• Date range.
• Country.

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5. Return Analysis Dashboard

Objective: Analyze patterns in product returns to minimize loss.

Key Visuals:

• Total Returns: KPI card showing the total quantity of returns (negative Quantity values).
• Return Rate by Product: Bar chart showing the return rate for each product (Quantity as negative).
• Return Trends Over Time: Line chart showing returns by month or week.
• Returns by Country: Map visualization showing the distribution of returns across countries.
• Top Returned Products: Table or bar chart listing the most returned products.

Filters:

• Date range.
• Product (StockCode or Description).
• Country.

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6. Geographic Sales Dashboard

Objective: Visualize the geographical distribution of sales and identify key markets.

Key Visuals:

• Global Sales Map: A map showing revenue by country.
• Top Countries by Revenue: Bar chart ranking countries by total sales revenue.
• Country-Specific Product Popularity: Bar chart showing the top products sold in each country.
• Revenue per Customer by Country: A KPI or table showing average revenue per customer for each country.
• Sales Trends by Country: Line chart comparing sales trends for multiple countries.

Filters:

• Country.
• Date range.
• Product category or stock code.

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7. Operational Efficiency Dashboard

Objective: Analyze operational performance, such as sales velocity and invoice handling.

Key Visuals:

• Sales Velocity: KPI card showing average sales per day.
• Average Invoice Size: KPI card showing the average value per invoice.
• Invoices Processed Over Time: Line chart showing the number of invoices created daily or monthly.
• Invoice Distribution: Histogram of invoice sizes to show variation in order values.
• Top Invoices by Value: Table or chart showing the highest-value invoices.

Filters:

• Date range.
• Country.

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Features to Include in All Dashboards:

1. Slicers for Interactivity: Add slicers for InvoiceDate, Country, CustomerID, and StockCode to enable dynamic filtering.
2. Drill-throughs: Allow users to drill through from summary metrics (e.g., country revenue) to detailed views (e.g., customer or invoice details).
3. Custom Tooltips: Include additional information (e.g., quantity sold, unit price) in tooltips for data points.

These dashboards will help stakeholders gain a deeper understanding of sales trends, customer behavior, and inventory performance, enabling data-driven decision-making.