Sales Data Analysis Example

Comprehensive e-commerce sales analysis demonstrating aggregation, filtering, and visualization techniques.

Dataset Overview

Sample Data: Representative transactions across 5 regions and 5 product categories Columns: date, region, category, quantity, unit_price, total_sales

1. Total Sales by Region

Simple bar chart showing regional sales performance:

View Source

type: bar
title: Total Sales by Region
x: region
y: total_sales
width: 600
height: 400
data:
  source: '[{"region": "North", "total_sales": 125000}, {"region": "South", "total_sales": 98000}, {"region": "East", "total_sales": 112000}, {"region": "West", "total_sales": 87000}, {"region": "Central", "total_sales": 104000}]'

2. Sales Performance by Product Category

Compare performance across product categories:

View Source

type: bar
title: Sales by Product Category
x: category
y: revenue
width: 600
height: 400
data:
  source: '[{"category": "Electronics", "revenue": 185000, "num_transactions": 1250}, {"category": "Clothing", "revenue": 142000, "num_transactions": 2100}, {"category": "Food", "revenue": 98000, "num_transactions": 3200}, {"category": "Books", "revenue": 67000, "num_transactions": 1800}, {"category": "Home & Garden", "revenue": 134000, "num_transactions": 1150}]'

3. Regional Performance Heatmap

Compare regions and categories in a heatmap:

View Source

type: heatmap
engine: d3
title: Sales Heatmap - Region vs Category
x: region
y: category
color: sales
width: 600
height: 400
data:
  source: '[{"region": "North", "category": "Electronics", "sales": 42000}, {"region": "North", "category": "Clothing", "sales": 28000}, {"region": "North", "category": "Food", "sales": 18000}, {"region": "North", "category": "Books", "sales": 15000}, {"region": "North", "category": "Home & Garden", "sales": 22000}, {"region": "South", "category": "Electronics", "sales": 35000}, {"region": "South", "category": "Clothing", "sales": 24000}, {"region": "South", "category": "Food", "sales": 16000}, {"region": "South", "category": "Books", "sales": 11000}, {"region": "South", "category": "Home & Garden", "sales": 12000}, {"region": "East", "category": "Electronics", "sales": 38000}, {"region": "East", "category": "Clothing", "sales": 32000}, {"region": "East", "category": "Food", "sales": 22000}, {"region": "East", "category": "Books", "sales": 8000}, {"region": "East", "category": "Home & Garden", "sales": 12000}, {"region": "West", "category": "Electronics", "sales": 28000}, {"region": "West", "category": "Clothing", "sales": 22000}, {"region": "West", "category": "Food", "sales": 15000}, {"region": "West", "category": "Books", "sales": 12000}, {"region": "West", "category": "Home & Garden", "sales": 10000}, {"region": "Central", "category": "Electronics", "sales": 42000}, {"region": "Central", "category": "Clothing", "sales": 36000}, {"region": "Central", "category": "Food", "sales": 27000}, {"region": "Central", "category": "Books", "sales": 21000}, {"region": "Central", "category": "Home & Garden", "sales": 78000}]'

4. Top 10 Performing Region-Category Combinations

Find the highest performing region-category combinations:

View Source

type: bar
title: Top 10 Region-Category Combinations
x: combination
y: sales
width: 700
height: 400
data:
  source: '[{"combination": "Central - Home & Garden", "sales": 78000}, {"combination": "North - Electronics", "sales": 42000}, {"combination": "Central - Electronics", "sales": 42000}, {"combination": "East - Electronics", "sales": 38000}, {"combination": "Central - Clothing", "sales": 36000}, {"combination": "South - Electronics", "sales": 35000}, {"combination": "East - Clothing", "sales": 32000}, {"combination": "North - Clothing", "sales": 28000}, {"combination": "West - Electronics", "sales": 28000}, {"combination": "Central - Food", "sales": 27000}]'

5. Transaction Count by Region

How many transactions occur in each region:

View Source

type: bar
title: Transaction Volume by Region
x: region
y: transaction_count
width: 600
height: 400
data:
  source: '[{"region": "North", "transaction_count": 1850}, {"region": "South", "transaction_count": 1420}, {"region": "East", "transaction_count": 1680}, {"region": "West", "transaction_count": 1290}, {"region": "Central", "transaction_count": 2260}]'

6. Average Order Value by Category

Which product categories have higher average transaction values:

View Source

type: bar
title: Average Order Value by Category
x: category
y: avg_order_value
width: 600
height: 400
data:
  source: '[{"category": "Electronics", "avg_order_value": 148}, {"category": "Home & Garden", "avg_order_value": 117}, {"category": "Clothing", "avg_order_value": 68}, {"category": "Books", "avg_order_value": 37}, {"category": "Food", "avg_order_value": 31}]'

7. Quantity Sold by Category

Total units sold in each product category:

View Source

type: bar
title: Units Sold by Category
x: category
y: total_units
width: 600
height: 400
data:
  source: '[{"category": "Food", "total_units": 12500}, {"category": "Clothing", "total_units": 8200}, {"category": "Books", "total_units": 5400}, {"category": "Home & Garden", "total_units": 3200}, {"category": "Electronics", "total_units": 2100}]'

8. Monthly Sales Trend

Track total sales over time by month:

View Source

type: line
title: Monthly Sales Trend
x: month
y: monthly_revenue
width: 700
height: 400
data:
  source: '[{"month": "2024-01", "monthly_revenue": 42000}, {"month": "2024-02", "monthly_revenue": 38000}, {"month": "2024-03", "monthly_revenue": 45000}, {"month": "2024-04", "monthly_revenue": 52000}, {"month": "2024-05", "monthly_revenue": 48000}, {"month": "2024-06", "monthly_revenue": 55000}, {"month": "2024-07", "monthly_revenue": 62000}, {"month": "2024-08", "monthly_revenue": 58000}, {"month": "2024-09", "monthly_revenue": 51000}, {"month": "2024-10", "monthly_revenue": 48000}, {"month": "2024-11", "monthly_revenue": 65000}, {"month": "2024-12", "monthly_revenue": 72000}]'

9. Regional Revenue Distribution

Pie chart showing relative contribution of each region:

View Source

type: pie
title: Revenue Distribution by Region
x: region
y: regional_revenue
width: 500
height: 500
data:
  source: '[{"region": "North", "regional_revenue": 125000}, {"region": "South", "regional_revenue": 98000}, {"region": "East", "regional_revenue": 112000}, {"region": "West", "regional_revenue": 87000}, {"region": "Central", "regional_revenue": 104000}]'

10. Category Performance Scatter

Compare average order value vs transaction volume:

View Source

type: scatter
title: Category Performance (AOV vs Volume)
x: num_transactions
y: avg_order_value
color: category
width: 600
height: 400
data:
  source: '[{"category": "Electronics", "avg_order_value": 148, "num_transactions": 1250}, {"category": "Clothing", "avg_order_value": 68, "num_transactions": 2100}, {"category": "Food", "avg_order_value": 31, "num_transactions": 3200}, {"category": "Books", "avg_order_value": 37, "num_transactions": 1800}, {"category": "Home & Garden", "avg_order_value": 117, "num_transactions": 1150}]'

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Key Metrics from Sales Data

Metric	Value
Total Revenue	$526,000
Average Order Value	$62
Transaction Count	8,500
Top Region	North ($125K)
Top Category	Electronics ($185K)

Analysis Techniques Demonstrated

1. Aggregation: Group by single and multiple dimensions
2. Ranking: Sort to find top performers
3. Comparisons: Side-by-side bar charts across categories
4. Trends: Monthly aggregations show seasonal patterns
5. Proportions: Pie charts show relative contribution
6. Multi-dimensional: Heatmaps show region × category interaction
7. Correlation: Scatter plots reveal relationships

Business Insights

• Regional Performance: North and Central regions lead in revenue
• Product Performance: Electronics has highest revenue despite fewer transactions
• Seasonal Patterns: Holiday months (Nov-Dec) show peak sales
• Volume vs. Value: Food has high volume but low per-transaction value
• Cross-sell Opportunities: Central region shows strong Home & Garden performance