Creating an E-commerce Sales Dashboard in Looker Studio

E-commerce businesses generate massive amounts of data. The challenge isn’t collecting data—it’s turning it into actionable insights. In this guide, I’ll walk you through building a complete e-commerce sales dashboard in Looker Studio that I’ve refined through dozens of implementations.

By the end, you’ll have a dashboard that answers these critical questions:

• How are sales trending?
• What products are performing best?
• Where are customers coming from?
• What’s the average order value?
• Which regions drive the most revenue?

The Dashboard We’ll Build

Here’s an example of the type of e-commerce dashboard we’ll create:

Our e-commerce dashboard will include:

1. KPI Scorecards - Total revenue, orders, AOV, customers
2. Sales Trend Chart - Revenue over time
3. Product Performance Table - Top products by revenue
4. Geographic Breakdown - Sales by region/country
5. Category Analysis - Revenue by product category
6. Interactive Filters - Date range, category, region

Prerequisites

You’ll need:

• Looker Studio account
• E-commerce data source (we’ll use a Google Sheets example, but this works with BigQuery, GA4, or any data source)
• Understanding of your key metrics

Step 1: Prepare Your Data

First, let’s ensure your data has the right structure. A typical e-commerce dataset should include:

Field	Type	Example
order_id	Text	ORD-12345
order_date	Date	2024-01-15
customer_id	Text	CUST-789
product_name	Text	Blue Widget
category	Text	Electronics
quantity	Number	2
unit_price	Number	49.99
total_revenue	Number	99.98
region	Text	North
country	Text	United States
traffic_source	Text	Google Ads

Sample Data for Testing

If you want to follow along, create a Google Sheet with this structure and at least 100 rows of sample data.

Pro Tip: For realistic data, vary your dates across 3-6 months, include 10-20 products across 3-5 categories, and distribute orders across multiple regions.

Step 2: Connect Your Data Source

1. Open Looker Studio
2. Click Create → Report
3. Select your data source:
   • For Google Sheets: Select the sheet
   • For BigQuery: Choose your table or custom query
   • For GA4: Connect your property
4. Click Add to create the report

If Using BigQuery (Recommended for Large Datasets)

Use this optimized query:

SELECT
  DATE(order_date) AS order_date,
  order_id,
  customer_id,
  product_name,
  category,
  region,
  country,
  traffic_source,
  quantity,
  unit_price,
  total_revenue
FROM `your-project.your_dataset.orders`
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 365 DAY)

Step 3: Set Up the Dashboard Layout

Before adding charts, plan your layout:

+------------------------------------------+
|           📊 E-commerce Dashboard         |
|           [Date Range Filter]             |
+----------+----------+----------+----------+
| Revenue  | Orders   |   AOV    | Customers|
+----------+----------+----------+----------+
|          Sales Trend Over Time            |
+--------------------+---------------------+
| Top Products       | Sales by Category   |
+--------------------+---------------------+
|        Sales by Region (Map/Chart)        |
+------------------------------------------+

Step 4: Create KPI Scorecards

Add four scorecards across the top:

Scorecard 1: Total Revenue

1. Click Add a chart → Scorecard
2. Configure:
   • Metric: total_revenue
   • Aggregation: SUM
3. Style:
   • Large, bold number
   • Add currency prefix ($)
   • Add comparison (vs previous period)

Scorecard 2: Total Orders

1. Add another scorecard
2. Configure:
   • Metric: order_id
   • Aggregation: COUNT DISTINCT

Scorecard 3: Average Order Value (Calculated Field)

First, create a calculated field:

1. Click Add a field in your data source
2. Name: Average Order Value
3. Formula: SUM(total_revenue) / COUNT_DISTINCT(order_id)

Then add a scorecard using this field.

Scorecard 4: Total Customers

1. Add scorecard
2. Configure:
   • Metric: customer_id
   • Aggregation: COUNT DISTINCT

Pro Tip: Add comparison to previous period for all scorecards. Users love seeing whether metrics are up or down.

Step 5: Add Sales Trend Chart

1. Add a Time Series Chart
2. Configure:
   • Dimension: order_date
   • Metric: total_revenue (SUM)
3. Optional enhancements:
   • Add a trend line (Style panel)
   • Add comparison period (toggle in Data panel)
   • Set granularity (day, week, month)

Adding Multiple Metrics

To show revenue AND orders on the same chart:

1. Add order_id (COUNT DISTINCT) as a second metric
2. In Style panel, set the second metric to use the right Y-axis
3. This creates a dual-axis chart

Step 6: Create Product Performance Table

1. Add a Table chart
2. Configure:
   • Dimension: product_name
   • Metrics:
     • total_revenue (SUM)
     • quantity (SUM)
     • order_id (COUNT DISTINCT) - label as “Orders”
3. Add sorting:
   • Sort by total_revenue, descending
4. Style:
   • Add heat map to revenue column
   • Limit to top 10 rows
   • Add pagination for full list

Add a Calculated Metric: Revenue per Order

1. Create calculated field: SUM(total_revenue) / COUNT_DISTINCT(order_id)
2. Add to table
3. This shows which products have higher transaction values

Step 7: Add Category Breakdown

1. Add a Pie Chart or Donut Chart
2. Configure:
   • Dimension: category
   • Metric: total_revenue (SUM)
3. Style:
   • Limit to top 5 categories
   • Show percentages in labels
   • Use consistent colors

Alternative: Use a Bar Chart for easier comparison:

1. Horizontal bars work better for category names
2. Add data labels for exact values

Step 8: Geographic Analysis

Option A: Geo Map (if you have country/region data)

1. Add a Geo Map
2. Configure:
   • Dimension: country (or region)
   • Metric: total_revenue (SUM)
3. Style:
   • Choose color range (light to dark)
   • Enable tooltips

Option B: Bar Chart by Region

1. Add a Bar Chart
2. Configure:
   • Dimension: region
   • Metric: total_revenue (SUM)
3. Sort by revenue, descending

Step 9: Add Interactive Filters

Date Range Filter

1. Add Insert → Date range control
2. Position at the top of the dashboard
3. Set default range (e.g., last 30 days)

Category Filter

1. Add Insert → Drop-down list
2. Configure:
   • Control field: category
3. Position next to date range

Region Filter

1. Add another Drop-down list
2. Configure:
   • Control field: region

Pro Tip: Use Advanced filters to let one chart filter others. Click on a product in the table, and all charts update.

Step 10: Add Traffic Source Analysis (Bonus)

If you have traffic source data:

1. Add a Bar Chart
2. Configure:
   • Dimension: traffic_source
   • Metrics:
     • total_revenue (SUM)
     • order_id (COUNT DISTINCT)
3. This shows which channels drive the most revenue

Calculate Conversion Value by Source

Create calculated field:

SUM(total_revenue) / COUNT_DISTINCT(customer_id)

This shows revenue per customer by traffic source.

Advanced Enhancements

Calculated Fields for Deeper Insights

Customer Lifetime Value (simplified):

SUM(total_revenue) / COUNT_DISTINCT(customer_id)

Revenue per Unit:

SUM(total_revenue) / SUM(quantity)

Month-over-Month Growth: Use comparison features in scorecards and time series charts.

Conditional Formatting

In tables, add conditional formatting:

1. Click on a metric in the table
2. Go to Style → Conditional formatting
3. Add rules:
   • Green if revenue > $10,000
   • Red if revenue < $1,000

Drill-Down Capabilities

Enable drill-down for hierarchical data:

1. In a chart, add multiple dimensions
2. Enable Drill down in Data panel
3. Users can click to go from Category → Product → SKU

Dashboard Best Practices

Based on 8+ years of building e-commerce dashboards:

1. Focus on Actionable Metrics

Include metrics that drive decisions:

• ✅ Revenue trend (take action on dips)
• ✅ Top products (stock management)
• ✅ Regional performance (marketing focus)
• ❌ Page views (not directly actionable)

2. Use Consistent Time Periods

All charts should default to the same date range. Use the date filter to control everything.

3. Add Context

Include:

• Comparison to previous period
• Target lines where relevant
• Brief explanatory text for complex metrics

4. Optimize for Your Audience

• Executives: High-level KPIs, trends
• Marketing: Channel performance, customer acquisition
• Operations: Product inventory, regional distribution

Common Issues and Fixes

Issue	Cause	Solution
Revenue shows as 0	Field type is Text	Change to Number in data source
Date not sorting correctly	Wrong date format	Ensure YYYY-MM-DD format
Filters not working	Not connected to data source	Check filter settings
Dashboard is slow	Too much data	Add date filters, use BigQuery

Sample Dashboard Template

Here’s a quick reference for your layout:

Row 1: Title + Date Filter + Category Filter Row 2: 4 KPI Scorecards Row 3: Sales Trend (full width) Row 4: Product Table (50%) + Category Pie (50%) Row 5: Regional Map or Bar Chart (full width)

Need Help Building Your E-commerce Dashboard?

Building effective dashboards takes time and expertise. If you need help:

• Connecting to your data sources (Shopify, WooCommerce, BigQuery)
• Creating custom metrics and KPIs
• Optimizing performance for large datasets
• Building executive-level reporting

Schedule a free consultation and let’s build a dashboard tailored to your business.

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Want more Looker Studio tutorials? Check out our guides on Building Your First Dashboard and Marketing Attribution Dashboards.