How to Use Heatmaps in Looker Dashboards

Heatmaps transform overwhelming grids of numbers into a clear story, immediately drawing your eye to the hotspots and cold zones in your data. Instead of scanning endless rows, you can spot patterns in seconds - whether it's identifying your busiest customer support hours or finding which states have the highest sales concentration. This guide will walk you through creating both grid-style and map-based heatmaps in your Looker dashboards so you can quickly turn data into decisions.

What is a Heatmap, Really?

At its core, a heatmap is a data visualization technique that uses color gradients to represent individual values in a matrix or on a map. Warmer and darker colors typically indicate higher values or greater density, while cooler and lighter colors signify lower values or sparse data. It’s the visual difference between a packed spreadsheet and an intuitive, at-a-glance report.

Common uses include:

Performance Analysis: A grid showing user engagement (e.g., logins) by hour of the day and day of the week, highlighting peak usage times.
Sales Monitoring: A map of the United States where states with higher sales numbers glow brighter, revealing your top-performing territories.
Financial Oversight: A table comparing product costs over time, with colors that flag any sharp increases.

The main benefit of a heatmap is its ability to make large volumes of data digestible. You don't need to read every cell, you just need to follow the colors to understand the narrative of your data.

Building a Grid Heatmap in Looker (Step-by-Step)

The most common type of heatmap is the grid format - perfect for analyzing the intersection of two different dimensions, like time and category. Let's build one to find the busiest times for customer support requests.

1. Prepare Your Data in an Explore

To create a grid heatmap, you need three key ingredients from your data model:

A primary dimension for the rows: We'll use Day of the Week.
A second dimension to pivot for the columns: We'll use Hour of the Day.
A measure to color the cells: We'll use a count of Support Tickets Created.

Start by heading to the Explore where your support ticket data lives. In the "All Fields" panel on the left:

Select the dimension for your rows (e.g., "Created Day of Week").
Select the dimension for your columns (e.g., "Created Hour").
Select the measure that will determine the color intensity (e.g., "Ticket Count").

Your data table will just look like a long list for now. The next step is what creates the grid structure.

2. Pivot Your Column Dimension

Pivoting is the essential step that transforms your long list into a two-dimensional grid. Find your column dimension ("Created Hour" in our example) in the data table, click the gear icon on the column header, and select Pivot.

After pivoting, click Run in the top right. You'll now have a proper grid with "Day of Week" as your rows and each "Hour" as its own column, with the corresponding ticket count in each cell.

3. Choose the Right Visualization

Here’s a slightly quirky but important detail: grid heatmaps in Looker are actually a feature of the Table (Next) visualization, not a standalone chart type.

In the Visualization pane, select the icon for "Table (Next)." This newer table visualization has the conditional formatting options we need.

4. Apply Conditional Formatting for the Heatmap Effect

Now it’s time to add the color. With your "Table (Next)" visualization active, click the Edit button in the top right of the visualization pane.

Under the Customizations tab, find the Conditional Formatting section. You might have to scroll a little.
Click Add Rule. A new formatting menu will appear.
In the dialogue box, under Format, choose Color scale. This is the setting that creates the color gradient.
Under Apply to, select the fields you want to color. In our case, this will be our pivot columns (the ticket counts for each hour). You can select All numeric fields to make it simple.
Under Color/Palette, choose a palette that makes sense. A sequential palette (like light green to dark green) is perfect for showing low-to-high values. A diverging palette (like red-white-green) is better for showing values that go above and below a center point, such as profit and loss.

And that’s it! Your plain data table will now be a colorful heatmap, clearly showing that support requests spike, for example, on Tuesday afternoons and are very low over the weekend.

Creating a Geospatial Heatmap on a Map

A grid isn’t the only way to visualize density. When your data has a geographic component, like zip codes or coordinates, a map-based heatmap is much more powerful. This is useful for identifying sales hotspots, customer concentrations, or delivery service areas.

1. Get Your Geographic Data Ready

To create a map heatmap, you will need:

A location dimension: Looker supports dimensions like State, Country, Zip Code. For the highest precision, use a dimension with the location data type that contains latitude and longitude coordinates.
A measure: This could be the number of customers, total sales, or order volume associated with each location.

In an Explore, select your location dimension (e.g., "Customer Zip Code") and your measure (e.g., "Order Count"). Run the query.

2. Select the "Map" Visualization

In the Visualization pane, choose one of the map options. "Map" works well for this, creating points on a world map. At first, you'll see a series of individual points for each location.

3. Enable the Density Heatmap Layer

To convert those points into a smooth heatmap, you'll need to edit the visualization settings.

Click the Edit button for the visualization.
Go to the Points tab in the edit menu.
Find the mode or type setting and change it from Points to Density Heatmap.

This will group nearby points into colored clusters, showing precisely where your activity is most concentrated. You can further adjust the Radius setting to define how large the "hot" areas are and tweak the color opacity to your liking.

Practical Tips for Better Heatmaps

Just making a heatmap isn't enough. Here's how to make them truly useful:

Choose Your Colors Wisely: The default color palettes are fine, but thinking about the story helps. Red often signals a problem, while green signals success. Use neutral, sequential colors for displaying intensity without positive or negative judgment.
Don't Hide the Numbers: In some cases, the color alone is enough. But if the specific values still matter, you can toggle on Show Cell Values in the table customization settings so your audience gets both the visual cues and the hard numbers.
Provide Context with a Title: Save your heatmap to a dashboard and give it a clear, descriptive title. "Customer Support Ticket Density by Day and Hour" is far more useful than "Ticket Count."

Final Thoughts

Heatmaps in Looker are a fantastic way to distill complex data tables into clear, actionable visual reports. Whether you’re setting up a grid heatmap using a pivoted table or a geospatial view with the map visualization, the core principle is the same: use color to tell a story about density, intensity, and focus. Once you master this skill, you'll find it an indispensable part of your reporting toolkit for quickly identifying trends.

While Looker offers powerful tools for those willing to configure pivots, select visualizations, and adjust conditional formatting rules, we know that process can be tedious. We created Graphed to remove those manual steps. You can simply ask a question in plain English, like "Show me a heatmap of sales by state last quarter," and instantly get an interactive, real-time dashboard. If you'd rather spend your time acting on insights instead of building reports, you can get started in seconds.