When to Use Treemap in Tableau?

A treemap in Tableau can transform densely packed hierarchical data into an instantly readable digest of your business performance. Instead of overwhelming your team with a massive spreadsheet, a treemap uses size and color to quickly show you what's working and what isn't. This article will walk you through what a treemap is, its specific strengths, and exactly when to use one to get the clearest insights from your data.

What Exactly is a Treemap?

Imagine your data is a company with different departments, teams within those departments, and employees within those teams. A treemap represents this entire structure in a single view using nested rectangles.

The core concept is simple: display hierarchical data as a set of nested rectangles of different sizes and colors, where each rectangle represents a category and its "size" corresponds to a specific data value. Think of it as a more sophisticated and space-efficient version of a pie chart.

Here are the key components that make a treemap work:

• Rectangles: Each rectangle, or "tile," represents a category in your data. Larger rectangles are further broken down into smaller rectangles to show sub-categories, creating the "nested" hierarchical structure.
• Size: The area of each rectangle is proportional to a specific quantitative value. For example, in a sales dashboard, the biggest rectangles would represent your products or regions with the highest sales.
• Color: Color adds another layer of information. It can be used as a gradient to represent the same value as size (e.g., darker blue for higher sales) or represent an entirely different measure (e.g., green for profit, red for loss).

By combining size and color, you can spot patterns almost instantly. A very large, bright red rectangle, for instance, immediately flags a high-selling product that is simultaneously losing money - an insight that might be buried in rows of a spreadsheet.

The Main Strengths of Using a Treemap

Treemaps are not just visually interesting, they serve a practical purpose by making complex datasets more intuitive. Their design is particularly well-suited for a few key analytical tasks.

Displaying Complex Hierarchical Data

This is a treemap's primary strength. Data often has a natural parent-child structure. Think of product data structured as Category > Sub-Category > Product Name, or geographical data as Continent > Country > State. A treemap presents this structure far more cleanly than a series of bar or pie charts ever could. The visual grouping of smaller rectangles within larger ones makes the hierarchy undeniably clear.

Comparing Parts of a Whole

When you need to see how individual components contribute to a total, treemaps excel. By representing values as area, they make it easy to spot the biggest and smallest contributors at a glance. If you want to know which product sub-category makes up the bulk of your 'Technology' sales, a treemap will show you instantly. This is far more effective than a pie chart when you have more than a handful of categories, as a pie chart with 20 slices becomes nearly unreadable.

Finding Patterns and Identifying Anomalies

The "size plus color" combination is a powerful analytical tool. By using size to show one metric (like Revenue) and color for another (like Profit Margin), you can quickly spot interesting relationships and outliers.

• High Size, Low Color Value: A large rectangle with a 'bad' color (e.g., red for low profit) points to a significant problem area.
• Low Size, High Color Value: A small rectangle with a 'good' color (e.g., bright green for high profit margin) could signify a hidden gem or an area of opportunity.

Maximizing Information on a Single Screen

Dashboards have limited real estate. Treemaps are incredibly space-efficient, allowing you to display hundreds or even thousands of data points within a defined area. This helps avoid clutter and the need for excessive scrolling, making your dashboard more user-friendly and impactful.

So, When Should You Use a Treemap in Tableau?

Knowing when to use a certain chart type is just as important as knowing how to build it. Treemaps are ideal in specific business scenarios where you need to analyze hierarchical data based on two different measures. Here are three common use cases.

Use Case 1: Analyzing Product Sales and Profitability

Let's say you're a retail manager analyzing performance across all your products. Your goal is to identify which product categories are an engine for growth and which are draining resources. You have hierarchical data (Category > Sub-Category) and two key metrics: Sales and Profit.

A treemap is the perfect solution:

• Assign Sales to the Size property. Rectangles representing 'Technology' or 'Furniture' will be large if they drive a lot of revenue.
• Assign Profit to the Color property. Set up a diverging color palette where green means high profit, and red means high loss.

Instantly, you can answer critical questions:

• Which products are our "Stars"? (Large and dark green)
• Which are "Problem Children"? (Large and red - selling a lot but at a loss)
• Are there any "Hidden Gems"? (Small but dark green - not selling much, but highly profitable)

The answer to the infamous "Why are our tables so unprofitable?" question often found in Tableau's Superstore data becomes immediately apparent with this setup.

Use Case 2: Mapping Website Performance by Marketing Channel

As a digital marketer, you need to know where your website visitors are coming from. Your Google Analytics data is neatly hierarchical: Channel Grouping > Source/Medium > Landing Page. You're most interested in which channels bring in the most traffic (Sessions) and which ones result in the best engagement (low Bounce Rate or high Conversions).

Here’s how a treemap helps:

• Assign Sessions to the Size property. The 'Organic Search' channel will likely take up the most space.
• Assign Bounce Rate to the Color property. Let's use red for a high bounce rate and blue for a low bounce rate.

This visualization will give you a clear-cut view of your marketing performance. You might discover that a specific PPC campaign (a medium-sized rectangle) has an alarmingly high bounce rate (bright red), signaling that your landing page isn't matching the ad's promise.

Use Case 3: Visualizing Regional Business Performance

If you're a business development director for a global company, you need a high-level overview of performance across territories. Instead of presenting a dense table of financial figures in a quarterly review, a treemap provides an executive-friendly summary.

Structure your treemap this way:

• Create your hierarchy with Region > Country > State.
• Set the Size to represent Revenue.
• Set the Color to represent YoY Growth %.

Executives can immediately see which regions are the largest contributors to the bottom line (by size) and which are growing the fastest (by color). It steers the conversation toward strategy instead of getting bogged down in interpreting numbers.

When Not to Use a Treemap

While powerful, a treemap is not a one-size-fits-all solution. Using it in the wrong context can lead to more confusion, not less.

• Don't use it for showing precise comparisons. It's hard for the eye to accurately judge and compare the exact areas of different rectangles, especially side-by-side ones with different aspect ratios. A simple bar chart is much better for showing that Region A had $50,500 in sales versus Region B's $50,100.
• Don't use it for analyzing trends over time. A treemap is a snapshot in time. To see how sales have changed month-over-month, a line chart is the correct and most effective choice.
• Don't use it if your size measure includes negative values. A rectangle cannot have a negative area. A treemap can fail to render or produce a confusing chart if you try to size it by a measure like Profit, which can be negative. Use color for negative values, not size.
• Don't use it for displaying flat, non-hierarchical data. If you're simply comparing revenue between ten different salespeople, forcing that into a Treemap is poor practice. A bar chart would be simpler, cleaner, and easier to read.

How to Build a Treemap in Tableau in 5 Steps

Let’s put theory into practice by building the "Product Sales & Profitability" treemap using Tableau’s Sample - Superstore dataset.

1. Connect to Your Data: Open Tableau and connect to the Sample - Superstore data, which is included by default.

2. Select Your Basic Fields & Visualization Type: Hold down the Ctrl key (or Command on Mac) and select the Category dimension and the Sales measure from the Data pane. Then, go to the "Show Me" panel in the top right corner and click on the treemap icon. Tableau will instantly generate a basic treemap for you.

3. Build Out the Hierarchy: Now let's add the next level. Drag the Sub-Category dimension from the Data pane and drop it directly onto the Label card in the Marks pane. You'll see the larger category rectangles now contain smaller, labeled rectangles for each sub-category.

4. Add a Second Measure with Color: This is where the insight comes from. Drag the Profit measure from the Data pane and drop it onto the Color card in the Marks pane. By default, Tableau will apply a color gradient. You can customize this by clicking the Color card, selecting "Edit Colors," and choosing a diverging palette like "Red-Green Diverging." This will make profits green and losses red.

5. Refine and Customize Tooltips: The basic treemap is now built. To make it even more useful, you can clean up the labels and enhance the tooltip. Drag a field like Profit Ratio to the Tooltip card. Now, when a user hovers over any rectangle, they won't just see the Category, Sub-Category, Sales, and Profit, but also the Profit Ratio, adding valuable context without cluttering the main view.

Final Thoughts

Treemaps are a fantastic tool in the Tableau visualization arsenal, offering a clear and compact way to understand hierarchical data and compare parts of a whole across two different measures. They excel at uncovering patterns in complex datasets like product performance, web analytics, or regional sales, turning potentially overwhelming numbers into an actionable visual summary.

Recognizing the right use case and building charts in tools like Tableau still involves a learning curve and time spent on manual setup. At Graphed, we’ve simplified this entire process. You connect your data sources, then describe what you need in plain English - like "create a treemap of my top-selling product categories by sales, and color-code them by profit." Our AI data analyst builds the interactive dashboard for you in seconds, letting you go from question to insight faster than ever before.