How to Use Scatter Plot in Power BI

A scatter plot is one of the most effective ways to see the relationship between two different numbers. Instead of just guessing if your ad spend is related to your website traffic, a scatter plot shows you the connection instantly. This article walks you through exactly how to create, customize, and read a scatter plot in Power BI so you can uncover hidden patterns in your business data.

What is a Scatter Plot (and When Should You Use One)?

At its core, a scatter plot (also known as a scatter chart or scattergram) displays data points at the intersection of two different numeric variables. One variable determines the position on the horizontal axis (X-axis), and the other determines the position on the vertical axis (Y-axis). The result is a cloud of dots, and the shape of that cloud tells you a story about your data.

Think of a simple example: tracking daily ice cream sales versus the daily temperature. You’d put temperature on the X-axis and sales on the Y-axis. Each day would be a single dot on the chart. After a month, you'd likely see a cloud of dots rising from left to right, clearly showing that on hotter days, you sell more ice cream.

Scatter plots are incredibly useful for three main tasks:

• Finding Relationships (Correlation): This is their primary job. You can quickly see if a relationship between two variables is positive (as one goes up, the other goes up), negative (as one goes up, the other goes down), or if there's no relationship at all (the dots are just a random blob).
• Identifying Clusters: Sometimes, the dots will form distinct groups or clusters. For a marketer, this might show that certain types of campaigns cluster together, having a similar cost-per-lead and volume of leads, separate from other campaign types.
• Spotting Outliers: An outlier is a data point that sits far away from the rest of the pack. A scatter plot makes these outliers immediately obvious. Is there a single day where you had ten times more sales than usual? Or a marketing campaign that cost a fortune but delivered zero leads? These will stick out like a sore thumb.

Building Your First Scatter Plot in Power BI: A Step-by-Step Guide

Let's build one from scratch. We'll use a common marketing scenario: analyzing the performance of different ad campaigns. We want to see the relationship between how much we spent on a campaign and how many leads it generated.

Step 1: Get Your Data Ready

Before you start, make sure your data is structured for success. For a basic scatter plot, you need at least two numeric columns. In our example, we'd have:

• Campaign Name (Text)
• Total Spend (Numeric)
• Leads Generated (Numeric)

If you have additional metrics like Conversion Rate or Cost Per Lead, that's even better! These can add more layers to your analysis, which we’ll get to in a moment.

Step 2: Add the Scatter Chart Visual

In Power BI Desktop, look at the Visualizations pane on the right side. Find the scatter chart icon — it looks like a set of dots scattered in a grid. Click it to add a blank scatter chart to your report canvas.

Step 3: Populating the Fields

Now, you need to tell Power BI what data to show. Drag your data fields from the Data pane into the appropriate buckets in the Visualizations pane. For a scatter chart, you have several important options:

• X Axis: This is your independent variable — the thing you are adjusting or measuring. Let's drag our Total Spend field here. The horizontal position of each dot will be determined by how much was spent.
• Y Axis: This is your dependent variable — the result you are measuring. We'll drag our Leads Generated field here. The vertical position of each dot will be determined by the number of leads.
• Legend: This breaks down your data by category. If you drag a categorical field here, Power BI will use different colors for each category. For now, let's leave this blank.
• Values: Often called "Details," this determines what each individual dot on your chart represents. This is a critical field! Drag your Campaign Name field here. Without this, Power BI will likely just show you a single dot representing the grand total of all spend and all leads. Adding Campaign Name tells Power BI to plot one dot for each campaign.

Once you've done this, you should see your very first scatter plot take shape! Each dot represents a unique campaign, positioned based on its spend and lead count.

Step 4: Adding a Third Dimension with Size

What if you want to see another layer of information? That's where the Size bucket comes in. It allows you to use a third numeric value to control the size of each bubble. For example, if we also have a Click-Through Rate (CTR) for each campaign, we can drag the CTR field into the Size field. Now, campaigns with a higher CTR will appear as larger bubbles. This type of chart is often called a bubble chart, and it's fantastic for visualizing three variables at once.

Step 5: Animating Your Data with the Play Axis

The Play Axis is one of the coolest features for scatter plots in Power BI. It allows you to see how your data changes over time. If you have a date field (e.g., a "Month" column), you can drag it into the Play Axis bucket. This adds an animation timeline below your chart. You can press play to watch how the relationship between your X and Y variables evolves, month by month. It's an amazing way to visualize trends and tell a dynamic story about your data's journey.

Making Your Scatter Plot Tell a Story: Customization and Analysis

A default chart gives you a starting point, but customization is what turns raw data into a clear insight. Select your scatter plot and click on the "Format your visual" icon (the paintbrush) in the Visualizations pane.

Formatting for Clarity

Here are a few essential formatting options to improve readability:

• General: Use the Title section to give your chart a descriptive name, like "Campaign Spend vs. Leads Generated." Don't assume your audience knows what they're looking at.
• Visual > Markers: Here, you can change the Shape of the data points from the default circle to a square, triangle, or other shapes. You can also customize the marker size.
• Visual > Colors: If you used the Legend field, you can customize the colors assigned to each category to match your brand or to make certain categories stand out.
• Visual > Category labels: Turn these on if you want the name of each data point (e.g., the campaign name) to appear directly on the chart next to its dot. This can get crowded fast, so use it carefully!

Adding Analytical Tools to Your Chart

Formatting makes the chart prettier, but analytics features make it smarter. Under the "Add further analysis to your visual" tab (the magnifying glass icon), you'll find some powerful tools:

• Trend Line: This is a must-have for most scatter plots. Click "Add line," and Power BI will draw a line that best fits the general direction of your data points. This line instantly visualizes the correlation. An upward-sloping line shows a positive correlation, while a downward-sloping line shows a negative one.
• Symmetry Shading / Ratio Line: These are more advanced tools. A symmetry line can help you see if there are more data points above or below a simple 1:1 line. A ratio line is perfect for analyzing metrics like profitability, showing all points above or below a specific ratio you define.

Common Pitfalls and Best Practices

As you get more comfortable, keep these tips in mind to avoid common mistakes.

1. The Problem of Overplotting

If you have thousands of data points, your chart can quickly become an unreadable blob where dots are plotted on top of each other. This is called overplotting. To fix this, use Power BI's Filters pane to narrow down your data, perhaps by looking at a specific time frame or product category.

2. Confusing Correlation with Causation

This is the most famous rule in data analysis. Just because two variables move together doesn't mean one causes the other. For example, ice cream sales and shark attacks are positively correlated. Why? Because both happen more frequently during the hot summer months. The heat is the underlying cause, not the ice cream. Your scatter plot shows a relationship exists, but it's your job to investigate the "why."

3. Always Label Your Axes and Give Your Chart a Clear Title

It sounds simple, but it's the easiest mistake to make. A chart with axes labeled "total_rev" and "lead_cts_monthly" is confusing. Change them to "Total Revenue" and "Monthly Leads Generated." A clear title explaining what the chart shows is just as important.

Final Thoughts

Creating a scatter plot in Power BI is a straightforward process that grants you a powerful view into the relationships hiding in your data. By mapping two numeric variables against each other, you can quickly spot trends, identify outliers, and start asking deeper, more informed questions about what drives your business performance.

While mastering tools like Power BI is a valuable skill, we know that the initial steps of gathering, connecting, and cleaning your data are often the biggest headache. We built Graphed to simplify that process entirely. Instead of configuring data sources and manually building each visualization, you can connect your platforms like Google Analytics, Shopify, or Salesforce in a few clicks, and then just ask for what you need in plain English. That lets you move directly from a question to an insight in seconds, not hours.