How to Create a Donut Chart with AI

A donut chart is one of the quickest ways to see how different parts make up a whole, like showing which marketing channels are driving the most website traffic. Traditionally, creating one involved wrestling with spreadsheets, downloading CSV files, and clicking through a dozen menu options. This article shows you a much faster way: creating clear, accurate donut charts in seconds, just by describing what you want to see using everyday language.

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What Exactly is a Donut Chart?

Think of a donut chart as a pie chart with the center cut out. Both are used to display proportional data, where each slice represents a category's percentage of the total. That "donut hole" in the middle isn't just for looks - it provides a great space to display a key piece of information, like the total value of all the slices combined.

For example, you could create a donut chart to show your monthly sales by product as a key performance indicator (KPI). The slices would show the percentage of revenue from Product A, Product B, and Product C. Inside the hole, you could display the total monthly revenue, giving you two pieces of information in one clean visualization: overall performance and the individual contributors.

This design makes donut charts slightly more space-efficient and, some argue, easier to read than traditional pie charts because they encourage the eye to compare the length of the arcs rather than the less intuitive angles of pie slices.

When to Use (and Not Use) a Donut Chart

While useful, donut charts have specific use cases where they shine. Using them in the wrong context can lead to confusing reports that are harder to understand than a simple table. Here’s a quick guide on when to use them and when to choose another chart type.

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The Best Times to Use a Donut Chart:

• Comparing parts to a whole: This is their primary function. They are perfect for answering questions like, "What percentage of our Q3 revenue came from each sales region?" or "What proportion of our website visitors are using mobile vs. desktop?"
• For a small number of categories: Donut charts work best with six or fewer categories. This keeps the chart clean and easy to read. Anything more turns the visualization into a jumble of tiny, indistinguishable slices.
• Highlighting simple proportions: They excel at providing a high-level overview of a dataset. If you want to show that one or two categories make up the majority of the total, a donut chart communicates that instantly.

Relatable Examples:

• Analyzing traffic sources from Google Analytics (Organic, Direct, Social, Paid).
• Breaking down an ad campaign budget by platform (Facebook, Google, LinkedIn).
• Showing quarterly sales figures by top-selling products.

When to Avoid a Donut Chart:

• For comparing categories against each other: The human brain isn't great at accurately comparing the size of different arcs. A bar chart will always be better and more precise if you need to see if Category A is slightly larger than Category B.
• When you have many categories: If you're analyzing data with ten different categories, a donut chart will be unreadable. A horizontal bar chart is a much better choice, as it can neatly display many categories.
• Showing changes over time: Donut charts represent a snapshot at a single point in time. To track a metric's performance over weeks, months, or years, a line chart or a column chart is the proper tool for the job.

The Manual Way vs. The AI Way of Creating Charts

For years, even creating a simple chart like a donut chart was a manual, often frustrating task. The process locked data insights behind technical know-how. But with modern AI tools, that workflow has been completely transformed.

The Old-School Manual Process

If you've ever built a report for a weekly meeting, this probably sounds familiar:

1. Log into a platform like Google Analytics or Shopify.
2. Navigate to the right report and set the date range.
3. Export the data you need as a CSV file.
4. Open the file in Google Sheets or Excel.
5. Manually clean the data, removing unnecessary rows and columns.
6. Highlight the relevant data, click "Insert Chart," find the "Donut Chart" option, and hope it formats correctly.
7. Spend another ten minutes tweaking titles, labels, colors, and font sizes to make it presentable.

This whole process can take 15-30 minutes for a single chart. If your manager asks a follow-up question, you often have to start all over again. By the time you've wrangled the data and built the report, half your day is gone, and the data is already slightly out of date.

The New AI-Powered Approach

AI-powered analytics flips the entire process on its head. Instead of navigating complex software and manipulating data manually, it allows you to describe the chart you want in plain English. There’s no software to learn and no technical expertise required.

The core idea is simple: what if you could skip all the clicks, exports, and formatting, and just ask for your chart? This conversational approach removes the barrier between you and your data, transforming a 20-minute chore into a 20-second request.

How to Create a Donut Chart Using Plain English

Modern AI analytics tools make chart creation feel less like a technical task and more like having a conversation with a data analyst. Here’s a general step-by-step guide to how it works.

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Step 1: Connect Your Data Sources

First, you connect your business apps directly to the AI tool. This is usually a one-time setup involving signing in with your account credentials, just like logging into any other app. The system then automatically syncs your data in the background, making it ready for analysis. This eliminates the need for manual CSV downloads forever. Common sources you can connect include:

• Google Analytics
• Shopify or other e-commerce platforms
• Facebook Ads & Google Ads
• Salesforce or HubSpot
• Even data in a Google Sheet

Step 2: Ask Your Question in Natural Language

Once your data is connected, you can start asking for visualizations using simple, conversational prompts. You don’t need to know the official names of metrics or dimensions, the AI is smart enough to understand what you mean.

Example 1: Analyzing Website Traffic Instead of digging through Google Analytics, you can just ask:

Show me a donut chart of my website sessions by channel for the last 30 days.

The AI will instantly generate a donut chart with slices for 'Organic Search,' 'Direct,' 'Social,' 'Paid Search,' and so on, each correctly labeled with its percentage of the total sessions.

Example 2: Visualizing Sales Data Want to see what's driving revenue on your e-commerce store? Just ask:

Create a donut chart of our Shopify revenue by product category this quarter.

Instantly, you’ll get a clear visual showing which product categories are your top performers, without ever having to log into the Shopify admin and run a manual report.

Example 3: Breaking Down Ad Spend Need to understand your campaign budget allocation? A simple prompt is all it takes:

What's the breakdown of my Facebook Ads spend by campaign? Show me as a donut chart for this month.

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Step 3: Refine and Ask Follow-Up Questions

This is where AI analytics truly stands out from static tools. Your first chart often leads to more questions. With a conversational tool, you can continue to refine and drill down into the data with follow-up prompts.

After creating the website traffic chart from Example 1, you might realize some small channels are cluttering the view. You can follow up with:

Okay, now group all channels representing less than 5% into an 'Other' slice.

Or maybe you want to add the total number to the chart. You can just ask:

Put the total number of sessions in the middle of the chart.

No need to start over. The AI understands the context of your conversation and modifies the existing chart, allowing for exploratory analysis that uncovers insights at the speed of thought.

Tips for Effective Donut Charts

• Keep Categories Minimal: If you have more than six categories, your chart becomes a colorful but useless blob. Use a prompt to group smaller slices into an "Other" category to keep your visualization clean and focused on the most significant data points.
• Order Your Slices: A good donut chart should have its slices arranged logically, typically from largest to smallest, moving clockwise. Well-designed AI tools will usually do this for you automatically.
• Use Clear Labels: Ensure each slice is clearly labeled with both the category name and its percentage or value. This prevents confusion and makes the chart scannable without needing a separate legend.
• Leverage the Center: That empty space is valuable real estate. Use it to display the grand total - total sessions, total revenue, total users, etc. It provides immediate context for the proportions shown in the slices.
• Focus on a Single Dataset: Avoid trying to compare this month's data with last month's data in the same donut chart. The visual comparison will be confusing. It’s better to place two charts side-by-side or use a bar chart for direct comparison.

Final Thoughts

Donut charts offer a simple and effective way to visualize how individual categories contribute to a whole, but the traditional process of creating them has always been slow and manual. By leveraging AI, you can replace the tedious work of exporting files and fighting with spreadsheet settings with simple, conversational requests, getting you from a raw question to a clear insight in seconds.

This is precisely why we built Graphed. We wanted to make data accessible to everyone, not just those with technical skills. Our tool connects to all your key data sources - from Google Analytics and Shopify to your ad platforms - and lets you create entire dashboards using conversational language. We handle all the hard work behind the scenes, so you can stop wrestling with data and focus on making informed decisions for your business.