How to Make a Clustered Column Chart in Google Analytics with AI

Trying to compare the performance of different marketing channels in Google Analytics can feel like you need a background in data science. You know the insight is buried in there somewhere, but getting it into a clean, simple chart is another story. This article will show you how to create a clustered column chart with your GA data, first the hard way with spreadsheets, and then the easy way using AI.

What is a Clustered Column Chart and Why Use It?

First, let's get on the same page. A clustered column chart (also called a grouped bar chart) is perfect for comparing multiple categories across a specific range. Instead of a single bar for each time period (like in a simple column chart), you have a "cluster" of bars representing different segments.

Imagine you want to see how your traffic from Google, Facebook, and Instagram has changed over the last three months. A clustered column chart would give you a group of three bars for each month - one for Google, one for Facebook, and one for Instagram - placed side-by-side.

This is incredibly useful for answering questions like:

• Which marketing channels are growing the fastest? You can visually compare the height of the bars for each channel from month to month.

• How did a specific campaign impact different platforms? See if the bars for your paid social channels grew more than organic search during your latest ad push.

• Are there seasonal trends affecting certain channels more than others? Compare performance across the same months in different years to spot patterns.

The side-by-side comparison makes it easy to spot trends, winners, and losers without having to cross-reference multiple reports. It turns confusing rows of data into a clear story.

The Traditional Way: Export, Pivot Tables, and Patience

Before AI tools became mainstream, creating a clustered column chart from Google Analytics data was a multi-step, manual chore that could easily use up your morning. Here’s a look at the process - it’s important to understand the friction that modern tools eliminate.

Step 1: Get Your Data Out of Google Analytics

Your journey starts inside the Google Analytics interface. You need to find the right data first.

1. Log in to your Google Analytics 4 property.

2. Navigate to Reports > Acquisition > Traffic acquisition.

3. Adjust the date range to the period you want to analyze (e.g., "Last 90 days").

4. This report defaults to grouping traffic by "Session default channel group." If you want to compare specific sources like Facebook vs. Instagram, you'll need to change the primary dimension to "Session source / medium."

5. You have your raw data, but it's not ready for a clustered chart yet. Find the "Share this report" icon (top right) and click Download File > Download CSV.

You’ve successfully extracted the numbers. Now the "fun" begins.

Step 2: Wrangle the Data in a Spreadsheet

Open your downloaded CSV in Google Sheets or Microsoft Excel. You’ll see a flat table of data, which isn’t formatted correctly for a clustered column chart. Your goal is to pivot this data so that your dates are the rows, your channels are the columns, and the values are the metrics (like sessions or users).

1. Clean the data: Your export likely includes rows you don't need, like a summary row at the bottom. Delete any unnecessary information, leaving just your list of sources and metrics.

2. Create a Pivot Table: This is where most people get tripped up.

   • Select all your data.

   • In Google Sheets, go to Insert > Pivot table.

   • In the Pivot Table editor, drag your date dimension (you might need to create a "Month" column from the date first) into the "Rows" field.

   • Drag your "Session source / medium" into the "Columns" field.

   • Drag your key metric, like "Sessions" or "Users," into the "Values" field.

3. Filter for what matters: Your pivot table will likely include every single traffic source. You’ll need to filter the columns to show only the ones you want to compare (e.g., google / organic, facebook.com / referral, and instagram.com / referral).

After all that wrangling, you should finally have a neat table that’s structured properly for charting.

Step 3: Build the Clustered Column Chart

With your data perfectly arranged, you can now create the visualization.

1. Select your entire pivot table.

2. Go to Insert > Chart.

3. Google Sheets or Excel will usually guess the chart type, but if not, select "Column chart." Critically, make sure it’s the clustered type, not the stacked type.

4. Format and stylize: Clean up the chart by adding a clear title (e.g., "Traffic Comparison: Google vs. Facebook vs. Instagram"), labeling your axes correctly, and adjusting the colors for better readability.

And you're done! The result is a great chart, but the process is slow, tedious, and prone to error. If you need to update it next week, you have to do it all over again. The data is also static - it’s a snapshot from the moment you exported it, not a live view of performance.

The Modern Way: Ask and You Shall Receive

AI-powered data analysis tools completely change the game. Instead of the multi-step manual process above, you simply connect your Google Analytics account once and then ask for the chart you need using plain English.

Here’s how this streamlined, modern approach works.

Step 1: Connect Your Google Analytics Account

First, you connect your GA account to an AI analytics platform. This is typically a one-time setup that takes just a few clicks. You authorize the tool with your Google account, select the GA property you want to use, and you're done. No messing with APIs, no exporting CSVs. The tool handles syncing your data in the background, keeping it always up-to-date.

Step 2: Describe the Chart You Want in Plain English

This is where the magic happens. Instead of building pivot tables, you just type what you want to see. Think of it as telling a data analyst exactly what to build for you.

You can start with a simple request. For example, you could ask:

Show me my website sessions from google, facebook, and instagram for the last 90 days.

The AI will likely generate a basic line or bar chart. Now you can refine it to get the clustered column chart you want.

Change this to a clustered column chart, grouped by month.

The AI understands the request and instantly rebuilds the visualization. You get a perfectly formatted clustered column chart comparing your traffic from those three sources, with each month showing a cluster of three bars. It’s done in seconds, not hours.

Step 3: Ask Follow-Up Questions to Dig Deeper

The conversation doesn't have to stop there. This is a massive advantage over the static spreadsheet method. You can continue drilling down to find more insights.

Let’s say the chart reveals that Instagram traffic is lagging. You can ask follow-up questions like:

• "What was the conversion rate for each of these sources?"

• "Break down the Google traffic into organic and paid."

• "Show me the same chart but for new users instead of sessions."

• "Which landing pages drove the most traffic from Facebook last month?"

Each question brings you a new visualization or a quick answer, allowing you to explore your data at the speed of thought. You can test hypotheses and uncover hidden trends without ever leaving the conversation or wrestling with another pivot table.

Best Practices for Your Charts

Whether you're building a clustered column chart manually or with AI, a few design principles will make your insights clearer and more impactful.

• Don't Over-Cluster: Clustered charts are great, but they get chaotic if you try to compare too many things at once. Stick to 3-4 categories per cluster for maximum readability. If you need to compare more, consider a different chart type or break the data into multiple charts.

• Use Clear Labels and a Title: Every chart should have a descriptive title that explains what the viewer is looking at. For example, "Monthly Ad Spend vs. Conversions (Q3)" is much better than "Chart 1." Make sure your X and Y axes are clearly labeled, too.

• Leverage Color Thoughtfully: Use distinct colors for each category to make them easy to tell apart. Be mindful of your brand's style guide and ensure there’s enough contrast for the chart to be readable.

Final Thoughts

Creating a clustered column chart with your Google Analytics data provides a powerful way to compare performance across different segments. While the traditional method requires manual data exports and cumbersome spreadsheet work, modern AI tools have transformed this process into a simple conversation.

At Graphed, we built our tool specifically to eliminate this kind of data busywork. You can connect your Google Analytics account in seconds and simply ask for the charts and reports you need - no more fighting with CSVs or pivot tables. We handle the live connection to your data, so you get instant, up-to-date visualizations that help you make better decisions, faster.