How to Make a Waterfall Chart in Google Analytics with AI

A waterfall chart is one of the best ways to tell a clear story about how a starting value, like last month's website traffic, increased and decreased to become an ending value. It visualizes the cumulative effect of positive and negative changes, making it easy to see exactly what contributed to your net growth or decline. This article will show you exactly how to create a waterfall chart using your Google Analytics data, first through a manual process in Google Sheets and then through a much faster AI-powered method.

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What Exactly is a Waterfall Chart?

Imagine you're trying to explain to your team why website sessions went up by 500 this month. Simply saying "sessions grew by 500" is fine, but it doesn't explain the why. A waterfall chart breaks down that story piece by piece.

It starts with a column representing last month's total sessions. Then, it adds "floating" columns to show the positive contributions (e.g., +800 sessions from organic search, +300 from email) and the negative ones (e.g., -400 from a paused paid ad campaign, -200 from social media). The final column shows this month's total, connecting the entire story.

These charts are incredibly useful for analyzing Google Analytics data because they can clearly answer questions like:

• Which marketing channels contributed most to our user growth last quarter?
• How did changes in traffic from specific countries affect our overall sessions?
• What was the step-by-step impact of new marketing campaigns versus budget cuts on our total conversions?

It moves beyond a simple "before and after" comparison and shows the individual steps of the journey between those two points.

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The Problem: Google Analytics Lacks Native Waterfall Charts

For all of its powerful reporting capabilities, Google Analytics 4 doesn't offer a built-in waterfall chart visualization. You can create bar charts, line graphs, and scatter plots, but if you want to visualize that cumulative story of positive and negative changes, you have to look outside the platform.

Traditionally, this meant exporting your data to a spreadsheet program like Google Sheets or Microsoft Excel and painstakingly building the chart from scratch. While this method works, it's manual, time-consuming, and prone to little errors along the way. Your report is also static, as soon as new data comes in, your exported sheet is already out of date.

Fortunately, there are now two paths forward: the tried-and-true manual method, which is good to know, and a newer, AI-driven approach that automates the entire process.

Method 1: Manually Building a Waterfall Chart in Google Sheets

If you're comfortable working in spreadsheets and need to create a one-off report, this manual method is a solid option. It requires a bit of data prep and chart configuration, but it gives you full control over the final product.

Let’s walk through building a waterfall chart that visualizes the change in users by channel from one month to the next.

Step 1: Get Your Data from Google Analytics 4

First, you need to pull the right numbers. To do this, you’ll use the comparison feature in GA4.

1. Navigate to Reports > Acquisition > Traffic acquisition in your GA4 property.
2. In the top right, click the date range. Select your "this month" period (e.g., June 1-30) and activate the "Compare" toggle.
3. Select your "last month" period (e.g., May 1-31) as the comparison range and click Apply.
4. You will now see a table showing data for both periods, including a "% Change" column.
5. Click the "Share this report" icon (top right, next to the report title) and select Download File > Download CSV.

This gives you a raw data file to work with.

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Step 2: Structure Your Data in Google Sheets

Now, open a new Google Sheet and import the CSV you just downloaded. You’ll need to clean up and structure this data to work for a waterfall chart. The raw export isn't formatted correctly to build this chart type.

Create a new table with the following columns:

• Category: The items that contribute to the change (e.g., Start, Organic Search, Paid, Social, End)
• Base: An invisible bar that the positive or negative change will "sit" on. This is the trick to making the bars float.
• Increase: A positive contribution.
• Decrease: A negative contribution.

Here’s how to populate it:

1. Start Total: Your first row will be the "Start" value. Put your total users from last month's period here (let's say it was 10,000 for our example).
2. Changes by Channel: For each traffic channel (Organic, Paid, etc.), calculate the change in users between the two periods. The GA4 export gives you both values, so just subtract: (This Month Channel Users - Last Month Channel Users).
3. End Total: For your "End" row, calculate your total from this month.
4. The 'Base' Column Formulas: This is where the magic happens.

When you're done, your data should look something like this:

Note: Start and End should have their values in a column designated as a full bar, not Increase/Decrease. For simplicity in the chart builder, you can put them in the 'Increase' column and then special-format those bar colors later.

Step 3: Create and Customize Your Waterfall Chart

With your data perfectly structured, creating the chart is straightforward.

1. Select your data table (cells A1 through D7 in our example).
2. Go to Insert > Chart.
3. In the Chart Editor, select Chart type > Stacked column chart. Google Sheets will generate a messy-looking stacked chart initially. Don’t worry, now we’ll fix it.
4. Under the "Series" section of the editor, click on your "Base" series. Change its fill color to "None". This makes the bars that lift up your data invisible, creating the floating effect.
5. Customize your "Start" and "End" bars to have a different color (like gray or blue) than your "Increase" (green) and "Decrease" (red) bars. You can do this by double-clicking on an individual bar in the chart to format it.
6. Finally, you may want to enable "connector lines," which show the relationship between each column. In the Customize > Series section, find and check the box for stacked column series options.

After a few minutes of formatting, you’ll have a clean waterfall chart. It’s effective, but the manual process of exporting, cleaning, structuring, and formatting takes time and is prone to slip-ups. If you have to do this every week, it quickly becomes a major time sink.

Method 2: Using an AI Data Analyst to Create Waterfall Charts Instantly

What if you could skip all the spreadsheet work? That’s where AI-powered analytics tools come in. Instead of you manipulating an exported CSV, you connect your Google Analytics account once and then just ask for the chart you want in plain English.

How it works: A conversation with your data

Modern AI-native tools don't have static dashboards with pre-defined widgets. Instead, they leverage a conversational interface. You simply write a prompt describing the chart you want to see.

A typical workflow might look like this:

1. Connect your data source: You simply authorize a secure, read-only connection to your Google Analytics property. This only takes a few clicks.
2. Ask a Question in Plain English: You type in a prompt just like you would if you were talking to an analyst from your team. Since the AI has a deep understanding of Google Analytics' structure and metrics, you don’t need to know the exact technical terms or complicated data queries to create a report.
3. Get an instant visualization: The AI interprets your request, automatically queries the live data from GA, and renders the exact visualization you asked for in a matter of seconds.

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Example Prompts for a Waterfall Chart

Instead of manually preparing data tables, you would just use prompts like these:

• "Create a waterfall chart showing the change in users by Sessions default channel grouping from last period to this period."
• “Build me a waterfall report visualizing our change in revenue last month, starting with the total from the month before."
• "Make a waterfall chart from Google Analytics to show the contribution of each country to our total session growth from Q1 to Q2."

The AI model understands terms like "last period" or "revenue," and it can instantly pull the correct data and set up the complex waterfall structure without needing a single formula. Best of all, the chart is connected to live data. There's no more manual exports - you're always looking at the freshest numbers as the chart updates automatically.

The Benefits of the AI Approach

• Speed and efficiency: What takes 45 minutes or more in Google Sheets takes less than 30 seconds with an AI tool. You get to spend your time analyzing insights, not fighting spreadsheet formulas.
• Accuracy and reliability: By automating the data wrangling process, you eliminate human error. The AI handles all the complex calculations, ensuring the chart is always accurate.
• Data access for everyone: You no longer have to be a spreadsheet pro to get the reports you need. Anyone in your team, from marketers to executives, can get answers to their data questions simply by asking. This makes your entire organization more data-driven.

Final Thoughts

Waterfall charts are a uniquely powerful tool for explaining the "why" behind changes in your Google Analytics KPIs. Whether you choose the hands-on control of the manual Google Sheets method or the speed and convenience of an AI-powered platform, adding this visualization to your reporting toolkit will level up your ability to tell compelling data stories.

Moving away from tedious, manual reporting is exactly why we built Graphed. We wanted to eliminate the busywork of exporting CSVs and fighting with chart builders. With our platform, you can connect Google Analytics in minutes and ask in plain English for the exact report you need - like a "waterfall chart of user growth by channel" - and get a live, interactive visualization instantly. This helps you get straight to the insights so you can spend your time acting on data instead of just preparing it.