How to Forecast Revenue in Google Analytics

Thinking about future revenue can feel like gazing into a crystal ball, but it doesn’t have to be guesswork. Your Google Analytics account holds all the historical data you need to build a surprisingly accurate revenue forecast. This article will show you exactly how to leverage your GA4 data to project future earnings, step-by-step.

Why Forecasting is Easier Than You Think

You don't need a degree in data science to forecast revenue. The basic principle is surprisingly simple. Your future revenue depends on three key factors:

• How many people visit your site (Traffic)

• What percentage of them make a purchase (Conversion Rate)

• How much they spend on average (Average Order Value)

The core formula looks like this:

Traffic (Sessions) x eCommerce Conversion Rate x Average Order Value = Forecasted Revenue

Google Analytics tracks all of these metrics for you. Your job is to pull that historical data out of GA4, identify trends, and use those trends to project what will happen in the coming months. It might sound complex, but we'll break it down into manageable steps.

Step 1: Get Your GA4 Data in Order

Before you can forecast, you need a solid foundation of clean, reliable data. A forecast is only as good as the data it’s built on.

Confirm Your E-commerce Tracking is Working

This is the most critical step. If you aren't tracking purchases and revenue in Google Analytics, any forecast will be pure speculation. You can check this by going to Reports > Monetization > E-commerce purchases in your GA4 property. If you see data and revenue figures populating here, you’re good to go. If this report is empty, your top priority should be setting up e-commerce tracking correctly before moving forward.

Identify and Locate Your Key Metrics in GA4

Using our formula from above, we need to gather data on our three main pillars. Here’s where to find them in GA4:

• Traffic/Sessions: This tells you how many visits your site gets. You can find this metric across many reports, but the Reports > Acquisition > Traffic acquisition report is a great starting point. The primary metric here is Sessions.

• eCommerce Conversion Rate: This is the percentage of sessions that included a purchase event. GA4 calls this the Session conversion rate. You'll need to configure your key events so "purchase" is marked as a conversion.

• Average Order Value (AOV): This is the average revenue per transaction. In Universal Analytics, this was known as AOV. In GA4, the equivalent metric is called Average purchase revenue. You can typically find it alongside your revenue data in monetization reports.

Step 2: Export Your Historical Data to a Spreadsheet

While Google Analytics is fantastic for collecting data, it isn't designed for building forecast models. For that, we need to export the data to a more flexible tool like Google Sheets or Microsoft Excel.

For a basic forecast, we need monthly data for at least the last 12-24 months. This longer time frame is crucial for identifying seasonal patterns (like holiday rushes or summer slumps).

How to Export Your Data From GA4:

1. Go to the Reports > Acquisition > Traffic acquisition report.

2. Set the date range in the top right to your desired period (e.g., the last 24 full months).

3. Ensure the report is showing data grouped by 'Session default channel group' and change the timeline dropdown to show 'by Month'.

4. Click the “Share this report” icon (a small box with an arrow) in the top right corner.

5. Select “Download File” and choose to download as a CSV.

This will give you the raw data for 'Sessions' by channel. You will also need to get your revenue numbers. You can do this from the Reports > Monetization > Overview report, adjusting the dates and downloading the CSV in the same way.

Once you have your CSV files, combine them into a single spreadsheet. Your goal is to have a simple table showing each month with its corresponding Total Sessions, Total Transactions (or Conversions), and Total Revenue.

From these three data points, you can calculate your eCommerce Conversion Rate and Average Purchase Revenue for each month:

• Conversion Rate: Total Transactions / Total Sessions

• Average Order Value: Total Revenue / Total Transactions

Your spreadsheet should now look something like this:

Step 3: Analyze Trends and Project Future Performance

Now comes the fun part: finding the story in your data. With your monthly metrics laid out, you can start looking for patterns that will help you predict the future.

Look for Growth Trends and Seasonality

Create a simple line chart for each of your key metrics (Sessions, Conversion Rate, AOV). This visual aid will make trends leap off the page.

Ask yourself these questions:

• Growth: Is there a consistent upward or downward trend? For example, are your sessions growing by an average of 3% each month over the last year?

• Seasonality: Are there predictable peaks and valleys? Maybe your AOV always goes up in November during gift-giving season, or your traffic always dips in July when your target audience is on vacation.

Projecting Your Core Metrics

Now we will project values for the next 3, 6, or 12 months for our three key metrics.

1. Forecasting Sessions (Traffic)

This is often the most variable metric, as it's heavily influenced by your marketing efforts. A simple approach is to calculate your average month-over-month (MoM) growth rate from the previous year.

For example, if your traffic in the last 12 months grew by an average of 4% per month, you can apply that 4% growth rate to subsequent months. Always apply a sanity check here. Factor in any planned campaigns. If you're doubling your ad spend in Q4, a 4% growth projection is too conservative.

2. Forecasting Conversion Rate and AOV

Conversion Rate and Average Order Value tend to be more stable than traffic. Unless you are planning major website redesigns or pricing changes, using a historical average is often a safe bet.

A good starting point is to take the average Conversion Rate and AOV from the last 3 or 6 months. This gives more weight to recent performance. However, remember to adjust for seasonality. If you're forecasting for December and your conversion rate always jumps by 20% that month, factor that increase into your projection.

Step 4: Putting It All Together in Your Model

With projections for your three key metrics, you can finally build your revenue forecast. In your spreadsheet, add new rows for your future months. In the columns, manually enter your forecasted numbers for Sessions, Conversion Rate, and AOV based on your analysis.

Then, create a final column, "Forecasted Revenue," and use our original formula:

Forecasted Revenue = Forecasted Sessions * Forecasted Conversion Rate * Forecasted AOV

Your finished forecast might look something like this:

A More Advanced Approach: Channel-Specific Forecasting

A basic, site-wide forecast is a great start. But for more accuracy, you can build a separate forecast for each of your main traffic channels (e.g., Organic Search, Paid Search, Email, Social).

Different channels have different traffic trends, conversion rates, and even AOVs. For instance, your Paid Search traffic might grow rapidly due to budget increases, while your Organic Search traffic grows more slowly and steadily. By forecasting for each channel individually and then summing the results, you create a more nuanced and reliable overall business forecast.

Final Thoughts

Forecasting your revenue doesn't require complex software, it starts with understanding the key performance drivers already tracked in Google Analytics. By exporting your historical data and analyzing it for trends, you can build a powerful spreadsheet model that replaces guesswork with data-driven strategy.

The manual process of exporting, cleaning, and updating that spreadsheet data can be time-consuming, especially when you need to refresh your forecast each month. We built Graphed to remove this friction. Instead of juggling CSVs, you connect your Google Analytics account once and can then use simple, natural language to instantly pull up your performance trends for traffic, conversion rates, and revenue. It automates all the data-gathering drudgery so you can focus on building the forecast and making smarter decisions for your business.