How to Forecast Sales in Google Analytics

Trying to predict future sales can feel like guesswork, but your Google Analytics 4 data holds powerful clues about what's to come. With a little setup, you can move from reacting to past performance to proactively anticipating future revenue. This tutorial breaks down how to use GA4’s built-in predictive features and a simple manual method to create your own sales forecast.

Setting the Stage: Why Use Google Analytics for Forecasting?

Your website and email list aren't just a brochure, they're a living ecosystem of user behavior. Every click, session, and purchase is a data point that signals intent. While it’s not a flawless crystal ball, using GA4 for sales forecasting helps you build a data-driven foundation for your business strategy instead of relying purely on gut feelings.

A good forecast helps you answer critical questions like:

Should we increase our ad spend next quarter?
Do we need to order more inventory for a specific product line?
Are we on track to hit our annual revenue goals?

Instead of guessing, you can create an educated hypothesis based on the trends and patterns already happening on your site.

What GA4 Can (and Can't) Predict

It's important to start with realistic expectations. Google Analytics uses machine learning models to analyze your historical data and predict future user actions. However, these models have clear limitations.

What GA4 is great at:

Identifying User Segments: It can analyze a user's behavior (pages visited, items viewed, frequency of visits) to predict the likelihood that they will make a purchase or stop visiting your site (churn) within the next 7 days.
Predicting Revenue from Existing Users: GA4 can forecast the revenue you can expect from active users over the next 28 days. This is based on their past purchase behavior.
Extrapolating from Trends: The platform is excellent at spotting patterns in your traffic and conversion data, which you can use to manually project future performance.

What GA4 struggles with:

External Factors: The models know nothing about your upcoming summer sale, the big PR feature that’s about to drop, or a new competitor who just launched. Your forecast will not account for these external marketing activities or market shifts.
Small Data Sets: Machine learning needs a lot of data to make reliable predictions. If your site has low traffic or infrequent sales, GA4’s predictive metrics may not be available or accurate for your account.
Sudden Changes: If your business model suddenly pivots or a product goes viral overnight, historical data becomes less relevant, and the forecast's accuracy will temporarily decrease.

The Building Blocks for a GA4 Forecast

Before you can start forecasting, you need to ensure GA4 has the right information. There are two essential ingredients: proper tracking and enough data.

Step 1: Ensure Your E-commerce Tracking is Set Up Correctly

This is the absolute baseline. To forecast sales, Google needs to know when sales are happening. You must have e-commerce tracking configured to send a purchase event every time a customer completes a transaction.

Crucially, this event needs two specific parameters:

currency: The three-letter currency code (e.g., "USD," "EUR").
value: The total numerical value of the purchase (e.g., 99.99).

Without this data, GA4 has no concept of revenue and cannot predict it.

Step 2: Check If You Meet the Data Thresholds

For Google's predictive models to activate, your property needs to meet minimum data requirements over a recent 28-day period. The model needs to "train" itself on the behavior of both purchasers and non-purchasers.

Specifically, you need:

At least 1,000 returning users who triggered the purchase event.
At least 1,000 returning users who did not trigger the purchase event.

These thresholds must be maintained consistently for the predictive features to remain active. If you dip below this number, the features may become unavailable until you meet the requirements again.

How to Use GA4's Predictive Metrics

If your property meets the data thresholds, GA4 automatically enriches your data with three predictive metrics:

Purchase probability: The likelihood an active user in the last 28 days will make a purchase in the next 7 days.
Churn probability: The likelihood a recently active user will not visit your site again in the next 7 days.
Predicted revenue: The expected revenue from all purchase conversions within the next 28 days from an active user.

You can't see these metrics in standard reports. You access them primarily by creating Predictive Audiences and using Explore Reports.

1. Create Predictive Audiences

The easiest way to leverage these metrics is by building an audience. GA4 even suggests some for you. Navigate to Admin > Audiences > New audience. On the creation screen, check under "Predictive" in the Suggested Audiences section.

You'll see options like:

Likely 7-day purchasers: All users whom Google predicts are most likely to buy in the next week. These are your 'hot leads'.
Likely 7-day churning users: Active users who are not expected to visit again in the next week. This is an ideal segment for re-engagement campaigns.
Predicted 28-day top spenders: Users predicted to generate the most revenue in the next 28 days. This is your VIP segment.

Creating these audiences lets you target these specific groups directly in your Google Ads campaigns. For forecasting, you can analyze them in Explore reports to see which channels bring in the most valuable, high-intent users.

2. Analyze Forecasts in an Explore Report

To see the raw predicted revenue numbers, you'll need to build a free-form exploration. This allows you to combine dimensions and metrics in a custom table.

Here’s how to build a basic report:

Navigate to the Explore section in the left-hand navigation and select Blank or Free form.
Give your exploration a helpful name, like "Predicted Revenue by Channel."
In the Variables column on the left, click the '+' sign next to 'Dimensions'. Search for and import Session default channel group.
Next, click the '+' sign next to 'Metrics'. Search for and import Predicted revenue and Total users.
Drag Session default channel group from the Variables column to the Rows section in the 'Tab Settings' column.
Drag Total users and Predicted revenue to the Values section.

The table that appears will now show you a powerful view: your marketing channels ranked by total projected revenue. This report directly answers, "Which of my channels is forecasted to drive the most sales in the next 28 days?" You can now see if the organic search traffic is more valuable than your paid search traffic from a future-looking perspective.

A Manual Forecasting Method for Any GA4 Account

What if you don't meet the data thresholds for GA4’s predictive features? You’re not out of luck. You can still create a very effective sales forecast using a classic marketing formula and historical data from your own standard reports.

The magic formula is simple:

Here’s how to find these numbers and put them together.

Step 1: Find Your Historical Averages

Set the date range in GA4 to a stable, representative period, like the last 90 days. Avoid periods with major sales events unless that's what you are forecasting for.

Gather Your Metrics:

Find your average traffic: Go to Reports > Acquisition > Traffic acquisition. Find the Users metric. To get a monthly average, divide this total by 3.
Find your average conversion rate: In the same report, find the Session conversion rate metric. Be sure your primary conversion event is set to purchase.
Find your average order value (AOV): Go to Reports > Monetization > E-commerce purchases. To calculate AOV manually, divide Total revenue by Total purchasers.

Step 2: Project Future Traffic Growth

Next, determine a realistic increase for your traffic. Look at your month-over-month to year-over-year user growth rate. If your user count grew from 10,000 to 12,000 over the past month, that's a handsome 20% growth rate. You can also base this on planned marketing efforts. For example, if you're doubling your ad spend, you might project a more aggressive traffic increase.

Let's use a conservative 10% for our example.

Imagine last month you had 20,000 users. Your calculation would be:

20,000 * (1 + 0.10) = 22,000 Forecasted Users

Step 3: Put It All Together

Now, plug your historical averages and your traffic forecast back into our simple core formula. Let's say your historical data showed:

Average Conversion Rate: 1.5%
Average Order Value: $75

Your sales forecast for the next month would be:

22,000 (Forecasted Users) * 0.015 (Conversion Rate) * $75 (AOV) = $24,750

You now have a logical, data-backed sales forecast. You can repeat this calculation by channel to get more granular, allowing you to fine-tune your channel-specific strategies.

Final Thoughts

Forecasting sales in Google Analytics is a powerful habit that shifts you from being reactive to proactive. By combining GA4's built-in predictive user segmentation with sound manual trend analysis, you can develop an educated outlook on your future performance, turning raw data into an actionable strategic plan.

While pulling this data together in GA4 and managing calculations in a separate spreadsheet certainly works, it can quickly become a time-intensive process. We built Graphed to remove that exact friction. Instead of manually searching reports for metrics, you can connect your Google Analytics account and simply ask, "What's our month-over-month revenue growth?" or "Build me a dashboard forecasting sales for next quarter based on a 15% traffic increase." We create the live visuals and reports for you, so you can spend your time acting on insights, not just finding them.