How to Do Predictive Analysis in Google Sheets

Predictive analysis lets you use your historical data to make educated guesses about the future, and you don't need a data science degree to get started. Google Sheets has powerful, built-in functions that can help you forecast future sales, website traffic, or inventory needs with just a few clicks. This guide will walk you through how to perform simple predictive analysis directly within your spreadsheets using formulas you already have access to.

What Exactly is Predictive Analysis?

Predictive analysis is the practice of using existing data to identify trends and patterns to predict future outcomes. Think of it as looking at your business's track record to create a reasonable forecast of what's to come. It’s not about finding a magic crystal ball, it's about making data-informed decisions instead of relying purely on gut feelings.

For example, you could:

• Forecast next quarter's sales based on the previous two years of data.

• Estimate how much website traffic you'll get next month.

• Predict how many support tickets your team will receive during a holiday period.

You don’t need expensive BI software or complex statistical models for this. Google Sheets is an excellent starting point because it's free, collaborative, and comes equipped with functions specifically designed for forecasting. It democratizes data analysis, allowing anyone to turn a simple spreadsheet of past performance into a forward-looking tool.

Step 1: Get Your Data Ready for Prediction

The quality of your prediction is entirely dependent on the quality of your data. The principle of "garbage in, garbage out" is especially true here. Before you can use any formulas, you need to gather, structure, and clean your dataset.

Gather Your Historical Data

Your first step is to collect the data you want to analyze. Time-series data - data collected at regular intervals - is ideal for a lot of business forecasting. This could be daily user registrations, weekly revenue, or monthly sales figures.

You'll need at least two columns:

• The Independent Variable (X-axis): This is your time component. It should be a series of numbers that increases consistently, like months (1, 2, 3), years (2021, 2022, 2023), or specific dates.

• The Dependent Variable (Y-axis): This is the value you want to predict. It could be sales, website sessions, subscribers, or any other key metric you track.

Structure Your Data in Google Sheets

Organize your data in a clean, simple table. Put your time variable in Column A and the value you're tracking in Column B. This simple structure makes it easy for Google Sheets' functions to understand the relationship between the two.

For example, here's a sample dataset for monthly website sessions over a year:

Example Dataset: Monthly Website Sessions

Clean Your Data

Manually scan your data for any obvious mistakes. Check for typos, empty cells, or entries that don’t make sense. If there are gaps in your data, you might need to find the missing values or remove the period to avoid skewing your forecast. Also, be mindful of outliers - a number that is abnormally high or low. For instance, if you had a one-time viral blog post that drove 100,000 sessions in a single month, that outlier could throw off your prediction based on linear growth. You may need to either adjust that value to be closer to the average or remove it from the dataset used for your forecast.

Step 2: Use the FORECAST Function for a Single Prediction

The simplest way to predict a future value in Google Sheets is with the FORECAST function. This function uses linear regression to predict a single future point based on the straight-line trend of your existing data.

Understanding the FORECAST Function Syntax

The formula looks like this:

=FORECAST(x, data_y, data_x)

Let's break down each component:

• x: The new x-value (the future time point) for which you want to predict a y-value. In our example, if we want to predict the traffic for the next month, our 'x' would be 13.

• data_y: The range of cells containing your known dependent values (your historical traffic numbers). In our table, this would be B2:B13.

• data_x: The range of cells containing your known independent time values (months 1 through 12). In our table, this would be A2:A13.

Step-by-Step Forecasting Example

Using the monthly website session data from above, let's predict the traffic for the 13th month.

1. Click on an empty cell where you want your prediction to appear.

2. Type the formula, filling in the correct cell ranges. If your data is in cells A2:B13, the formula would be:

=FORECAST(13, B2:B13, A2:A13)

When you press Enter, Google Sheets will analyze the trend from months 1-12 and calculate the expected sessions for month 13. Based on our sample data, it would return a value around 17,541. This tells you that if the current growth trend continues, you can expect about 17,541 sessions next month.

You can easily extend this to forecast several more periods. Just create new rows for months 14 and 15, and drag your formula down. Be sure to use absolute cell references ($B$2:$B$13 and $A$2:$A$13) for your data ranges if you plan to drag the formula, so the historical data reference doesn't shift.

Step 3: Visualize Your Forecast with a Line Chart

A number is good, but a chart is even better. Visualizing your data and forecast helps you instantly see the trend and check if the prediction seems reasonable.

Here’s how to create a chart:

1. Add a few forecasted data points to your sheet (e.g., for months 13, 14, and 15).

2. Highlight all your data, including the column headers and your newly forecasted values. (For our example, you'd select cells A1 through B16.)

3. Click on Insert > Chart from the top menu.

4. Google Sheets will usually default to a Line Chart, which is perfect for this. If not, select it from the Chart Type dropdown in the chart editor sidebar.

You will instantly see a chart showing your solid blue line of historical data, which then continues as a projected line for your future months. This visual puts the numbers into context and makes your forecast easy to understand at a glance.

Step 4: Use TREND to Predict Multiple Values at Once

The FORECAST function is great for one value at a time, but if you want to project an entire quarter or year's worth of data, the TREND function is more efficient. It's an array function, meaning it can return multiple values across a range of cells.

Understanding the TREND Function Syntax

The syntax for TREND is similar but more powerful:

=TREND(known_data_y, known_data_x, new_data_x)

• known_data_y: Your range of known values (e.g., B2:B13).

• known_data_x: Your range of known time periods (e.g., A2:A13).

• new_data_x: The new range of future time periods you want to predict (e.g., A14:A16 for months 13, 14, and 15).

Step-by-Step TREND Example

Let’s predict our website sessions for the next three months (13, 14, and 15) all at once.

1. First, add the numbers 13, 14, and 15 in cells A14, A15, and A16, respectively. This is your new_data_x range.

2. In cell B14, type the TREND formula:

=TREND(B2:B13, A2:A13, A14:A16)

Once you press Enter, Google Sheets will automatically populate cells B14, B15, and B16 with the predicted session counts for all three future months. This saves you from having to write individual formulas for each future point.

Bonus: Quick Predictions with Chart Trendlines

If you need an even faster, more visual way to see a forecast, you can add a trendline directly to your chart. This method is excellent for presentations or quickly assessing a trend without writing any formulas.

1. Create a line chart using only your historical data (e.g., A1:B13).

2. Double-click the chart to open the Chart editor pane.

3. Go to the Customize tab.

4. Expand the Series section.

5. Scroll down and check the box for Trendline.

A dotted line will appear, extending past your existing data to show the projected path. For even more insight, you can customize the trendline:

• Label: Change this to "Use Equation" to display the linear regression formula (like y = mx + b) on the chart. You can use this formula to manually calculate forecasts.

• Show R²: Checking this box shows the "R-squared" value. This number (from 0 to 1) tells you how well the trendline fits your data. A value closer to 1 means your data points are very close to a perfect straight line, making the forecast more reliable.

Important Limitations and Best Practices

These functions provide simple, powerful ways to forecast, but it's important to be aware of their limitations.

• Predictions aren't guarantees. A forecast is an educated guess. Unexpected market changes, new competitors, or a successful new marketing campaign can all change the trajectory of your business.

• Models assume linear growth. FORECAST and TREND are built on the assumption that your growth follows a straight line. If your data has strong seasonality (e.g., sales spiking every November) or is growing exponentially, these simple models will be less accurate.

• Data quality is everything. An incomplete or messy dataset will lead to a poor forecast. Always take the time to clean and review your numbers first.

• Keep predictions short-term. Forecasting one or two periods ahead based on 12 months of data is reasonable. Using that same data to predict the outcome three years from now is unlikely to be accurate.

Final Thoughts

Getting started with predictive analysis doesn't have to be complicated. By using the built-in FORECAST and TREND functions in Google Sheets, you can turn your historical data into actionable, forward-looking insights. These tools empower you to make smarter, data-driven decisions for your business, all within a simple spreadsheet.

While forecasting in Sheets is a great step, it can still require a lot of manual work downloading CSVs and wrangling formulas. We created Graphed to streamline this entire process. Instead of building reports by hand, you can connect sources like Google Analytics, Shopify, and your CRM in a few clicks. Then, you can simply ask in plain English, "Show me a forecast of our Shopify sales for next quarter," and Graphed builds a live dashboard that answers your question instantly. It's the fastest way to get from data to insights without fighting spreadsheets.