PPC forecasting with Google Sheets and Vertex AI

Learn how to do PPC forecasts using Google Sheets and Vertex AI that will help you glean actionable insights and make data-backed decisions. The post PPC forecasting with Google Sheets and Vertex AI appeared first on Search Engine Land. - As the PPC landscape continues to evolve, having the ability to predict future campaign performance is invaluable.  This article will cover some of my favorite PPC forecasting techniques using Google Sheets and Vertex AI that paint a clearer picture of the future and empower clients with actionable insights.  While no tool or technique can provide a 100% accurate picture of the future, the methods outlined here can show us a glimpse into the potential trajectories of PPC campaigns. Google Sheets FORECAST function: The basics Google Sheets offers an easy-to-use and reliable forecasting function using the formula: =FORECAST(z, known_y values, known_x values) Where: z is the data point for which you want to predict a corresponding y-value. known_y's is the range of dependent data points (usually your past results or outcomes). known_x's is the range of independent data points (usually the variable you think might influence your outcomes). This function is a great tool if you only have two dimensions. However, it uses linear regression, which is fine for a quick forecasting sneak peek but nothing too advanced to account for external circumstances or other data sources. Let’s say you have historical data from the last year and want to forecast future budget predictions to have some numbers to plan with.  In this example, we have the current year’s sales data up to August and want to forecast future sales from September to December. If we visualize those forecasts, you’ll quickly see the drawbacks of using this method. The blue line represents the known sales data up to August, and the red line represents the predicted sales data.  The prediction is not more than a trendline, which might help to get a high-level look at something but is nothing compared to the blue line, which is basically how real business data will look like. Supercharging the Google Sheets FORECAST function To fix the issue of linear regression, there are multiple ways to approach the forecast formula with advanced methods.  Instead of just using the linear =FORECAST() function, you can add a little twist by adding trend data or other market predictions into the forecast formula, as in: =FORECAST()*3rdParty_Trend_Data You can grab trend data from public sources like Google Trends, Google Keyword Planner, Dataset Search by Google or industry reports (from PwC, EY, McKinsey, etc.) and export it into a CSV or any other format you’re used to working with.  Clean up those datasets to match your original sheet’s structure, like data on a day-to-day, week-to-week or month-to-month basis, Next, supplement the FORECAST function to get a more realistic prediction rather than just a straight line going up or downwards. In this example, we have used additional trend data, which shows an increased trend toward Q4 of the year. The numbers are, therefore, different from the predicted sales without trend data. If we visualize those new data, we can see that the trend data gives us better insights and more detail compared to a flat trend line. As a rule of thumb, it is almost always a good idea to support those forecasts with as much data as possible and supply data on more detailed timeframes like day-to-day or week-to-week. Get the daily newsletter search marketers rely on. “> “> “> Processing…Please wait. SUBSCRIBE See terms. function getCookie(cname) { let name = cname + "="; let decodedCookie = decodeURIComponent(document.cookie); let ca = decodedCookie.split(';'); for(let i = 0; i

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