Gabriel Gutierrez, Head of Finance at Teads, joined us as one of our amazing speakers at the FP&A Summit in San Diego. This article has been adapted from Gabriel’s session on predicting revenues using machine learning within FP&A.

Did you know that you can predict revenue with machine learning?

It’s a fascinating concept that FP&A teams can leverage to gain a significant advantage.

By analyzing historical sales data, marketing efforts, economic indicators, and even customer sentiment, machine-learning models can identify patterns and trends that are difficult for humans to see.

But how can FP&A teams utilize this type of technology in their roles?

Keep reading to find out.

Why does FP&A exist?

Before I get into the future of FP&A, I think it’s important to start at the beginning and think about why FP&A exists.

In my opinion, FP&A’s main role is to help businesses make better decisions.

We’re in charge of long-term planning, monitoring market trends, and understanding in-depth business performance. Based on those things, we provide insights and recommendations for future action.

FP&A is essential in shaping the company's long-term strategy.

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How data leads to better predictions and decisions

The most important part of our role in FP&A is to collect and analyze data. We can collect business data and base our predictions on it. For example, we can explore a large dataset to create reports (variance analysis) and make accurate predictions.

With those predictions, we can create budgets, and monthly forecasts, and share those insights with different stakeholders.

And then we would have the business partnering side of FP&A. This is when we step up as strategic partners to the business and converse with different heads of departments and even the C-Suite.

Every budget and forecast we create influences the future of the company. Once one cycle ends, another begins.