This study contributes to the firm growth debate by applying machine learning. We compare a prominent machine learning technique – random forest analysis (RFA) – to traditional regression in terms of their goodness-of-fit on a dataset of 168,055 firms from Belgium and the Netherlands. For each of these firms, we have one to six years of historical data involving demographic and financial information. The data show high variation in firm growth rates, which is difficult to capture with traditional linear regression (R2 in the range of 0.05–0.06). The RFA fares three to four times better, achieving a much higher goodness-of-fit (R2 of 0.16–0.23). RFA indicates that perhaps firm growth is less random than suggested by traditional regression analysis. Generally, given the modest selection of variables in our dataset, this demonstrates that machine learning can be of value to firm growth research.