Sales and demand forecasters have a variety of techniques at their disposal to predict the future. While most analysts will examine historical sales or other kinds of data as a guide, many forecasters rely heavily on judgment. There’s no question that judgment can (and probably should!) play a significant role in arriving at your final, consensus forecast–but statistical forecasting can offer a level of automation and insight that can substantially improve your forecast accuracy, particularly when you are producing large quantities of forecasts on a rolling basis.
This article covers two common approaches for forecasting sales using statistical methods: time series models and regression models. The advantage of these approaches is that they offer a lot of “bang for your buck”. On one hand, they are robust methods that can detect and extrapolate on patterns in your data like seasonality, sales cycles, trends, responses to promotions, and so on. On the other hand, they are easily accessible approaches, especially with the right tools.