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.
Preparing forecasts using data that contain one or more unusually large or small demand periods can be challenging. Depending on your forecasting approach, these “outliers” can have a significant impact on your forecasts. This article surveys three different approaches to forecasting data containing unusual demand periods, discusses the pros and cons of each and recommends when it is best to use each approach.
The recording of our recent educational one-hour Webinar is now available for viewing on-demand
This session demystifies regression modeling, demonstrating how these models can provide insight into your data and why they often yield more accurate results than alternative forecasting methods. Learn:
- When to apply regression models
- How to build and diagnose the models
- How to use leading indicators, lagged vari¬ables, Cochrane-Orcutt terms and “dummy” variables
- Best practices for applying regression models
You can register now for our next live Webinar How to Effectively Combine Judgment with Statistical Forecasts
Forecasting is a dynamic and interdisciplinary field that involves a multitude of people, processes and techniques. Forecasters are constantly building on their current pool of knowledge in order to improve their processes, drawing on whatever resources they can find. A forecaster’s best resource, however, is often other forecasters—but it can be difficult to share information if everyone is speaking a different language. For this post we’ve compiled and defined a list of terms that every forecaster should be familiar with. Continue reading