Trying to predict what sales will be like next year, next month, next week, or even tomorrow is as much an art as it is a science. Because the forecasting process is often difficult and tedious, and because there’s no guarantee that a forecast will be precise, some companies and businesses don’t even bother to do it, resigning themselves to what Hamlet would call “the slings and arrows of outrageous fortune,” that the business world can certainly hash out.
Yet forecasting, if done properly, can be a great tool to reduce uncertainty about the future, as well as risk: it can make it easier to manage staffing and inventory levels; decide when to tap credit lines, step up marketing and sales efforts, or acquire new equipment; and/or determine whether a new product will be worth launching. In short, forecasting is intended to facilitate better planning.
These next few posts will introduce you to the different kinds of forecasting methods available, how to determine which method is most appropriate for your project, what information you need to generate the forecast, how to generate it, and how to test it for effectiveness.
This post serves as a roadmap for what is to come on Insight Central. For the next few posts, you can expect to see (in this order):
- A high-level discussion of the different categories of forecasting methods: time series, causal/econometric, judgmental, artificial intelligence, and other.
- A deeper discussion of the more popular forecasting methods: regression analysis, exponential smoothing, moving average, ARIMA (Box-Jenkins), as well as some of the judgmental methods like surveys, composite forecasts, scenario building, and simulation. Each discussion will also include a section on how to know if it’s one you should use.
- A discussion about the data you use to forecast; and how to prepare it for forecasting.
- How to determine if your forecast model is valid, reliable, and good for predicting.
- Some (nontechnical) case studies in which Analysights applied forecasting methods, and the results.
The next several posts will give you the pointers you need in order to forecast your business’ sales more effectively, and I believe you’ll find them to be informative, interesting, and exciting, not to mention beneficial to your top and bottom lines. Let the voyage begin!
Tags: business forecasting, causal modeling, econometric, Forecasting, forecasting methods, judgmental forecasting, predictions, predictive modeling, regression analysis, sales forecasting, scenario building, simulation, time series