Forecasting is an important tool for making informed business decisions. Regardless of the size and profile of a company, It helps the organization’s management anticipate trends in important business indicators, such as sales expectations or customer behavior. The forecast is a valuable asset but it requires specific skills and correct data. This article will help you better understand what forecasting is, how it works and how it can be an asset for your organization.
The data used for forecasting methods can either come from primary sources or secondary sources.
- Primary sources: Primary sources provide first-hand information, collected directly by the person or organization that is doing the forecasting. The data is usually collected from various questionnaires, focus groups, or interviews and, although all the information is difficult to gather and centralize, the direct way of acquiring the data makes primary sources the most trustworthy ones.
- Secondary sources: Secondary sources provide information that has already been gathered and processed by a third-party organization. Receiving the data in an organized and compiled way makes the forecasting process quicker.
Importance of Forecast
- Estimating the success of a new business venture
- Estimating financial necessities
- Helping managers make the right decisions
- Formulating effective plans for the future
- Helping an organization improve
Methods of Forecasting
There are four main forecasting methods that you can use to determine future values, revenues, expenses, costs, trends. They are:
- Straight-line method: This is the easiest forecasting method, both to learn and to follow. It’s typically used by financial analysts to determine future revenues based on past trends and figures.
- Moving average: This technique analyzes the underlying pattern of a dataset to estimate future values. The most widely-used types are the three-month and the five-month moving average.
- Simple linear regression: It is especially useful when analyzing the connection between different variables, to get a more accurate prediction.
- Multiple linear regression: It is mainly used for forecasting revenues, in situations where two or more independent variables are needed for a projection.