What are the primary methods used for sales forecasting?

The primary methods used for sales forecasting include time series analysis, regression analysis, and qualitative methods.

Time series analysis is a statistical technique that deals with time series data, or trend analysis. This method involves the analysis of data collected over time to identify patterns or trends. These patterns are then used to predict future sales. For instance, if a company's sales have been increasing by 10% every year for the past five years, it might forecast a similar increase for the next year. However, this method assumes that the factors influencing sales will remain consistent, which may not always be the case.

Regression analysis, on the other hand, is a statistical process for estimating the relationships among variables. It includes many techniques for modelling and analysing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. In sales forecasting, the dependent variable could be the sales and the independent variables could be factors like price, advertising spend, GDP, etc. The regression analysis helps in understanding how the typical value of the dependent variable changes when any one of the independent variables is varied.

Qualitative methods of sales forecasting are generally used when data are scarce, not available, or no longer relevant. Common types of qualitative techniques include: the Delphi method, market research, and historical life-cycle analogy. The Delphi method seeks to obtain a consensus estimate from a group of experts. Market research often involves surveys or interviews with customers or potential customers. Historical life-cycle analogy uses the sales history of similar products to forecast the sales of a new product.

Each of these methods has its strengths and weaknesses, and the choice of method often depends on the specific circumstances, including the availability and quality of data, the degree of accuracy required, and the time period of the forecast. It's also common for businesses to use a combination of these methods to create more accurate and robust sales forecasts.