Forecasting sales using several qualitative or quantitative methods
Sales forecasts are an essential tool for the performance and profitability of the company. As part of the development of its business plan, the company can forecast the evolution of its sales volume and its potential turnover in order to make the right management decisions.
Thanks to the numerous sales forecasting methods, the company will be able to draw up a convincing commercial offer and deploy its marketing strategy. These qualitative or quantitative methods are based on the analysis of current or historical data. The choice of the method must be made according to the company's sector of activity, its installation on the market and the forecast horizon.
Demand variables that can affect the data
Most forecasting methods are data-driven. However, this data can be affected by variations in demand: if these variables are not taken into account in the forecasting methods, the information obtained will be incorrect. In order to identify the variables that may have an impact on the accuracy of the data, the company will have to choose the most appropriate sales forecasting model that will minimize forecasting errors.
On the one hand, one must consider the general trend of sales evolution. This trend materializes the evolution of the demands over the long term. On the other hand, depending on the time of the year, the demand and the sales volume of a product can vary: this is called seasonality of demand. Two other variables can have an impact on the data: the demand cycle and random variations.
Qualitative methods for forecasting product sales
If historical sales data is poor or non-existent, the company can opt for qualitative forecasting methods, based on the opinions of various stakeholders.
The opinion polling method
In the absence of historical data, especially at the beginning of an activity or at the launch of a new product, a first solution is to ask for the opinion:
- staff from the company's key departments (sales, marketing, etc.);
- a panel of experts (the Delphi method);
- consumers, through surveys.
At the end of all these surveys, a consensus on the forecast can be reached by aggregating the opinions of the participants. These questionnaires presented to several samples of individuals allow the company to estimate the distribution of the population's opinion on a larger scale.
The comparison or analogical method
At the beginning of a product's life cycle (when it is launched), the company can predict the sales volume of this product based on the sales data of a similar product. This neighboring product can be sold by the same company or by a third party, provided that the market structures are comparable. This method is more commonly used in sectors where the life of a product is limited, resulting in a poor data history.
The relevance of the first two forecasting methods is evaluated after the fact, using deviation indicators such as the standard deviation.
Market research allows:
- the analysis of the environment and the regulations of the company's sector;
- the analysis of the market in its entirety;
- the analysis of supply and demand;
- the understanding of the needs, expectations and habits of consumers.
Depending on their field of action, these market studies are based on the implementation of a commercial strategy or a marketing plan. Two market research models can be distinguished according to the approach with which the data is evaluated. The quantitative research model relies on figures and statistics to measure information.
As for the qualitative study, it is a study model that is carried out on a reduced sample of consumers, allowing to deepen the information collected especially during the first opinion polls of the consumers. The company can thus get an idea of future demand for a product and adapt its management processes.
Quantitative methods for forecasting product sales
While qualitative forecasting is subjective, quantitative forecasting is based on objective and precise calculations. These methods are used when the analysis of historical data gives enough information to build a successful sales forecasting model. Quantitative models consist of reproducing the structure of the past to forecast the future, by extrapolating past data.
Simple quantitative methods
Simple quantitative methods are based on the use of relatively simple mathematical formulas. Although they do not take into account the demand variables that can affect the data, they still allow for the first approximate forecasts to be made of possible variations in demand.
To do this, the company can use a mathematical formula specific to one of these three calculation methods:
- the extreme points method;
- the double mean method, known as Mayer's method;
- the least squares method.
The moving average method
The moving average method eliminates fluctuations in sales data due to seasonal and random variations. Moving averaging involves dividing a given period (year, quarters, months, etc.) into several periods and calculating each average. For example, sales can be averaged for each three-month period in a year. To forecast the next year's sales, simply average the averages for the same periods and so on. This forecasting model is best suited for a company that is already well established in its market and has a large data history.
Exponential smoothing methods
Exponential smoothing of a data set is based on the idea that the future depends more on the recent past than on the more distant past. In this sense, the smoothing method gives more weight to the most recent sales data. This model is most suitable for growing companies. Exponential smoothing methods include:
- simple exponential smoothing, only suitable for data series with no trend or seasonality;
- double exponential smoothing, suitable for data series with a trend;
- Holt's method, an improved version of double smoothing;
- Winters' method, adapted to data series with seasonality and trend.
Regression and correlation methods
The regression method consists of showing the evolution of a variable in relation to another variable (simple regression) or to several other variables (multiple regression). For example, the regression model can analyze the relationship between sales revenue (dependent variable) and advertising expenditure (independent variable).
The correlation expresses the strength of the relationship between the variables under study. The calculation of the correlation coefficient allows to measure the degree of correlation: if the correlation is strong, the sales forecast will be all the more precise and reliable.
All these qualitative and quantitative forecasting methods are not exclusive. By combining the methods, the company can limit the uncertainties and maximize the reliability of the forecast.
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