All you need to know about sales forecasting
What is sales forecasting?
What are the challenges of sales forecasting?
What data is needed for sales forecasting?
What are the factors influencing the sales forecast?
What are the components of a sales forecast?
How to develop a sales forecast?
Sales forecasting method: sales cycles
Sales forecasting method: intuition
The impact of sales forecast optimization on cash flow
Sales forecasting method: historical data
Sales forecasting method: multi-variable analysis
Sales forecasting method: the sales pipeline
Sales forecasting method: the mix of other methods
How to improve sales forecasts?
How to calculate a sales forecast?
How to forecast the sales of a product?
How to calculate monthly sales?
How to forecast in a business plan?
How to avoid pitfalls in sales forecasting?
Sales forecasting software
How to choose your sales forecasting software
Why use sales forecasting software?
Predictive models used by sales forecasting software
Inventory forecasting: the fundamentals
Why sales forecasting is important for inventory optimization
How to improve promotion forecasting
Principles and challenges of promotion forecasting
5 techniques for managing in-store promotions
6 techniques to manage your e-commerce promotions
How to make assortment forecasts?
What is assortment forecasting?
How to optimize the assortment forecast?
How to develop a good pricing strategy?
Pricing: how to develop promotional scenarios?
Managing margins and revenues with the right software
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.
Interested in learning more? Schedule a meeting today and see what Bamboo Rose Decision Intelligence can do for your brand!