Quantitative Sales Forecasting (Edexcel A Level Business): Revision Note

Introduction to Quantitative Sales Forecasting

• The sales forecast is an important business planning tool

  • It provides an estimation of future sales figures using past data and considering predictable external factors

• Sales forecasts can be used to identify trends in product sales which can then be compared with the market as a whole

Main Methods used in Quantitative Sales Forecasting

Calculation of Time-series Analysis

Moving Averages

• Sometimes past sales data is too erratic for clear trends to be identified

• A moving average smoothes raw data and allows analysts to spot patterns even when sales are subject to seasonal variations

• Four-month or twelve-month moving averages are used where seasonality is a key factor in sales

Steps to calculate the moving average

• The moving total is calculated by adding together sales figures for a specified number of periods

  • E.g. A three-month moving total is calculated by adding the first three months, followed by the second three months, and the third three months, etc.

• The centred average is calculated by dividing the moving total by the specified number of periods

  • E.g. A three-month centred average is calculated by dividing the three month moving total by three

• A series of centred averages is known as the moving average

• When plotted on the same graph, the 3-month centred average provides a smoother curve, which makes extrapolation of the data for forecasting relatively straightforward

A comparison of raw sales data and 3-month centred average data

Interpreting Scatter Graphs

• Scatter graphs allow businesses to compare two variables, such as sales volume and advertising, to establish if there is any correlation between them

An example of a scatter graph showing the number of sales managers employed by a business and the volume of Items sold

Types of correlation

• A correlation exists where there is a relationship or connection between two variables 

• A positive correlation means as one variable increases, so does the other variable

  • A line of best fit that slopes upwards can be identified 

• A negative correlation means as one variable increases, the other variable decreases

  • A line of best fit that slopes downwards can be identified 

• No correlation means there is no connection between the two variables

  • It is not possible to identify a line of best fit
    

The main types of correlation between two variables

• Correlation does not always indicate a relationship or causation  between two sets of variables so businesses need to conduct research to establish whether a relationship exists as well as the strength of that relationship

• Where a line of best fit can be identified and when causation is determined, a business can extrapolate the data to make predictions around changes to either of the variables

  • E.g. extrapolation the line of best fit in the example below, the business could predict that employing seven sales managers would be result in likely sales of 46 units

• Extrapolation assumes that what has happened in the past will be the same as what will happen in the future

An example of a scatter graph with a line of best fit showing the number of sales managers employed by a business and the volume of items sold

Limitations of Quantitative Sales Forecasting

• Quantitative sales forecasting techniques are useful where the future is expected to reflect what has happened in the past

• It is especially effective in the short term

• In the longer term, uncertainties - especially in the external environment - can make simple extrapolation of past data unreliable

Examples of external factors that may influence the accuracy of the sales forecast 

• In many cases, the sales forecast can provide little more than an estimate of future performance 

  • As long as it is approximately accurate, businesses can use the sales forecast to plan resources such as staff, finance and production and to produce budgets

• Businesses can improve the accuracy of sales forecasts by

  • Conducting detailed market research

  • Employing experts with excellent market knowledge

  • Revising the sales forecasts frequently

  • Forecasting for the short- to medium-term