Quantitative Sales Forecasting (Edexcel A Level Business)

• 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

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

• 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

• 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