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

Method

Explanation

Moving Averages

  • A series of averages calculated from successive segments of a series of data

  • These averages smooth data so that trends may be more easily identified

Extrapolation

  • The prediction of future sales from past data

  • Extrapolation can often be done simply by extending a line of best fit

Correlation

  • Where there is a link between two variables there is a correlation

  • Correlations may be positive or negative

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 for calculate the moving average: Step 1 is calculating the moving total, and Step 2 is calculating the centred average.

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

Worked Example

RJ Inflatables is a manufacturer of novelty celebration balloons. Its monthly sales from January to July are shown in Table A.

Rachel Jameson, the managing director, is concerned that sales are declining but is struggling to identify a trend with the sales data she has available. Rachel’s financial administrator has suggested using a moving average so that she can forecast future sales with greater accuracy.

Table A

Table showing monthly sales in pounds. January: 42,000; February: 51,000; March: 45,000; April: 33,000; May: 47,000; June: 30,000; July: 45,000.

Calculate a three-month moving average using RJ Inflatables January to July sales data. (6)

Step 1 - Calculate a three-month moving total of sales for each group of three months

Table showing monthly sales and three-month moving totals in pounds for January to July. Sales figures vary between £30,000 to £51,000.

Step 2 - Calculate the three-month centred average  for each group of three months

Table showing monthly sales in pounds, three-month moving totals, and centred averages from January to July, with calculations included alongside.
  • 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

Graph showing raw sales and 3-month centred average from January to July. Blue line for raw sales, red line for centred average, with a key at the bottom.

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

Scatter graph showing the relationship between the number of sales managers employed and the volume of sales, with data points marked as red crosses.

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

Three graphs showing positive, negative, and no correlation between variables A and B, with data points and trend lines, labelled accordingly.

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

Graph shows a linear relationship between number of sales managers and volume of sales, with data points plotted and a trend line.

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

Examiner Tip

When drawing a line of best fit, you should try to include as many data points above the line as below the line.

Watch out for outlying data - if there is more than one outlier above the line, adjust your line of best fit upwards. Similarly, if there is more than one outlier below the line, adjust your line of best fit downwards. Just one outlier should not influence your line of best fit.

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

Flowchart depicting external factors affecting sales forecasts: seasonality, competition, publicity, market changes, and legislation changes.

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

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