3.3.1 Quantitative Sales Forecasting

• Why is Sales Forecasting important?

  • Provides estimation of future sales figures using past data & considering predictable external factors.
  • Can be used to identify trends in product sales - then be compared with the market as a whole
• Moving Averages

  • Series of averages calculated from successive segments of series of data.
  • Averages smooth data so trends are easily identified.
• Extrapolation

  • Prediction of future sales from past data
  • Done by extending line of best fit
• Correlation

  • Where there is a link between two variables there is a correlation.
  • Correlations may be positive or negative.
• • A moving average smoothes raw data and allows analysts to spot patterns even when sales are subject to seasonal variations
• When is 4 or 12 month moving averages used?

  • where seasonality is a key factor in sales
• How is moving averages calculated?

  A) total

  B) centred

  
• How is moving averages calculated?

  • adding together sales figures for a specified number of periods
  • The centred average is calculated by dividing the moving total by the specified number of periods
• • A series of centred averages is known as the moving average
• • Scatter graphs allow businesses to compare two variables, (sales volume and advertising), to establish if there is any correlation between them
• 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
• 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
• factors affecting accuracy of QSF

  A) seasonality

  B) competition

  C) publicity

  D) market

  E) legislation

  
• Limitations of QSF

  • Only effective in short term.
  • Only useful if future is expected to reflect what happened in the past.
• • 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
• How do businesses improve accuracy of QSF?

  • Conducting detailed market research
  • Employing experts with excellent market knowledge
  • Revising the sales forecasts frequently
  • Forecasting for the short- to medium-term
• Advantages of using extrapolation

  • Simple method
  • Not much data required
  • Quick & cheap
• Disadvantages of using extrapolation

  • Unreliable if there's significant fluctuations in historical data.
  • Assumes past trend will follow into future - unlikely in competitive environment.
  • Ignores qualitative factors.