6.1.5

Quantitative Sales Forecasting

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Quantitative Sales Forecasting

Quantitative sales forecasting helps businesses predict future sales based on historical data.

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Time-series analysis

  • Time-series analysis refers to the use of past data and trends to forecast and predict future trends.
  • Time-series analysis allows businesses to use moving average calculations to forecast future sales based on historical data.
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Moving average calculation

  • Three period moving averages allow a business to use three sets of (usually annual) data to calculate an average for future predictions.
    • Three period moving averages reduce the impact of a single anomaly on future predictions as an average from three years is calculated.
  • Four quarter moving averages allow a business to use data from four quarters (three month periods) to calculate an average sales figure.
    • Four quarter moving averages increase calculation accuracy because they minimise the impact of unusual or seasonal sales figures.
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Correlations

  • Correlations can be used by marketing departments to examine the relationship between two variables.
  • Scatter graphs can be used to show correlation and allow businesses to extrapolate data. Extrapolation involves using past data trends to predict future performance.
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Types of correlations

  • Positive correlation occurs when an increase in one variable results in an increase in the other variable.
    • For example, if increasing advertising spending results in an increase in sales, there is a positive correlation and a business is likely to raise its advertising budget.
  • Negative correlation occurs when an increase in one variable results in a decrease in the other variable.
    • For example, if increasing advertising spending results in a decrease in sales, there is a negative correlation, and a business is unlikely to raise its advertising budget.
  • There is no correlation if a relationship between two variables cannot be determined.
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Graphs

  • A line of best fit can be used on scatter graphs to represent data and identify the general relationship between plotted points of data.
    • For example, if the temperature increases, total customer spending on ice cream may increase.
    • This is a positive correlation and the general trend can be highlighted using a line of best fit.
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Disadvantages of quantitative techniques

  • Changes in the external environment (political, environmental, social, technological, legal and economic) can impact the business’ future performance.
    • This is not reflected in the past performance data which is used to extrapolate.
  • Changes in the internal environment (culture, leadership, financial performance) can impact the business’ future performance.
  • Quantitative sales forecasting can be time-consuming and complex.

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