Quantitative Sales Forecasting

Business: BTEC Level 3 National Extended Diploma, topic 6.1.5

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

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.

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.

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.

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.

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.

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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