Sales forecasting is like predicting the weather for your business. Just as a meteorologist uses past data and patterns to guess tomorrow’s temperature, a marketer uses historical sales, market trends, and seasonality to estimate future sales.
Imagine a grocery store that sells apples. If the store knows that apples are usually sold 30% more during the summer, it will order more apples before July. If it overestimates, the apples rot and the store loses money. If it underestimates, customers leave for competitors. Accurate forecasting keeps the shelves full and the profits healthy.
Seasonal Adjustment: Accounts for regular peaks and troughs.
When answering exam questions, always link your forecast to a business decision. For example, “If we forecast a 15% increase in sales, we should increase marketing spend by 10% to capture the market.” This demonstrates understanding of the *impact* of forecasting.
| Month | Actual Sales (units) | Forecasted Sales (units) | Error (%) |
|---|---|---|---|
| January | 1,200 | 1,150 | -4.2% |
| February | 1,350 | 1,300 | -3.7% |
| March | 1,500 | 1,550 | +3.3% |
Show how you calculate the forecast error: \$\text{Error} = \frac{\text{Actual} - \text{Forecast}}{\text{Actual}} \times 100\%\$. Discuss what a high error means for decision‑making.
Once a forecast is made, managers can answer questions like:
Remember: Forecast → Decision → Outcome. In your answer, illustrate this chain with a concrete example, and explain how the forecast influenced the decision and what the expected outcome was.