Time series techniques examine sales patterns in the past in order to predict sales in the future. For example, with a trend analysis, the marketing executive identifies the rate at which a company’s sales have grown in the past and uses that rate to estimate future sales. For example, if sales have grown 3 percent per year over the past five years, trend analysis would assume a similar 3 percent growth rate next year.
A simple form of analysis such as this can be useful if a market is stable. The problem is that many markets are not stable. A rapid change in any one of a market’s dynamics is likely to result in wide swings in growth rates. Just think about auto sales before, during, and after the government’s Cash for Clunkers program. What sold the previous month could not account for the effects of the program. Consequently, if an executive were to have estimated auto sales based on the rate of change for the previous period, the estimate would have been way off.
The Cash for Clunkers program was an unusual situation; many products may have wide variations in demand for other reasons. Trend analysis can still be useful in these situations but adjustments have to be made to account for the swings in rates of change. Two common adjustments are the moving average, whereby the rate of change for the past few periods is averaged, and exponential smoothing, a type of moving average that puts more emphasis on the most recent period.