If you want to forecast the future — next quarter’s sales, for example — you need to get a handle on what’s happened in the past. So you always start with what’s called a baseline (that is, past history — how many poppy seeds a company sold last year, where the market futures wound up last month, what the temperature was today).

Unless you’re just going to roll the dice and make a guess, you need a baseline for a forecast. Today follows yesterday. What happens tomorrow generally follows the pattern of what happened today, last week, last month, last quarter, last year. If you look at what’s already happened, you’re taking a solid step toward forecasting what’s going to happen next.

You can choose from several different forecasting methods, and it’s here that judgment begins. The three most frequently used methods, in no special order, are moving averages, exponential smoothing, and regression.

## Method #1: Moving averages

Moving averages may be your best choice if you have no source of information other than sales history — but you do need to know your sales history. The underlying idea is that market forces push your sales up or down.

By averaging your sales results from month to month, quarter to quarter, or year to year, you can get a better idea of the longer-term trend that’s influencing your sales results.

For example, say you find the average sales results of the last three months of last year — October, November, and December. Then you find the average of the next three-month period — November, December, and January (and then December, January, and February, and so on). Now you’re getting an idea of the general direction that your sales are taking.

## Method #2: Exponential smoothing

Exponential smoothing is closely related to moving averages. Just as with moving averages, exponential smoothing uses past history to forecast the future. You use what happened last week, last month, and last year to forecast what will happen next week, next month, or next year.

The difference is that when you use smoothing, you take into account how bad your previous forecast was — that is, you admit that the forecast was a little screwed up. (Get used to that — it happens.) The nice thing about exponential smoothing is that you can take the error in your last forecast and use that error, so you hope, to improve your next forecast.

If your last forecast was too low, exponential smoothing kicks your next forecast up. If your last forecast was too high, exponential smoothing kicks the next one down.

The basic idea is that exponential smoothing corrects your next forecast in a way that would have made your prior forecast a better one. That’s a good idea, and it usually works well.

## Method #3: Regression

When you use regression to make a forecast, you’re relying on one variable to predict another. For example, when the Federal Reserve raises short-term interest rates, you may rely on that variable to forecast what’s going to happen to bond prices or the cost of mortgages.

In contrast to moving averages or exponential smoothing, regression relies on a different variable to tell you what’s likely to happen next — something other than your own sales history.

Many accounting and CRM programs ship with native forecasting tools. If you can export a sales report to Microsoft Excel, you can also perform any of the three forecasting analyses outlined previously with the free included Analysis ToolPak.