A cycle is similar to a seasonal pattern (see the “Seasonality” section, later in this chapter), but you don’t consider it in the same way as you do seasonality. The upswing might span several years, and the downswing might do the same. Furthermore, one full cycle might take four years to complete, and the next one just two years. A good example is the business cycle: Recessions chase booms, and you never know just how long each is going to last. In contrast, yearly seasons have the same length, or nearly so.
Damping factor
The damping factor is a fraction between 0.0 and 1.0 that you use in exponential smoothing to determine how much of the error in the prior forecast will be used in calculating the next forecast.
Exponential smoothing
Stupid term, even if technically accurate. Using exponential smoothing, you compare your prior forecast to the prior actual (in this context, an actual is the sales result that Accounting tells you – after the fact – that you generated). Then you use the error – that is, the difference between the prior forecast and the prior actual – to adjust the next forecast and, you hope, make it more accurate than if you hadn’t taken the prior error into account. In Chapter 15, I show you how really intuitive an idea this is, despite its pretentious name.
Forecast period
The forecast period is the length of time that’s represented by each observation in your baseline. The term is used because your forecast usually represents the same length of time as each baseline observation. If your baseline consists of monthly sales revenues, your forecast is usually for the upcoming month. If the baseline consists of quarterly sales, your forecast is usually for the next quarter. Using the regression approach, you can make forecasts farther into the future than just one forecast period, but the farther your forecast gets from the most recent actual observation, the thinner the ice.
Moving average
You’ve probably run into the concept of moving averages somewhere along the line. The idea is that averaging causes noise in the baseline to cancel out, leaving you with a better idea of the signal (what’s really going on over time, unsullied by the inevitable random errors). It’s an average because it’s the average of some number of consecutive observations, such as the average of the sales in January, February, and March. It’s moving because the time periods that are averaged move forward in time – so, the first moving average could include January, February, and March; the second moving average could include February, March, and April; and so on.
There’s no requirement that each moving average include three values – it could be two, or four, or five, or conceivably even more. (Chapter 13 fills you in on the effects of choosing more or fewer periods to average.)
Predictor variable
You generally find this term in use when you’re forecasting with regression. The predictor variable is the variable you use to estimate a future value of the variable you want to forecast. For example, you may find a dependable relationship between unit sales price and sales volume. If you know how much your company intends to charge per unit during the next quarter, you can use that relationship to forecast the sales volume for next quarter. In this example, unit sales price is the predictor variable.
Regression
If you use the regression approach to sales forecasting, it’s because you’ve found a dependable relationship between sales revenues and one or more predictor variables. You use that relationship, plus your knowledge of future values of the predictor variables, to create your forecast.
How would you know those future values of the predictor variables? If you’re going to use unit price as a predictor, one good way is to find out from Product Management how much it intends to charge per unit during each of the next, say, four quarters. Another way involves dates: It’s entirely possible, and even common, to use dates (such as months within years) as a predictor variable. Even I can figure out what the next date value is in a baseline that at present ends at November 2015.
Seasonality
During the span of a year, your baseline might rise and fall on a seasonal basis. Perhaps you sell a product whose sales rise during warm weather and fall during cold. If you can see roughly the same pattern occur within each year over a several-year period, you know you’re looking at seasonality. You can take advantage of that knowledge to improve your forecasts. It’s useful to distinguish seasons from cycles. You never know how long a given cycle will last. But each of four seasons in a year is three months long.
Trend
A trend is the tendency of the level of a baseline to rise or fall over time. A rising revenue trend is, of course, good news for sales reps and sales management, to say nothing of the rest of the company. A falling baseline of sales, although seldom good news, can inform Marketing and Product Management that they need to make and act on some decisions, perhaps painful ones. Regardless of the direction of the trend, the fact that a trend exists can cause problems for your forecasts in some contexts – but there are ways of dealing with those problems. Chapter 17 shows you some of those ways.
A baseline is a series of observations – more to the point in this book, a revenue stream – that you use to form a forecast. There are three typical forecasts, depending on what the baseline looks like:
❯❯ If the baseline has held steady, your best forecast will probably be close to the average of all the sales amounts in the baseline.
❯❯ If the baseline has been rising, your forecast will likely be higher than the most recent sales amount.
❯❯ If the baseline has been falling, your forecast will probably be lower than the most recent sales amount.
Note: Those weasely words likely and probably are there because when there’s a seasonal aspect to the sales that doesn’t yet appear in your baseline, the next season might kick in at the same point as your forecast and reverse what you’d expect otherwise.
There’s another weasel word: often. You’ll have plenty of opportunities to use one variable, such as the total of sales estimates from individual sales representatives, to forecast the variable you’re really interested in, sales revenues. In that case, you might get a more accurate forecast by using Excel to figure the formula that relates the two variables,
