have your competitors been cutting into your market share, or is the total size of the market shrinking? If the problem is the competition, maybe you want to do something to take back your share, even if that requires putting more resources into the product line – such as retooling its manufacture, putting more dollars into promotions, or cutting the price. But if the total market itself is shrinking, it may just be time to bail out.
❯❯ How old is the product? Products do have life cycles. When products are bright and shiny, the sales revenues can grow sharply over a fairly short time frame. When products reach maturity, the sales usually flatten out. And then, as newer, better, fancier products arrive, the sales start to drop. Think streaming video versus DVD. Get Marketing and Product Management to assess whether the product is getting long in the tooth. If it is, it may be time to get out. Or, it may be smart to spruce up the product and differentiate it from the competition’s versions, in order to squeeze some more profitable revenue out of it before you give up on it. Forecasting can inform that kind of decision, although it can’t make it for you.
❯❯ How will Sales support the product? If your company decides that it’s not yet time to abandon the product, Sales Management needs to make some decisions about how to allocate its resources – that is, its sales reps. One way to do that, of course, is to take the product out of some reps’ bags and replace it with another, more robust product. (Keep in mind that some reps prefer older products because they can use familiar sales strategies.)
❯❯ Is it possible that the decline in sales is due more to large-scale economic conditions than to problems with the product itself? If so, you may decide to hang in and wait for the economy, consumer confidence, or the index of leading economic indicators to improve, instead of making a drastic decision to drop a product line.
To size inventories
During the late 1980s, I worked for a Baby Bell – one of the companies that was spun off by the AT&T breakup. For a couple of years, I was in charge of managing resale equipment inventories at that Baby Bell.
My staff and I reduced the size of the equipment intended for sale to customers from a grotesque $24 million to a more reasonable $9 million in 18 months, without resorting to write-downs. We did it by forecasting sales by product line. This helped us tell which products we could expect to have high turns ratios (the speed with which the product line would sell) and we’d buy those in quantities that increased our discounts from our suppliers.
Until we were almost out of them, we refused to buy any products that our forecasts indicated would have low turns ratios. It didn’t matter how piteous the pleadings of the sales managers who wanted them on hand for fast delivery just in case a customer decided to buy one and wanted it installed right now. (Getting a huge PBX out of warehouse storage in West Eyesocket, Connecticut, and shipping it to Broken Pelvis, Montana, can take longer than you may think. For one thing, you may have to pressure Connecticut’s Regional VP into letting go of it. Today, VoIP software is rapidly replacing big electronic switches, but the principle remains the same: Expensive stuff can be hard to move.)
Plus, the annual carrying costs for equipment inventory in the late 1980s averaged around 15 percent of the cost of the equipment, including storage, cost of money, obsolescence, and so on. So by reducing the total inventory cost by $15 million, we saved the company $2.25 million each year. (That savings actually covered the cost of our salaries, by the way, with plenty left over.)
You need to get a handle on the specialized terminology used in forecasting for a couple very practical reasons. One is that you may be asked to explain your forecasts to your boss or in a meeting of, for example, sales managers. In those situations you want to say things like, “We decided to use regression on the baseline because it turned out to be more accurate.” You don’t want to find yourself saying “Jeff found a formula in a book he has, and we used it on these numbers here. Seems to work okay.”
Another good reason is that Excel uses many of these terms, as do other programs, and figuring out what’s going on is a lot easier if you know what the terms mean. Okay, deep breath.
Autoregressive integrated moving averages (ARIMA)
I mention autoregressive integrated moving averages (ARIMA) here not because this book is going to use it or even talk much about it. But if you’re going to do forecasting, some smart aleck will eventually ask you if you used ARIMA, and you should know how to reply. ARIMA is in part a forecasting method, and also a way of evaluating your baseline so that you can get quantitative evidence that supports using a regression approach, a moving-average approach, or a combination of both. Unless you really take to this forecasting stuff, you’ll usually do just fine without it, even though it’s an excellent, if complex, diagnostic tool.
By the way, your answer to the smart aleck should be, “No. I’ve been working with this baseline for so long now that I know I get my best results with exponential smoothing. Which, as you know, is one of the forms that ARIMA can take.”
Baseline
A baseline is a sequence of data arranged in chronological order. In terms of this book’s basic topic, the forecasting of sales, some examples of baselines include total monthly revenues from January 2010 through December 2015, number of units sold weekly from January 1, 2015, through December 31, 2016, and total quarterly revenues from Q1 2007 through Q4 2016. Data arranged like this is sometimes called a time series, but in this book I use the term baseline.
Correlation
A correlation coefficient expresses how strongly two variables are related. Its possible values range from –1.0 to +1.0, but in practice you never find correlations so extreme. The closer a correlation coefficient is to +/–1.0, the stronger the relationship between the two variables. A correlation of 0.0 means no relationship. So, you might find a correlation of +0.7 (fairly strong) between the number of sales reps you have and the total revenue they bring in: The greater the number of reps, the more that gets sold. And you might find a correlation of –0.1 (quite weak) between how much a rep sells and his telephone number.
A special type of correlation is the autocorrelation, which calculates the strength of the relationship between one observation in a baseline and an earlier observation (often, but not always, the relationship between two consecutive observations). The autocorrelation tells you the strength of the relationship between what came before and what came after. This in turn helps you decide what kind of forecasting technique to use. Here’s an example of how to calculate an autocorrelation that might make the concept a little clearer:
=CORREL(A2:A50,A1:A49)
This Excel formula uses the CORREL function to show how strong (or how weak) a relationship there is between whatever values are in A2:A50 and those in A1:A49. The most useful autocorrelations involve baselines that are sorted in chronological order. (This sort of autocorrelation is not quite the same as the
