Excel® Sales Forecasting For Dummies®, 2nd Edition
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You wouldn’t have pulled this book off the shelf if you didn’t need to forecast sales. And I’m sure that you’re not Nostradamus. Your office isn’t filled with the smell of incense and it’s not your job to predict the date that the world will come to an end.
But someone — perhaps you — wants you to forecast sales, and you find out how to do that here, using the best general-purpose analysis program around, Microsoft Excel.
This book concentrates on using numbers to forecast sales. If you’re a salesperson, or a sales manager, or someone yet higher up the org chart, you’ve run into forecasts that are based not on numbers but on guesses, sales quotas, wishful thinking, and Scotch.
I get away from that kind of thing here. I use numbers instead. Fortunately, you don’t need to be a math major to use Excel for your forecasting. Excel has a passel of tools that will do it on your behalf. Some of them are even easy to use, as you’ll see.
That said, it’s not all about numbers. You still need to understand your products, your company, and your market before you can make a sensible sales forecast, and I have to trust you on that. I hope I can. I think I can. Otherwise, start with Part 1, which talks about the context for a forecast.
You can hop around the chapters in this book, as you can in all books that feature the guy with a pool ball rack for a head. There are three basic approaches to forecasting with numbers — moving averages, smoothing, and regression — and you really don’t have to know much about one to understand another. It helps to know all three, but you don’t really need to.
The phrase foolish assumptions is, of course, redundant. But here are the assumptions I’m making:
I’m assuming that you know the basics of how to use Excel. Entering numbers into a worksheet, like numbers that show how much you sold in August 2015; entering formulas in worksheet cells; saving workbooks; using menus; that sort of thing.
If you haven’t ever used Excel before, don’t start here. Do buy this book, but also buy Excel 2013 For Dummies by Greg Harvey (published by Wiley), and dip into that one first.
In the margins of this book, you find icons — little pictures that are designed to draw your attention to particular kinds of information. Here’s what the icons mean:
Anything marked with this icon will make things easier for you, save you time, get you home in time for dinner. You get some of what I’ve distilled from all my years browsing those blasted newsgroups.
Not a lot of warnings in this book, but there are a few. These tell you what to expect if you do something that Microsoft hasn’t sufficiently protected you against. And there are some of those.
A string around your finger. There are some things to keep in mind when you’re doing your forecasts, and it’s usually easier to remember them than to have to look them up over and over. I do want you to read this book over and over, as I do with murder mysteries, but you’ll get your work done faster if you remember this stuff.
Speaking of stuff, anything marked with this icon is stuff you can probably ignore — but if you’re having trouble getting to sleep you may want to read these. I don’t get into heavy-duty mathematical issues here, but you see some special things about how Excel prepares your forecasts. Sleep tight.
In addition to what you’re reading right now, this product also comes with a free access-anywhere Cheat Sheet that tells you about Excel data analysis add-in tools, how to use forecasting functions, what you get out of the Excel LINEST function, and what to do when setting up your baseline in Excel. To get this Cheat Sheet, simply go to www.dummies.com and search for “Excel Sales Forecasting For Dummies Cheat Sheet” in the Search box.
I’ve also provided files for each chapter so that you can try out what I’m talking about in the leisure of your own home. You can find these files at www.dummies.com/go/excelsalesforecasting.
Are you looking for information about the basics of forecasting? Why it works? Why it’s not just a self-licking ice cream cone? Start at Chapter 1.
Do you want to know how to put your data together in a workbook? Head to Chapter 5 to find out more about baselines, and then check out the chapters on using tables in Excel.
If you’re already up on forecasting basics and tables, head for Chapter 8, where you’ll see how to use pivot tables to set up the baseline for your forecast.
And if you know all that stuff already, just go to Chapter 10 and start looking at how to manage your forecasts yourself, without relying on the various tools that take care of things for you. You’ll be glad you did.
Part 1
IN THIS PART …
In Part 1, I talk about why forecasting sales can help your business in ways that seem to have little to do with sales. Part 1 also tells you why forecasting isn’t simply a matter of using formulas to crunch numbers. But, face it, some numbers have to be crunched, and here you find an introduction to baselines — which are the basis for the number-crunching. I try to convince you that forecasting really does work, and I back up that claim by showing you how.
Chapter 1
IN THIS CHAPTER
Knowing the different methods of forecasting
Arranging your data in an order Excel can use
Getting acquainted with the Analysis ToolPak
Going it alone
A sales forecast is like a weather forecast: It’s an educated guess at what the future will bring. You can forecast all sorts of things — poppy-seed sales, stock market futures, the weather — in all sorts of ways: You can make your own best guess; you can compile and composite other people’s guesses; or you can forecast on the basis of wishful thinking.
Unfortunately, none of these options is truly acceptable. If you want to make better forecasts, you need to take advantage of some better options. And there are different ways to forecast, ways that have proven their accuracy over and over. They take a little more time to prepare than guessing does, but in the long run I’ve spent more time explaining bad guesses than doing the forecasts right in the first place.
Microsoft Excel was originally developed as a spreadsheet application, suited to figuring payment amounts, interest rates, account balances, and so on. But as Microsoft added more and more functions — for example, AVERAGE and TREND and inventory-management stuff — Excel became more of a multipurpose analyst than a single-purpose calculator.
Excel has the tools you need to make forecasts, whether you want to prepare something quick and dirty (and who doesn’t from time to time?) or something sophisticated enough for a boardroom presentation.
The tools are there. You just need to know which tool to choose for which situation and, of course, how to use it. You need to know how to arrange data for the tool. And you need to know how to interpret what the tool tells you — whether that tool’s a basic one or something more advanced.
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 during each of the last ten years, where the market futures wound up each of the last 12 months, what the daily high temperature was year-to-date).
Unless you’re going to just 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. (Part 1 of this book talks about forecast baselines and why they work.)
An Excel forecast isn’t any different from forecasts you make with a specialized forecasting program. But Excel is particularly useful for making sales forecasts, for a variety of reasons:
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.
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 baseline sales history. Later in this chapter, I show you more of the logic behind using moving averages. 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, 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. The averaging process evens out the bumps you get from discouraging economic news or temporary boomlets.
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 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.
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 might 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.
Which method of forecasting you use does make a difference, but regardless of your choice, in Excel you have to set up your baseline data in a particular way. Excel prefers it if your data is in the form of a table. In Part 2, I fill you in on how to arrange your data so that it best feeds your forecasts, but following is a quick overview.
There’s nothing mysterious about an Excel table. A table is something very much like a database. Your Excel worksheet has columns and rows, and if you put a table there, you just need to manage three requirements:
Figure 1-1 shows a typical Excel table.
FIGURE 1-1: You don’t have to keep the records in date order — you can handle that later.
Why bother with tables? Because many Excel tools, including the ones you use to make forecasts, rely on tables. Charts — which help you visualize what’s going on with your sales — rely on tables. Pivot tables — which are the most powerful way you have for summarizing your sales results in Excel — rely heavily on tables. The Data Analysis add-in — a very useful way of making forecasts — relies on tables, too.
For years, Excel depended on an informal arrangement of data called a list. A list looked a lot like a table does now, with field names in its first row, followed by records. But a list did not have built-in properties such as record counts or filters or total rows or even a name. You had to take special steps to identify the number of rows and columns the list occupied.
In Excel 2007, Microsoft added tables as a new feature, and tables have all those things that lists lack. One aspect of tables is especially useful for sales forecasting. As time passes and you get more information about sales figures, you want to add the new data to your baseline. Using lists, you had to define what’s called a dynamic range name to accommodate the new data. With tables, all you need to do is provide a new record, usually in a new row at the end of the table. When you do so, the table is automatically extended to capture the new data. Anything in the workbook — charts, formulas, whatever — is also automatically updated to reflect the new information. Tables are a major improvement over lists and this book makes extensive use of them.
You find a lot more about creating and using tables in Chapter 6. In the meantime, just keep in mind that a table has different variables in different columns, and different records in different rows.
“Ordering your data” may sound a little like “coloring inside the lines.” The deal is that you have to tell Excel how much you sold in 1999, and then how much in 2000, and in 2001, and so on. If you’re going to do that, you have to put the data in chronological order.
The very best way to put your data in chronological order in Excel is by way of pivot tables. A pivot table takes individual records that are in an Excel table (or in an external database) and combines the records in ways that you control. You may have a table showing a year’s worth of sales, including the name of the sales rep, the product sold, the date of sale, and the sales revenue. If so, you can very quickly create a pivot table that totals sales revenue by sales rep and by product across quarters. Using pivot tables, you can summarize tens of thousands of records, quite literally within seconds. If you haven’t used pivot tables before, this book not only introduces the subject but also makes you dream about them in the middle of the night.
Three particularly wonderful things about pivot tables:
Part 3 gets into the business of making actual forecasts, ones that are based on historical data (that is, what’s gone on before). You see how to use the Data Analysis add-in to make forecasts that you can back up with actuals — given that you’ve looked at Part 2 and set up your actuals correctly. (Your actuals are the actual sales results that show up in the company’s accounting records — say, when the company recognizes the revenue.)
The Data Analysis add-in is a gizmo that has shipped with Excel ever since 1995. It includes a convenient way to make forecasts, as well as to do general data analysis. The three principal tools that the Data Analysis add-in gives you to make forecasts are:
Those are the three principal forecasting methods, and they form the basis for the more-advanced techniques and models. So it’s no coincidence that these tools have the same names as the forecasting methods mentioned earlier in this chapter.
The Data Analysis add-in is an add-in. An add-in does tasks, like forecasting, on your behalf. An add-in is much like the other tools that are a part of Excel — the difference is that you can choose whether to install an add-in. For example, you can’t choose whether the Goal Seek tool (under What-If Analysis on the Ribbon’s Data tab) is available to you. If you decide to install Excel on your computer, Goal Seek is just part of the package. Add-ins are different. You can decide whether to install them. When you’re installing Excel — and in most cases this means when you’re installing Microsoft Office — you get to decide which add-ins you want to use.
The following sections offer a brief introduction to the three Data Analysis tools.
Given a good baseline, the Data Analysis can turn a forecast back to you. And then you’re responsible for evaluating the forecast, for deciding whether it’s a credible one, for thinking the forecast over in terms of what you know about your business model. After all, Excel just calculates — you’re expected to do the thinking.
You may already be familiar with moving averages. They have two main characteristics, as the name makes clear:
The basic idea, as with all forecasting methods, is that something regular and predictable is going on — often called the signal. Sales of ski boots regularly rise during the fall and winter, and predictably fall during the spring and summer. Beer sales regularly rise on NFL Sundays and predictably fall on other days of the week.
But something else is going on, something irregular and unpredictable — often called noise. If a local sporting goods store has a sale on, discounting ski boots from May through July, you and your friends may buy new boots during the spring and summer, even though the regular sales pattern (the signal) says that people buy boots during the fall and winter. As a forecaster, you typically can’t predict this special sale. It’s random and tends to depend on things like overstock. It’s noise.
Let’s say you run a liquor store, and a Thursday night college football game that looked like it would be the Boring Game of the Week when you were scheduling your purchases in September has suddenly in November turned into one with championship implications. You may be caught short if you scheduled your purchases to arrive at your store the following Saturday, when the signal in the baseline leads you to expect your sales to peak. That’s noise — the difference between what you predict and what actually happens. By definition, noise is unpredictable, and for a forecaster it’s a pain.
If the noise is random, it averages out. Some months, sporting goods stores will be discounting ski boots for less than the cost of an arthroscopy. Some months, a new and really cool model will come out, and the stores will take every possible advantage. The peaks and valleys even out. Some weeks there will be an extra football game or two and you’ll sell (and therefore need) more bottles of beer. Some weeks there’ll be a dry spell from Monday through Friday, you won’t need so much beer, and you won’t want to bear the carrying costs of beer you’re not going to sell for a while.
The idea is that the noise averages out, and that what moving averages show you is the signal. To misquote Johnny Mercer, if you accentuate the signal and eliminate the noise, you latch on to a pretty good forecast.
So with moving averages, you take account of the signal — the fact that you sell more ski boots during certain months and fewer during other months, or that you sell more beer on weekends than on weekdays. At the same time you want to let the random noises — also termed errors — cancel one another out. You do that by averaging what’s already happened in two, three, four, or more previous consecutive time periods. The signal in those time periods is emphasized by the averaging, and that averaging also tends to minimize the noise.
Suppose you decide to base your moving averages on two-month records. That is, you’ll average January and February, and then February and March, and then March and April, and so on. In that case you’re getting a handle on the signal by averaging two consecutive months and reducing the noise at the same time. Then, if you want to forecast what will happen in May, you hope to be able to use the signal — that is, the average of what’s happened in March and April.
Figure 1-2 shows an example of the monthly sales results and of the two-month moving average.
FIGURE 1-2: The moving average shows the general direction of the sales (the signal), and deemphasizes the random variations (the noise).
Chapter 14 goes into more detail about using moving averages for forecasting.
I know, the term exponential smoothing sounds intimidating and pretentious. I guess it’s both — although I promise I’m not responsible for it. (If you really want, you can find out why it’s called that in Chapter 15.) In any event, don’t worry about what it’s called — it’s just a kind of self-correcting moving average.
Suppose that in June, you forecast $100,000 in sales for July. When the July sales results are in, you find that your July forecast of $100,000 was $25,000 too low — you actually made $125,000 in sales. Now you need to forecast your sales for August. The idea behind this approach to forecasting is to adjust your August forecast in a way that would have made the July forecast more accurate. That is, because your July forecast was too low, you increase your August forecast above what it would have been otherwise.
More generally:
You don’t make these adjustments just by guessing. There are formulas that help out, and the Data Analysis add-in’s Exponential Smoothing tool can enter the formulas for you. Or you can roll your own formulas if you want. Turn to Chapter 15 to see how to do that.
Figure 1-3 shows what you would forecast if your prior forecast (for July) was too low — then you boost your forecast for August.
FIGURE 1-3: Here’s what happens if your forecast for July was an underestimate. Notice that the August forecast is kicked up.
And if your prior, July forecast was too high, you cool your jets a little bit in your August forecast, as shown in Figure 1-4.
FIGURE 1-4: Your forecast for March 2015 was too high, so exponential smoothing makes you back off your forecast for April 2015.
The term regression doesn’t sound as bad as exponential smoothing, but it is — I admit — more complicated, at least in terms of the math.
And that’s why the Regression tool in the Data Analysis add-in is convenient. The add-in takes responsibility for the math, just as it does with moving averages and exponential smoothing. Remember: You still have to give a good baseline to the tools in the Data Analysis add-in to get accurate results.
Here’s a quick look at forecasting with regression. (You can find a more detailed look in Chapter 11.)
The idea behind regression is that one variable has a relationship with another variable. When you’re a kid, for example, your height tends to have a relationship to your age. So if you want to forecast how tall you’ll be next year — at least, until you quit growing — you can check how old you’ll be next year.
Of course, people differ. When they’re 15 years old, some people are 5 feet tall, some are 6 feet tall. On average, though, you can forecast with some confidence how tall someone will be at age 15. (And you can almost certainly forecast that a newborn kidlet is going to be under 2 feet tall.)
The same holds true with sales forecasting. Suppose your company sells consumer products. It’s a good bet that the more advertising you do, the more you’ll sell. At least it’s worth checking out whether there’s a relationship between the size of your advertising budget and the size of your sales revenue. If you find that there’s a dependable relationship — and if you know how much your company is willing to spend on advertising — you’re in a good position to forecast your sales.
Or suppose your company markets a specialty product, such as fire doors. (A fire door is one that’s supposed to be resistant to fire for some period of time, and there are a lot of them in office buildings.) Unlike consumer products, something such as a fire door doesn’t have to be a particular off-the-shelf color or have a fresher-than-fresh aroma. If you’re buying fire doors, you want to get the ones that meet the specs and are the cheapest.
So if you’re selling fire doors, as long as your product meets the specs, you’d want to have a look at the relationship between the price of fire doors and how many are sold. Then you check with your marketing department to find out how much they want you to charge per door, and you can make your forecast accordingly.
The point is that more often than not you can find a dependable relationship between one variable (advertising dollars or unit price) and another (usually, sales revenue or units sold).
You use Excel’s tools to quantify that relationship. In the case of regression forecasts, you give Excel a couple of baselines. To continue the examples used so far in this section:
If you give Excel good baselines, it will come back to you with a formula.
It’s just a touch more complicated than that. Excel also gives you a number, called a constant, that you need to add to the result of the multiplication. But as Chapter 11 shows, you can get Excel to do that for you.
I’ve been doing this stuff for a long time, and I can’t tell you how critical it is to chart your baseline and your forecast. Being able to visualize what’s going on is important for several reasons.
Using Excel’s charts, you can see how your actuals are doing (see Figure 1-5). And by charting your actuals, you can see how well your sales forecasts do against the actual sales results. Figure 1-6 shows a forecast that’s based on moving averages, against the monthly actuals.
FIGURE 1-5: An Excel chart makes it much easier to see how your sales are doing.
FIGURE 1-6: Notice how the moving average lags behind the actual results.
By charting your baseline and your forecasts, you can:
Yes, an R squared or some other summary statistic can give you a concise estimate of how well your forecasts are working. But there’s nothing, nothing, like a chart to tell you if you’re forecasting results or if you’re forecasting junk. Chapter 9 shows you how to set up charts with Excel.
There’s a lot to be said for using the Data Analysis add-in to create your forecasts. The add-in’s tools are quick, they do the heavy lifting for you, and they’re reasonably comprehensive, taking care of the math and some of the charting.
But there’s nothing like doing it yourself. When you wave goodbye to the Data Analysis add-in, you establish and maintain control over what’s going on with the forecast. If you have formulas in your worksheet cells — formulas that support your forecasts — you can change those formulas as your forecasting needs change. And you can change — or add to — the baseline and immediately see what the effect doing so has on your forecast. That’s because the formulas are live: They react to changes in their inputs.
When the add-in’s tools give you not formulas but static values instead, you can’t easily experiment with the forecasts or see the effect of modifying the baseline. And the add-in’s Regression tool gives you just the static values. The Exponential Smoothing tool is a little better, but it mixes formulas with static values. And the Moving Averages tool forces you to start from scratch if you want to change the number of records in the baseline that make up a moving average.
Suppose that you have the number 3 in cell A1 and the number 5 in cell A2. In cell A3 you can enter the sum of those two numbers, 8. But if you now change the number 3 in cell A1 to, say, 103, you still have 8 in A3. It’s a constant — a number, not a formula. It doesn’t react to what’s in cell A1 or A2: You’re still going to see the number 8 in cell A3.
On the other hand, suppose you have this in cell A3:
=A1 + A2
That’s a formula, not a constant, and it tells Excel to add whatever’s in A1 to whatever’s in A2. So if you change what’s in A1, or what’s in A2, Excel recalculates the result and shows it — in this example — in A3.
The point to keep in mind is that the add-in’s regression tool gives you numbers, not formulas. It calculates your forecast, and the underlying figures, and writes numbers onto your worksheet. That means, regardless of how you change the numbers in your baseline, you’re still going to be looking at the same forecast as offered by the Regression tool.
But — and it’s a big one — if you make the forecast yourself instead of relying on the add-in’s tool, you can enter the formulas that the add-in denies you. Why is this important? By entering the formulas yourself, you have more control over what’s going on with the forecast.
Relying on the add-in, which isn’t a bad toolbox, and is one that you can generally trust, is perfectly okay. However, if you enter formulas, ones that react to changes in your baseline, you can make a change in the baseline and see what happens to the forecast. You can change this month’s result from $100,000 to $75,000 and see whether your forecast for next month changes substantially. You can’t do that with the add-in’s Regression tool unless you start all over again, because it doesn’t give you formulas. To a smaller degree, the same is true of the Exponential Smoothing tool.
But the more important reason, the reason for you to consider entering the formulas yourself, is that you’re relying on your own knowledge of how and why forecasting works. In Part 4, I show you how to use functions like LINEST and TREND to do your regression-based forecasts. You also see how to use array formulas to get the most out of those Excel functions.
You don’t need to enter all the formulas yourself to make good forecasts. The add-in includes reasonably good tools. But if you do enter the formulas yourself, not only can you be more confident that you know what’s going on with your forecast, but you can also exercise more control over what your forecast says is going to happen. In a business as tricky and trappy as forecasting, the more control you have, the better.
Chapter 2
IN THIS CHAPTER
Knowing why you need to forecast
Understanding the language of forecasting
Seeing what Excel can do for you
Unless you really enjoy playing with numbers, you need a good reason to bother with forecasting sales. In this chapter, I tell you some of the business reasons to forecast, beyond the fact that your Vice President of Sales makes you do it.
Like all specialties, forecasting uses terms that are unfamiliar to those who haven’t yet been inducted into the secret society. This chapter introduces you to some of the important sales forecasting terminology.
If you’re going to make a credible forecast, you need access to an archive of historical data that isn’t necessarily easy to access. You’ll often find it right there in an Excel workbook, but sometimes it isn’t there; instead, it’s in your company’s accounting database, and someone will have to exhume it. In this chapter, you see some of the reasons to put yourself or your assistant through that task.
Excel offers several methods of forecasting. Each method works best — and some work only — if you set up a baseline using what Excel terms a table. Depending on the method you choose, that table may occupy only one column, or two (or more) columns. This chapter gives you an overview of those forecasting methods, along with a brief explanation of why you might use just one column of data for your baseline, or two or more columns, depending on your choice of forecasting method.
Excel is an ideal general-purpose analysis program to use for forecasting, in part because it has functions and tools that are intended to help you make your forecasts, and in part because you often store the necessary data in Excel anyway — so, it’s right there, ready for you to use. In this chapter, you find out what’s so great about using Excel to create your forecasts, and you find some groundwork on how best to put it to use in your own situation.