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Excel Sales Forecasting For Dummies
By Conrad Carlberg
John Wiley & Sons ISBN: 0-7645-7593-7
Chapter One A Forecasting Overview
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, all three of these options are less than desirable. If you want to do better more often than you do worse, you need to take advantage of some better options. Lucky for you, there are different ways to forecast, ways that have proven their accuracy over and over. They're a little more time consuming than guessing, 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 and know 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.
Understanding Excel Forecasts
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 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 I 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 program. But Excel is a very useful application for making sales forecasts, for a variety of reasons:
You often have sales history recorded in an Excel worksheet. When you already keep your sales history in Excel, basing your forecast on the existing sales history is easy - you've already got your hands on it.
Excel's charting features make it much easier to visualize what's going on in your sales history and how that history defines your forecasts. Excel has tools (found in what's called the Analysis ToolPak) that make generating forecasts easier. You still have to know what you're doing and what the tools are doing - you don't want to just jam the numbers through some analysis tool and take the result at face value, without understanding what the tool's up to. But that's what this book is here for.
You can take more control over how the forecast is created by skipping the Analysis ToolPak's forecasting tools and entering the formulas yourself. As you get more experience with forecasting, you'll probably find yourself doing that more and more.
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. 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.
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 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 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.
Getting the Data Ready
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 list. In Part II, 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 list. A list is something very much like a database. Your Excel worksheet has columns and rows, and if you put a list there, you just need to manage three requirements:
Keep different variables in different columns. For example, you can put sales dates in one column, sales amounts in another column, sales reps' names in another, product lines in yet another.
Keep different records in different rows. When it comes to recording sales information, keep different sales records in different rows. Put information about a sale that was made on January 15 in one row, and information about a sale made on January 16 in a different row.
Put the names of the variables in the list's first row. For example, you might put "Sales Date" in column A, "Revenue" in column B, "Sales Rep" in column C, and "Product" in column D.
Figure 1-1 shows a typical Excel list.
Why bother with lists? Because many Excel tools, including the ones you use to make forecasts, rely on lists. Charts - which help you visualize what's going on with your sales - rely on lists. Pivot tables - which are the most powerful way you have for summarizing your sales results in Excel - rely heavily on lists. The Analysis ToolPak - a very useful way of making forecasts - relies on lists, too.
You find a lot more about creating and using lists in Chapter 6. In the meantime, just keep in mind that a list has different variables in different columns, and different records in different rows.
Ordering your data
"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 best - and I mean the 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 a list (or in an external database) and combines the records in ways that you control. You may have a list 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.
There are a couple of wonderful things about pivot tables:
They can accumulate for you all your sales data - or, for that matter, your data on the solar wind, but this book is about sales forecasting. If you gather information on a sale-by-sale basis, and you then want to know how much your reps sold on a given day, in a given week, and so on, a pivot table is the best way to do so.
You can use a pivot table as the basis for your next forecast, which saves you a bunch of time.
They have a unique way of helping you group your historical data - by day, by week, by month, by quarter, by year, you name it. Chapter 8 gives you much more information on pivot tables, including troubleshooting some common problems.
Making Basic Forecasts
Part III 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 Analysis ToolPak to make forecasts that you can back up with actuals - given that you've looked at Part II 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 Analysis ToolPak (often abbreviated ATP) is a gizmo that has shipped with Excel ever since 1995. The ATP is a convenient way to make forecasts, as well as to do general data analysis. The three principal tools Excel's ATP 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 Analysis ToolPak 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 (Tools->Goal Seek) 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 ATP tools.
Given a good baseline, the ATP 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.
Putting moving averages to work for you
You may already be familiar with moving averages. They have two main characteristics, as the name makes clear:
They move. More specifically, they move over time. The first moving average may involve Monday, Tuesday, and Wednesday; in that case, the second moving average would involve Tuesday, Wednesday, and Thursday; the third Wednesday, Thursday, and Friday, and so on.
They're averages. The first moving average may be the average of Monday's, Tuesday's, and Wednesday's sales. Then the second moving average would be the average of Tuesday's, Wednesday's, and Thursday's sales, and so on.
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 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, your stores will be discounting ski boots for less than the cost of an arthroscopy. Some months, a new and really cool model will come on line, and they'll be taking every possible advantage. The peaks and valleys even out. Some weeks there will be an extra 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 months. The signal in those months 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. So 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.
Chapter 14 goes into more detail about using moving averages for forecasting.
Excerpted from Excel Sales Forecasting For Dummies by Conrad Carlberg Excerpted by permission.
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