Excel Sales Forecasting For Dummies

Excel Sales Forecasting For Dummies

by Conrad Carlberg


$26.99 $29.99 Save 10% Current price is $26.99, Original price is $29.99. You Save 10%.
View All Available Formats & Editions
Eligible for FREE SHIPPING
  • Want it by Monday, October 22?   Order by 12:00 PM Eastern and choose Expedited Shipping at checkout.
    Same Day shipping in Manhattan. 
    See Details


Excel Sales Forecasting For Dummies by Conrad Carlberg

Excel at predicting sales and forecasting trends using Microsoft Excel!

If you're a sales or marketing professional, you know that forecasting sales is one of the biggest challenges you face on the job. Unlike other books on the subject, Excel Sales Forecasting For Dummies, 2nd Edition leaves arcane business school terms and complex algebraic equations at the door, focusing instead on what you can do right now to utilize the world's most popular spreadsheet program to produce forecasts you can rely on.

Loaded with confidence boosters for anyone who succumbs to sweaty palms when sales predictions are mentioned, this trusted guide show you how to use the many tools Excel provides to arrange your past data, set up lists and pivot tables, use moving averages, and so much more. Before you know it, you'll become a forecaster par excellence—even if numbers aren't your jam.

  • Choose the right forecasting method
  • Find relationships in your data
  • Predict seasonal sales
  • Filter lists or turn them into charts

Consider this guide your crystal ball—and start predicting the future with confidence and ease!

Product Details

ISBN-13: 9781119291428
Publisher: Wiley
Publication date: 07/18/2016
Pages: 408
Sales rank: 808,542
Product dimensions: 7.30(w) x 9.00(h) x 0.90(d)

About the Author

Conrad Carlberg is a nationally recognized expert in quantitative analysis, data analysis, and management applications such as Microsoft Excel, SAS, and Oracle. He has been a sales engineer and also worked in product marketing. Conrad has written numerous books on Excel and other Microsoft Office applications. He holds a PhD in statistics.

Read an Excerpt

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.

    Using lists


    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:

  • Moving Averages

  • Exponential Smoothing

  • Regression

    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.
    All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
    Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

  • Table of Contents

    Introduction 1

    About This Book 1

    Foolish Assumptions 2

    Icons Used in This Book 2

    Beyond the Book 3

    Where to Go from Here 3

    Part 1 Understanding Sales Forecasting and How Excel Can Help 5

    Chapter 1 A Forecasting Overview 7

    Understanding Excel Forecasts 8

    Method #1 Moving averages 9

    Method #2 Exponential smoothing 9

    Method #3 Regression 10

    Getting the Data Ready 10

    Using tables 10

    Ordering your data 12

    Making Basic Forecasts 13

    Putting moving averages to work for you 14

    Making sense of exponential smoothing 16

    Using regression to get what you want 16

    Charting Your Data 19

    Forecasting with Advanced Tools 20

    Chapter 2 Forecasting: The Basic Issues 23

    Why Forecast? 24

    To plan sales strategies 24

    To size inventories 25

    Talking the Talk: Basic Forecasting Lingo 26

    Auto regressive integrated moving averages (ARIMA) 26

    Baseline 27

    Correlation 27

    Cycle 28

    Damping factor 28

    Exponential smoothing 28

    Forecast period 28

    Moving average 29

    Predictor variable 29

    Regression 29

    Seasonality 30

    Trend 30

    Understanding the Baseline 30

    Charting the baseline 31

    Looking for trends 32

    Setting Up Your Forecast 33

    Smoothing data 34

    Regression: It's all about relationships 34

    Using Your Revenue and Cost Data 35

    Chapter 3 Understanding Baselines 41

    Using Qualitative Data 42

    Asking the right questions 42

    Keeping your eye on the ball: The purpose of your forecast 43

    Recovering from Mistakes in Sales Forecasting 45

    Getting over it 45

    Using revenue targets as forecasts 46

    Recognizing Trends and Seasons 47

    Identifying trends 48

    Understanding seasonality 50

    Chapter 4 Predicting the Future: Why Forecasting Works 53

    Understanding Trends 54

    Watching revenues go up - and down 55

    Testing for trends 59

    Matchmaker, Matchmaker: Finding Relationships in the Data 63

    Choosing the predictors 64

    Analyzing the correlations 67

    Part 2 Organizing the Data 69

    Chapter 5 Choosing Your Data: How to Get a Good Baseline 71

    Early to Bed: Getting Your Figures in Order 72

    Why order matters: Moving averages 72

    Why order matters: Exponential smoothing 75

    Why order doesn't matter: Regression 76

    Staying Inside the Lines: Why Time Periods Matter 77

    Deciding how far to forecast 78

    Choosing your time periods 81

    Spacing Time Periods Equally 82

    Using periodic relationships 83

    When missing data causes unequal time periods 85

    Chapter 6 Setting Up Tables in Excel 87

    Understanding Table Structures 88

    Creating a Table 91

    Using the Total row 93

    Using other table features 96

    Filtering Lists 96

    Using Excel's table filters 96

    Using the Advanced Filter 98

    Importing Data from a Database to an Excel Table 100

    Chapter 7 Working with Tables in Excel 103

    Turning Tables into Charts 103

    Understanding chart types 104

    Creating the chart from your table 108

    Refining charts 109

    Using the Data Analysis Add-in with Tables 112

    Avoiding the Data Analysis Add-in's Traps 115

    Part 3 Making a Basic Forecast 119

    Chapter 8 Summarizing Sales Data with Pivot Tables 121

    Understanding Pivot Tables 122

    Making baselines out of sales data 123

    Totaling up the data 128

    Building the Pivot Table 130

    Grouping Records 134

    Knowing when to group records 135

    Creating the groups 135

    Avoiding Grief in Excel Pivot Tables 137

    Don't use blank dates 137

    Making multiple groups 138

    Chapter 9 Charting Your Baseline: It's a Good Idea 141

    Digging into a Baseline 142

    Using date and time data in Excel 142

    Charting dates and times in Excel 143

    Using Line charts 146

    Using XY (Scatter) charts 151

    Making Your Data Dance with Pivot Charts 152

    Using Two Value Axes 157

    Chapter 10 Forecasting with Excel's Data Analysis Add-in 159

    Installing Add-ins: Is the Add-in Even There? 160

    Using Moving Averages 162

    Moving day: Getting from here to there 163

    Moving averages and stationary baselines 164

    Using Exponential Smoothing 165

    Using the Regression Tool 168

    Chapter 11 Basing Forecasts on Regression 173

    Deciding to Use the Regression Tool 174

    Adopting the Regression approach 175

    Using more than one predictor variable 177

    Understanding the Data Analysis Add-in's Regression Tool 178

    Checking the forecast errors 182

    Plotting your actual revenues 183

    Understanding confidence levels 184

    Avoiding a zero constant 185

    Using Multiple Regression 186

    New predictor with forecast variable 187

    New predictor with existing variable 188

    Part 4 Making Advanced Forecasts 189

    Chapter 12 Entering the Formulas Yourself 191

    About Excel Formulas 192

    Doing it yourself: Why bother? 192

    Getting the syntax right 198

    Using Insert Function 199

    Understanding Array Formulas 205

    Choosing the range for the array formula 206

    Excel's three-finger salute: Ctrl+Shift+Enter 207

    Recognizing array formulas 208

    A special problem with array formulas 209

    Using the Regression Functions to Forecast 210

    Using the LINEST function 210

    Selecting the right range of cells 213

    Getting the statistics right 213

    Using the TREND function 215

    Chapter 13 Using Moving Averages 219

    Choosing the Length of the Moving Average 220

    Signaling: Left turn coming up? 220

    A little less noise, please 221

    Stepping it up 224

    Reacting Quickly versus Modeling Noise 226

    Getting a smoother picture 227

    Calculating and charting moving averages 228

    Using the Data Analysis Add-in to Get Moving Averages 229

    Using the Data Analysis add-in's Moving Average tool 230

    Charting residuals 234

    Chapter 14 Changing Horses: From Moving Averages to Smoothing 237

    Losing Early Averages 238

    Understanding Correlation 240

    When did they start going together? 240

    Charting correlated data 243

    Understanding Autocorrelation 244

    Calculating autocorrelation 253

    Diagnosing autocorrelation 254

    Chapter 15 Smoothing: How You Profit from Your Mistakes 259

    Correcting Errors: The Idea Behind Smoothing 260

    Adjusting the forecast 260

    Why they call it "exponential smoothing" 263

    Fooling around with the smoothing constant 266

    Using the Smoothing Tool's Formula 269

    Getting a forecast from the Exponential Smoothing tool 269

    Modifying the smoothing constant 273

    Finding the Smoothing Constant 275

    Developing the yardstick 275

    Minimizing the square root of the mean square error 278

    Problems with Exponential Smoothing 282

    Losing an observation at the start 282

    The Regression tool's standard errors: They're wrong 283

    Chapter 16 Fine-Tuning a Regression Forecast 285

    Doing Multiple Regression 286

    Using more than one predictor 286

    The thinking person's approach to multiple regression 292

    Interpreting the coefficients and their standard errors 296

    Getting a Regression Trendline into a Chart 301

    Evaluating Regression Forecasts 306

    Using autoregression 306

    Regressing one trend onto another 309

    Chapter 17 Managing Trends 311

    Knowing Why You May Want to Remove the Trend from a Baseline 312

    Understanding why trend is a problem 312

    Diagnosing a trend 313

    Getting a Baseline to Stand Still 315

    Subtracting one value from the next value 316

    Dividing one value by another 318

    Getting rates 320

    The downside of differencing 321

    And All the King's Men: Putting a Baseline Together Again 325

    Chapter 18 Same Time Last Year: Forecasting Seasonal Sales 329

    Doing Simple Seasonal Exponential Smoothing 330

    Relating a season to its ancestors 330

    Using the smoothing constants 334

    Getting Farther into the Baseline 338

    Calculating the first forecast 338

    Smoothing through the baseline level 341

    Tis the seasonal component 342

    Finishing the Forecast 344

    Modifying the formulas 344

    Using the worksheet 345

    Using the workbook 346

    Excel 2016's new Forecast Sheet 348

    Part 5 The Part of Tens 349

    Chapter 19 Ten Fun Facts to Know and Tell about Array Formulas 351

    Entering Array Formulas 352

    Using the Shift Key 352

    Noticing the Curly Brackets 354

    Using INDEX to Extract a Value from an Array Formula's Result 354

    A Quick Route to Unique Values 356

    Selecting the Range: LINEST 358

    Selecting the Range: TRANSPOSE 358

    Selecting a Range: TREND 359

    Editing an Array Formula 361

    Deleting Array Formulas 362

    Chapter 20 The Ten Best Excel Tools 363

    Cell Comments 363

    AutoComplete 364

    Macro Security 365

    The Customizable Toolbar 366

    Evaluate Formula 367

    Worksheet Protection 368

    Unique Records Only 369

    Using the Fill Handle 369

    Quick Data Summaries 370

    Help with Functions 370

    Index 373

    Customer Reviews

    Most Helpful Customer Reviews

    See All Customer Reviews