The Analysis ToolPak in Excel Sales Forecasting

The Analysis ToolPak in Excel sales forecasting figures out what's going on

with your data without your having to enter formulas. Excel's Analysis ToolPak

has three useful tools for directly forecasting — Moving Average, Exponential

Smoothing, and Regression — along with others that can help. Here's a list of

some tools that are part of the Analysis ToolPak:

Tool What It Does

ANOVA There are actually three different ANOVA tools. None is

specifically useful for forecasting, but each of the tools can

help you understand the data set that underlies your

forecast. The ANOVA tools help you distinguish among

samples — for example, do people who live in Tennessee

like a particular brand of car better than those who live in

Vermont?

Correlation This tool is an important one, regardless of the method you

use to do your forecast. If you have more than one variable,

it can tell you how strongly the two variables are related

(plus or minus 1.0 is strong, 0.0 means no relationship). If

you have only one variable, it can tell you how strongly one

time period is related to another.

Descriptive

Statistics

Use the Descriptive Statistics tool to get a handle on things

like the average and the standard deviation of your data.

Understanding these basic statistics is important so you

know what's going on with your forecasts.

Exponential

Smoothing

I hate this tool's name — it sounds ominous and

intimidating, which the tool is not. When you have just one

variable — something such as sales revenue or unit sales

— you look to a previous actual value to predict the next

one (maybe the previous month, or the same month in the

previous year). All this tool does is adjust the next forecast

by using the error in the prior forecast.

Moving

Average

A moving average shows the average of results over time.

The first one might be the average for January, February,

and March; the second would then be the average for

February, March, and April; and so on. This method of

forecasting tends to focus on the signal (what's really going

on in the baseline) and to minimize the noise (random

fluctuations in the baseline).

Regression Regression is closely related to correlation. Use this tool to

forecast one variable (such as sales) from another (such as

date or advertising). It gives you a couple of numbers to

use in an equation, like Sales = 50000 + (10 * Date).

Excel Sales Forecasting Functions

Give these sales forecasting functions in Excel a good baseline and you can

get a handle on future sales business. Some Excel forecast functions and

their actions appear in the following chart — keep it handy:

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get a handle on future sales business. Some Excel forecast functions and

their actions appear in the following chart — keep it handy:

Function What It Does

CORREL The worksheet version of the Analysis ToolPak's Correlation

tool. The difference is that CORREL recalculates when the

input data changes, and the Correlation tool doesn't.

Example: =CORREL(A1:A50, B1:B50). Also, CORREL gives

you only one correlation, but the Correlation tool can give

you a whole matrix of correlations.

LINEST You can use this function instead of the Analysis ToolPak's

Regression tool. (The function's name is an abbreviation of

linear estimate.) For simple regression, select a range of two

columns and five rows. You need to array-enter this function.

Type, for example, =LINEST(A1:A50, B1:B50,,TRUE) and

then press Ctrl+Shift+Enter.

TREND This function is handy because it gives you forecast values

directly, whereas LINEST gives you an equation that you

have to use to get the forecast. For example, use

=TREND(A1:A50,B1:B50,B51) where you're forecasting a

new value on the basis of what's in B51.

FORECAST The FORECAST function is similar to the TREND function.

The syntax is a little different. For example, use

=FORECAST(B51,A1:A50,B1:B50) where you're forecasting

a new value on the basis of the value in B51. Also,

FORECAST handles only one predictor, but TREND can

handle multiple predictors.

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