7-1. McGraw-Hill/Irwin Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved. 7 Market Potential And Sales Forecasting.

7-1

McGraw-Hill/Irwin Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved.

7

Market Potential

And Sales Forecasting

7-3

Forecasts vs. Potential

Expectations Possibilities

Firm/Brand Sales Forecast Sales Potential

Category Market Forecast Market Potential

7-4

Major Uses of Potential Estimates

1. To make entry / exit decisions

2. To make resource level decisions

3. To make location and other resource allocation decisions

4. To set objectives and evaluate performance

5. As an input to forecasts

7-5

Deriving Potential Estimates

Potential estimate

Past sales data

Secondary data

Surveys/ Primary data

Model/Statistical method

Judgment

Secondary sources

Data

Calculations Result

7-6

Useful Sources for Potential Estimates

• Government Sources

• Trade Associations

• Private Companies

• Financial and Industry Analysts

• Popular Press

• The Internet

7-7

New or Growing Product Potential

• Relative Advantage• Is the new product superior in key benefits?• To what degree?

• Compatibility• What level of change is required to understand and use

a new product?• For customers? Intermediaries? The company?

• Risk• How great is the risk involved?• What is the probability someone will buy a new

product?

7-8

Methods of Estimating Market and Sales Potential

• Analysis-Based Estimates1. Determine the potential buyers or users of the

product

2. Determine how many are in each potential group of buyers defined by step 1

3. Estimate the purchasing or usage rate

7-9

Market Potential: Electric CoilSIC Industry Purchases

of ProductNumber

of Workers

Average Purchase/Worker

National Number

of Workers

Estimated Potential

3611 Electrical Measuring

$160 3,200 $.05 34,913 $1,746

3612 Power Transformers

5,015 4,616 1.09 42,587 46,249

3621 Motors and Generators

2,840 10,896 .26 119,330 30,145

3622 Electrical Industry Controls

4,010 4,678 .86 46,805 40,112

$12,025 $119,252

7-10

How Are Sales Forecasts Used?

1. To answer “what if” questions

2. To help set budgets

3. To provide a basis for a monitoring system

4. To aid in production planning

5. By financial analysts to value a company

7-11

Judgment-based Forecasting Methods

• Naïve extrapolation

• Sales force composite

• Jury of expert opinion

• Delphi method

7-12

Customer-Based Forecasting Methods

• Market testing• Situations in which potential customers are

asked to respond to a product concept• Mall Intercept Surveys• Focus Groups

• Market surveys• A form of primary market research in which

potential customers are asked to give some indication of their likelihood of purchasing a product

7-13

Time-Series Forecasting Methods

• Moving Averages

• Exponential Smoothing

• Regression Analysis

7-14

Time-Series Extrapolation

8090

100110120130140150160170180190200

1 2 3 4 5 6 7 8 9 10 11••• •

•

•

• •1 • 12

••

• • • • • 174.5

s = 85.4 + 9.88 (time)

Time

Sales

7-15

Model-Based Methods

• Regression analysis

• Leading indicators

• Econometric models

7-16

Forecasting Method Usage

7-17

Use of New Product Forecasting Techniques by All Responding Firms

7-18

Developing Regression Models

• Plot Sales Over Time• Consider the Variables that Are Relevant to

Predicting Sales• Collect Data• Analyze the Data

• Examine the correlations among the independent variables

• Run the regression• Determine the significant predictors

7-19

Cereal Sales Data (Monthly)

7-20

Cereal Data

7-21

Cereal Data Correlation Matrix*

The numbers in each cell are presented as: correlation, (sample size), significant level

7-22

Regression Results: Cereal Data*

Numbers in ( ) are standard errors

7-23

Format for Reporting a Regression Model Based Forecast

7-24

The Impact of Uncertain Predictors on Forecasting

7-25

Potential Energy Bar Customers

7-26

Power Bar Data

7-27

Bass Model: PDA Actual vs. Predicted

7-28

Sample Format for Summarizing Forecasts

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Scenario-Based Forecasts

7-30

Summary of Forecasting Methods

7-31

Graphical Eyeball Forecasting

Time

Sales

• ••

••

•

•• • •

Range ƍ

Forecast

7-32

Potential Customers by Industry and Size

7-33

Sample Data

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Time-Series Regression Example

1 1002 1103 1054 1305 1406 1207 1608 175

Time Sales

Input Data

Computer/ Calculator

Sales=85.4+9.88 (time)

Prediction

94.3 105.2 115.0 124.9 134.8 144.7 154.6 164.4

Ŝ

7-35

Trial over Time for a New Product

Time

Nu

mb

er w

ho

try

a n

ew

pro

du

ct f

or f

irst

tim

e

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Summary of Forecasting Methods (cont.)