Transcript Chapter 6.ppt

6-1
6
Market Potential
and
Sales Forecasting
McGraw-Hill/Irwin
Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved.
Forecasts vs. Potential
Expectations
Firm/Brand
Category
Sales Forecast
Possibilities
Sales Potential
Market Forecast Market Potential
6-3
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
6-4
Deriving Potential Estimates
Data
Calculations
Result
Secondary sources
6-5
Useful Sources for Potential Estimates
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Government Sources
Trade Associations
Private Companies
Financial and Industry Analysts
Popular Press
The Internet
6-6
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?
6-7
Methods of Estimating Market and Sales Potential

Analysis-Based Estimates
1. 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
6-8
Market Potential: Electric Coil
SIC
Industry
Purchases
of Product
Number
of
Workers
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
Average National Estimated
Purchase Number Potential
/Worker
of
Workers
$119,252
6-9
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
6-10
Scenario-Based Forecasts
6-11
Summary of Forecasting Methods
6-12
Summary of Forecasting Methods (cont.)
6-13
Judgment-based Forecasting Methods
 Naïve extrapolation
 Sales force composite
 Jury of expert opinion
 Delphi method
6-14
Graphical Eyeball Forecasting
Range
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ƍ
Forecast
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•
Time
6-15
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
6-16
Time-Series Forecasting Methods
 Moving Averages
 Exponential Smoothing
 Regression Analysis
6-17
Potential Customers by Industry and Size
6-18
Sample Data
6-19
Time-Series Extrapolation
Sales
200
190
180
170
160
150
140
130
120
110
100
90
80
s = 85.4 + 9.88
(time)
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4
5
6
7
• 12
8
9
10
11
Time
6-20
Time-Series Regression Example
Prediction
Input Data
Time Sales
1
2
3
4
5
6
7
8
100
110
105
130
140
120
160
175
Computer/
Calculator
Sales=85.4+9.88 (time)
Ŝ
94.3
105.2
115.0
124.9
134.8
144.7
154.6
164.4
6-21
Number who try a new
product for first time
Trial over Time for a New Product
Time
6-22
Model-Based Methods
 Regression analysis
 Leading indicators
 Econometric models
6-23
Forecasting Method Usage
6-24
Use of New Product Forecasting
Techniques by All Responding Firms
6-25
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
6-26
Cereal Sales Data (Monthly)
6-27
Cereal Data
6-28
Cereal Data Correlation Matrix*
The numbers in each cell are presented as: correlation, (sample size), significant level
6-29
Regression Results: Cereal Data*
Numbers in ( ) are standard errors
6-30
Format for Reporting
a Regression Model Based Forecast
6-31
The Impact of Uncertain Predictors on Forecasting
6-32
Potential Energy Bar Customers
6-33
Power Bar Data
6-34
Bass Model: PDA Actual vs. Predicted
6-35
Sample Format for Summarizing Forecasts
6-36