6-1
Market Potential and Sales
Forecasting
Chapter 06
McGraw-Hill/Irwin
Copyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved.
Forecasts versus Potential
6-3
Five major uses of potential estimates
 To make entry/exit decisions
 To make resource level decisions
 To make location and other
resource allocation decisions
 To set objectives and evaluate
performance
 As an input to forecasts
6-4
Deriving Potential Estimates
6-5
Useful Sources for Potential Estimates






Government Sources
Trade Associations
Private Companies
Financial and Industry Analysts
Popular Press
The Internet
6-6
New or Growing Product Potential
 Relative Advantage
 Compatibility
 Risk
6-7
Methods of Estimating Market and
Sales Potential
 Determine who are the potential
buyers or users of the product
 Determine how many are in each
potential group of buyers defined by
step 1
 Estimate the purchasing or usage
rate
6-8
Market Potential: Electric Coil
6-9
Uses of Sales Forecasts
 To answer “what if” questions
 To help set budgets
 To provide a basis for a monitoring
system
 To aid in production planning
 By financial analysts to value a
company
6-10
Scenario-Based Forecasts
6-11
Judgment-based Forecasting Methods





Naïve extrapolation
Sales force composite
Jury of expert opinion
Delphi method
Electronic Markets
6-12
Summary of Forecasting Methods
6-13
Graphical Eyeball Forecasting
6-14
Customer-Based Methods
 Market Testing
 Situations in which potential
customers are asked to respond to a
product or product concept
 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
6-15
Time-Series Forecasting Methods
 Moving Averages
 Exponential Smoothing
 Regression Analysis
6-16
Potential Customers by Industry and
Size
6-17
Sample Data
6-18
Times-Series Extrapolation
6-19
Time-Series Regression Example
6-20
Trial over Time for a New Product
6-21
Model-Based Methods
 Regression analysis
 Leading indicators
 Econometric models
6-22
Forecasting Method Usage
6-23
Use of New-Product Forecasting
Techniques by All Responding Firms
6-24
Developing Regression Models
 Plot sales over time
 Consider the variables that are
relevant to predicting sales




Customer status and traits
“Our” marketing programs
Competitive behavior
General environment
 Collect data
 Analyze the data
6-25
Cereal Sales Data (monthly)
6-26
Cereal Data
6-27
Cereal Data Correlation Matrix*
6-28
Regression Results: Cereal Data*
6-29
Format for Reporting a Regression
Model Based Forecast
6-30
The Impact of Uncertain Predictors on
Forecasting
6-31