6-1

McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved. Market Potential and Sales Forecasting Chapter 06

6-3 Forecasts versus Potential

6-4 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

6-5 Deriving Potential Estimates

6-6 Useful Sources for Potential Estimates  Government Sources  Trade Associations  Private Companies  Financial and Industry Analysts  Popular Press  The Internet

6-7 New or Growing Product Potential  Relative Advantage  Compatibility  Risk

6-8 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

6-9 Market Potential: Electric Coil

6-10 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

6-11 Scenario-Based Forecasts

6-12 Judgment-based Forecasting Methods  Naïve extrapolation  Sales force composite  Jury of expert opinion  Delphi method  Electronic Markets

6-13 Summary of Forecasting Methods

6-14 Graphical Eyeball Forecasting

6-15 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

6-16 Time-Series Forecasting Methods  Moving Averages  Exponential Smoothing  Regression Analysis

6-17 Potential Customers by Industry and Size

6-18 Sample Data

6-19 Times-Series Extrapolation

6-20 Time-Series Regression Example

6-21 Trial over Time for a New Product

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  Customer status and traits  “Our” marketing programs  Competitive behavior  General environment  Collect data  Analyze the data

6-26 Cereal Sales Data (monthly)

6-27 Cereal Data

6-28 Cereal Data Correlation Matrix*

6-29 Regression Results: Cereal Data*

6-30 Format for Reporting a Regression Model Based Forecast

6-31 The Impact of Uncertain Predictors on Forecasting