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McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved.

Market Potential and Sales Forecasting

Chapter 06

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Forecasts versus Potential

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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

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Deriving Potential Estimates

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Useful Sources for Potential Estimates

Government Sources Trade Associations Private Companies Financial and Industry Analysts Popular Press The Internet

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New or Growing Product Potential Relative Advantage Compatibility Risk

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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

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Market Potential: Electric Coil

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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

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Scenario-Based Forecasts

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Judgment-based Forecasting Methods

Naïve extrapolation Sales force composite Jury of expert opinion Delphi method Electronic Markets

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Summary of Forecasting Methods

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Graphical Eyeball Forecasting

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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

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Time-Series Forecasting Methods Moving Averages Exponential Smoothing Regression Analysis

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Potential Customers by Industry and Size

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Sample Data

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Times-Series Extrapolation

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Time-Series Regression Example

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Trial over Time for a New Product

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Model-Based Methods Regression analysis Leading indicators Econometric models

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Forecasting Method Usage

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Use of New-Product Forecasting Techniques by All Responding Firms

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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

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Cereal Sales Data (monthly)

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Cereal Data

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Cereal Data Correlation Matrix*

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Regression Results: Cereal Data*

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Format for Reporting a Regression Model Based Forecast

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The Impact of Uncertain Predictors on Forecasting