C H A P T E R

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C H A P T E R

Market Potential and Sales Forecasting6

McGraw-Hill/Irwin © 2002 The McGraw-Hill Companies, Inc., All Rights Reserved.

Major TopicsMajor Topics

1. Potential versus Forecasting

2. Estimating Market and Sales Potential

3. Sales Forecasting & Methods*

4. Forecasting Method Usage*

5. What You need: Forecast (market and your firm)

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Definitions of Key TermsDefinitions of Key Terms

1. Potential Maximum sales (Saturation) attainable under a

given set of conditions within a specified period of time

2. Demand • Customer wants that are backed by buying power

3. Forecast Amount of sales expected to be achieved under a

set of conditions within a specified period of time

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Potential versus ForecastsPotential versus Forecasts

Expectations Possibilities

Firm/Brand

Category

Sales Forecast Sales Potential

Market Forecast Market Potential

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Marketing Expenditure

Demand

Market Potential - Prosperity

Market Minimum

Measuring PotentialMeasuring Potential

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Market PotentialMarket Potential

1. Hard to get it right 2. Fixed or Dynamic?* 3. Major Uses of Market Potential Estimates To make entry / exit decisions To make resource-level decisions (firm level) To make location and other resource allocation

decisions (product level) To set objectives and evaluate performance As a base for sales forecasting

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Market Potential (Cont’d)Market Potential (Cont’d)

4. Major Drivers of Potential Relative Advantage Compatibility Risk Role of Similar Products (caveat)

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Estimating Market PotentialEstimating Market Potential

1. Determine the potential buyers or users of the product.

2. Determine how many individual customers are in the potential groups of buyers defined in step 1.

3. Estimate the potential purchasing or usage rate.

4. 2 X 3 Market potential

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Market Potential: Electric CoilMarket Potential: Electric Coil

SIC Industry Purchases of Product

Number 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

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Area PotentialArea Potential

Sales and Marketing Management Magazine:

Buying Power Index

: .2 * (percentage of the population of the area)

+ .3 * (percentage of the retail sales of the area)

+ .5 * (percentage of the disposable income)

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Sales ForecastingSales Forecasting

1. How Are Forecasts Used? 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

2. Four Major Variables to Consider Customer Behavior Past and Planned Product Strategies Competition Environment (ex: national economy)

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Sales Forecasting Methods*Sales Forecasting Methods*

Judgment methods, which rely on pure opinions.

Customer-based methods, which use customer data.

Sales Extrapolation methods.Association/causal methods, model

relating market factors to sales.

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1. Judgmental Methods1. Judgmental Methods

Naïve extrapolation - takes most current sales and adds a judgmentally determined x%.

Sales Force - ask salespeople calling on retail account to forecast sales.

Executive Opinion - marketing manager opinion to predict sales based on experience.*

Delphi Method - a jury of experts sent a questionnaire and estimates sales and justifies the number.

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2. Customer-based Methods2. Customer-based Methods

Market testing - uses primary data collection methods to predict sales.

Market surveys - using purchase intention questions to predict demand.

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3. Sales Extrapolation Methods3. Sales Extrapolation Methods

Extrapolation - linearly extrapolates time series data.

Moving Averages - uses averages of historical sales figures to make a forecast.

Exponential Smoothing - relies on the historical sales data and is more complicated than the moving average.

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Time-Series Time-Series ExtrapolationExtrapolation

8090

100110120130140150160170180190200

1 2 3 4 5 6 7 8 9 10 11

••• •

•

•

• ••

••

• • • • • 174.5

s = 85.4 + 9.88 (time)

Time

Sales

•

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Moving AverageMoving Average

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4. Association/Causal Methods4. Association/Causal Methods

Correlation.Regression Analysis : Time + Other

VariablesLeading Indicators.Econometric Models: Multiple Equations

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Forecasting Method UsageForecasting Method Usage

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Example:Example:Developing Regression ModelsDeveloping Regression Models

Plot Sales Over TimeConsider the Variables that Are Relevant to

Predicting SalesCollect DataAnalyze the Data

Examine the correlations among the independent variables

Run the regressionDetermine the significant predictors

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Cereal Sales Data Cereal Sales Data (Monthly)(Monthly)

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Cereal DataCereal Data

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Cereal Data Correlation Matrix*Cereal Data Correlation Matrix*

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

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Regression Results: Cereal Data*Regression Results: Cereal Data*

Numbers in ( ) are standard errors

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Format for Reporting Format for Reporting a Regression Model Based Forecast*a Regression Model Based Forecast*

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The Impact of Uncertain Predictors on The Impact of Uncertain Predictors on ForecastingForecasting

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Using Forecasts in PracticeUsing Forecasts in Practice

Some points to rememberDo sensitivity analysisExamine Big ResidualsYou will miss turning points Report Format

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Sample Format for Summarizing Sample Format for Summarizing ForecastsForecasts