Ch 8 M Segment 3of3 Sales Forecast 1Aug06 n21

8/6/2019 Ch 8 M Segment 3of3 Sales Forecast 1Aug06 n21

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Evaluation of Market Segments(Part 3 of 3)

Evaluation of Market Segments(Part 3 of 3)

Methods to Estimate the Market &Company Sales Potential (Macro Analysis)

Top down approach

Bottom up approach

Assumptions in Selecting a Target Market (Micro)

Customers wants differ enough to warrantmarket segmentation (response elasticity)

Analysis of competition & costs indicate the firmhas resources and skills to compete effectively.


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Sales

Potential

Sales

Forecast

Sales

Level

25

15

1

Level

Planned

0 3 6

Maximum

Resources

Mktg Effort

(000)

Sales Potential--Sales Estimation:

Evaluation of Market SegmentsEvaluation of Market Segments


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Types of Sales ForecastingTypes of Sales Forecasting

TYPES OF

SALES

FORECASTS

Executive

JudgementMarket

TestsSurveys

Correlation

and Regression

AnalysisTime Series

Analysis


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Sales Forecasts MethodsSales Forecasts Methods

Sales Forecast The amount of a product a company expects to sell

during a specific period at a specified level of marketing

activities Common Forecasting Techniques

Executive judgment Based on the intuition of the firms managers

Surveys Sales force forecasting survey

Customer forecasting

Expert forecasting survey

Delphi technique (panel of experts)


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Sales Forecast MethodsSales Forecast Methods

Common Forecasting Techniques (contd)

Time-series analysis

Patterns in historical data yield information foruse in trend analysis

Trend analysis

Cycle analysis

Seasonal analysis

Random factoranalysis


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Common Forecasting Techniques

Regression Analysis - Predicting sales

based on the relationship between past salesand one or more variables.

Market Tests - Making a product availablein the marketplace and measuring purchases

and consumer responses

Using Multiple Forecasting Methods - acombination of forecasting methods mayresult in greater accuracy

Sales Forecasts MethodsSales Forecasts Methods


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Market Forecasts of the ChangingPurchasing Habits of U.S. HouseholdsMarket Forecasts of the ChangingPurchasing Habits of U.S. Households

25

20

15

10

5

01970 1980 1990 2004

Year

Medical

Recreation

Food

Clothing

SOURCE: Vision for the New Millennium . . .(Atlanta: Kurt Salmon Associates,1997). Used with permission

Applied

Marketing


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USCensus,99statab (3Feb01)

Food & Income SegmentationFood & Income Segmentation

1993 1996 1999 %

Income $34,868 38,014 43,951 +26

Food $4,399 4,698 5,031 +15

- at home $2,735 2,876 2,915 +6

- out ofhome

$1,664 1,823 2,116 +27

Consumer expenditure survey


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Franchise Sales Forecasts:Franchise Sales Forecasts:

Less Dough?Less Dough?

-2

0

2

4

6

8

10

12

1st Qtr -

FY2003

2nd Qtr 3rd Qtr 4th Qtr 1st Qtr -

FY2004

2nd Qtr

Average per-store Sales

Same storesales

*Wall Street Journal

Source: WSJ, 16 Sept.03

Case example: Cannibalism


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Macro Market Forecast: The Number ofpeople per fast food restaurantMacro Market Forecast: The Number ofpeople per fast food restaurant

2000

1500

1000

500

01980 1985 1990 1995 2002

Year

Fast Food

SOURCE:. . Wall Street Journal,


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Globalization & CompetitionGlobalization & Competition

Thought Leadership and

the Future of the MBAApplied

Marketing


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One percent INCREASE in US

GNP yields

0.6 percent INCREASE in the

number of business mastersawarded three years later

One percent INCREASE in thenumber of undergraduatedegrees awarded yields

0.9 percent INCREASE in thenumber of business mastersawarded two years later

One percent INCREASE in theratio of business bachelorsdegrees to total undergraduatedegrees yields

0.7 percent increase in the totalnumber of business mastersawarded

Source: Doti, J.L. & Tuggle, F. D. (July/August, 2005)

Impact of the EconomyImpact of the Economy


the Future of the MBA




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Masters Degree Providers (U.S.)Masters Degree Providers (U.S.)

607

695 747

810850

0

100

200300

400

500

600

700

800

900

1,000

92 96 99 01 03Number

f

utionsawar

ing10o

morebus

inessmastersde

grees

Source: AACSB analysis of Department of Education data

Estimate



Estimate


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What does this mean?

To create sustainable value, schools must

Define a focal point for excellence Select feasible and effective strategies

Distinguish themselves locally

Achieve global recognition Demonstrate learning achievement

Collaborate internationally






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Regression Analysis


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Bivariate Linear Regression

A measure of linear association thatinvestigates a straight-line relationship

Y = a + FX where . . . Y is the dependent variable

X is the independent variable

a and F are two constants to beestimated

Useful in forecasting


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Y intercept

a

The point at which a regression

line intercepts the Y-axis

F

The inclination of a regression line ascompared to a base line

Rise over run

(- notation for a change in

Slope


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Y

Scatter Diagram and Eyeball Forecast160

150

140

130

120

110

100

90

80

70 80 90 100 110 120 130 140 150 160 170 180 190

X

My line

Your line

Copyright 2000 Harcourt, Inc. All rights reserved.


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130

120

110

100

90

80

80 90 100 110 120 130 140 150 160 170 180 190

X

Y

Regression Line and Slope


XaY F !

X(

Y(


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X

Y

160

150

140

130

120

110

100

90

80

70 80 90 100 110 120 130 140 150 160 170 180 190

Y hat or

Dealer 3

Actual Y or

Dealer 7

Y hat or Dealer 7

Actual Y or

Dealer 3

Least-Squares Regression Line



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130

120

110

100

90

80

80 90 100 110 120 130 140 150 160 170 180 190

X

Y

}}{Deviation not explained

Total deviation

Deviation explained by the regression

Y

Scatter Diagram o Explained and

Unexplained Variation


Ch 8 M Segment 3of3 Sales Forecast 1Aug06 n21

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