3. Marketing Information sytem (Marketing Enviornment) and Demand forecasting.ppt

Kotler • Keller
Phillip Kevin Lane
Marketing Management • 14e

Collecting Information and
Forecasting Demand

Copyright © 2012 Pearson Education, Inc. Publishing as Prentice Hall Slide 3 of 28
Discussion Questions
1. What are the components of a modern
marketing information system?
2. What are useful internal records for such a
system?
3. What makes up a marketing intelligence system?
4. What are some influential macroeconomic
developments?
5. How can companies accurately measure and
forecast demand?

Collecting Information
Customers
Competitors
External Factors

Marketing Information System
People
Equipment
Procedures

Insight
Marketing Information System
Marketing Research
Marketing Intelligence
Internal Records
Order-to-Payment Cycle
Databases / Data Mining
Sales Information Systems
News and Trade Publications
Meet with customers, suppliers, distributors,
and other managers
Monitor social media sites

Internal Records

Marketing Intelligence
News and Trade Publications
Meet with customers, suppliers, distributors,
and other managers
Monitor social media
sites

Improving Marketing Intelligence
Sales Force
External Experts
Establish industry network
Customer Advisory Panel

Marketing Intelligence & the Internet
Independent Online Forums
Distributor or sales agents feedback sites
Customer review and expert opinion
sites
Customer complaint sites

Using Marketing Intelligence
Share Information
Quickly

Analyzing the Environment

Marketing Environmental Variables

Analyzing the Macroenvironment

Needs and Trends
Fad
Megatrend
Trend

Major Environmental Forces
Economic
Sociocultural
Natural
Technological
Political-Legal
Demographics

Demographic Environment
Worldwide population growth
Population age mix
Ethnic and other markets
Educational Groups
Household patterns

The World as a Village
If the world were a village of 100 people:
61 – Asian (20 Chinese, 17 Indian)
18 – Unable to read (33 have cell phones)
18 – Under 10 years of age (11 over 60 years old)
18 – Cars in the village
63 – Inadequate sanitation
67 – Non-Christian
30 – Unemployed or underemployed
53 – Live on less than $2 a day
26 – Smoke
14 – Obese
01 – Have AIDS
Source: David J. Smith and Shelagh Armstrong, If the World Were a Village: A
Book About the World’s People, 2nd ed. (Tonawanda, NY: Kids Can Press, 2002)

Economic Environment
Consumer
Psychology
Income
Distribution

Ourselves
Others
Universe
Organizations
Society
Nature
Sociocultural
Environment

Natural Environment
Environmental Regulations

Technological Environment
Accelerated pace of change
Unlimited opportunities
R&D Spending

Political-Legal Environment
Special Interest Groups
Government Agencies
Laws

Forecasting and Demand Measurement
Market
- Size
- Growth
- Profit potential

Market Types
Potential Market
Available Market
Target Market
Penetrated Market

Ninety Types of Demand Measurement

Demand Measurement
Market Demand
Company Demand

Market Demand Functions

Estimating Current Demand
Area market potential
Total market potential
Potential
Buyers
Average
purchase
quantity
Average
price
X X
Chain-ratio method
Demand
for new
light beer
Population
Average percentage of income spent on:
= X Food X Beverages X
Alcoholic
beverages
X
Expected % of
spending on
Light beer

Estimating Future Demand
Sales Force Opinions
Forecasting
Past Sales Analysis
Buyer’s Intentions
Expert Opinions

35
Forecasting Demand
• Simplest Method is EXTRAPOLATION
Time
Volume of
Sales
Present
Past
Future

36
Time Series Analysis
• The DECOMPOSITION METHOD
• Xt = Tt + St + It
– Xt = sales volume in period t
– Tt = trend value for period t
– St = seasonal Component for period t
– It = irregular/unpredictable component for period t

37
How to forecast using the
decomposition method?
• 1. Estimate the trend factor
– use regression, with time (the number of seasons from
time zero) as the independent variable and sales volume
as the dependent, OR
– just use a straight-line extrapolation
• 2.Calculate the trend value for each period/season to
date (Tt)

38
How to forecast using the
decomposition method?
• 3.For each season/period, calculate
• Actual - Trend = Seasonal + Irregular

39
The Next Steps?
• 4. Collect together the (Seasonal + Irregular) for each
season (Add together the S+I for all of the Spring
seasons, all of the Summers, etc)
• 5. The average (Seasonal + Irregular) for the Spring
seasons is your estimate of the Seasonal component
for Spring, and the same for the other seasons.

40
How to Make the Forecast?
• 6. For any future time-period, first calculate
the trend value
– e.g for Spring 2003, first calculate the trend value
for that quarter
• 7. Add in the seasonal element for
– this produces your estimate

41
What Are the Weaknesses?
• Forecasting based on time-series analysis assumes that time is the only
determinant of sales volume and that the link between time and volume
will stay the same in the future as in the past
• Tends to give poor results in times of instability, which is when you have
most need of accurate forecasts!
• There are many more sophisticated approaches to time series but in many
cases, ‘naïve’ methods give forecasts which are just as accurate

42
How To Evaluate the Forecast?
• Objectivity. Does the result depend on the data or on the person
making the forecast?
• Validity. How closely does a series of forecast estimates correlate with
the actual time series, for the time period used to make the forecast?
• Reliability. If we take different starting points for the forecast, do the
results stay approximately the same?
• Accuracy.How close are the forecasts to the actual figures, for the
period outside that used to generate the forecast?
• Confidence. Is there are high probability that we can accept the
results?
• Sensitivity.If we use the method to make forecasts using data with
very different patterns, do we get very different results?

43
Accuracy Is the Main Concern: How to
Measure It?
• Mean Error -but this could be zero if large positive
and large negative errors cancel each other out
• Mean Absolute Error
• Mean Square Error - to give a higher weighting to
bigger errors
• Root Mean Square Error - to give a result in the same
units as the original data

44
What Other Methods are Available?
• Barometric forecasting - leading indicators are used:
variables which change in advance of the variable
you wish to predict
• IDD traffic for forecasting international trade
• births for forecasting demand for primary schools,baby
clothes
• machine tool orders for forecasting national income
• new building starts for national income

45
• Market Surveys, whose usefulness depends on:
– cost of finding buyers
– buyers willingness to disclose their intentions
– buyers’ propensity to carry out their intentions
• Most useful for:
– Products where buyers plan ahead
– Products where potential buyers are a well-defined,
identifiable and small group
– New products where no past data is available

46
• Sales Force Opinion. Your sales force are closest to the
customer but:
– they may have incentives to distort their forecasts,
deliberately predicting low sales in order to increase
their bonuses and get lower sales targets:
– they may be unaware of broader developments, new
types of customer, macro-economic changes

47
• Expert Opinion: Ask industry analysts, consultants,
trade association members to make the forecast
– if this is done openly, there is a danger of ‘groupthink’
– an alternative is the ‘Delphi’ approach to expert
opinion
• ask a group of industry experts to write down forecasts
ANONYMOUSLY and to explain why they believe they are
correct
• circulate the forecasts to all those involved
• ask them all to revise their forecasts in the light of the
other experts’ opinion
– IN MANY CASES, DELPHI FORECASTS CONVERGE

48
What Other Methods are Available ?
• Market Testing
– Sales Wave Research: give the product to a group of
customers, measure their repeat buying rate. (May also
use this to compare the effect of different packaging, etc)
– Simulated Store Techniques: Give a group of target
customers some money to spend on the product, show
them your advertising, monitor their behaviour
– Test Marketing: make the product and sell it

Forecasting
• Predict the next number in the pattern:
a) 3.7, 3.7, 3.7, 3.7, 3.7, ?
b) 2.5, 4.5, 6.5, 8.5, 10.5, ?
c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?

Forecasting
• Predict the next number in the pattern:
a) 3.7, 3.7, 3.7, 3.7, 3.7,
b) 2.5, 4.5, 6.5, 8.5, 10.5,
c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5,
3.7
12.5
9.0

Outline
• What is forecasting?
• Types of forecasts
• Time-Series forecasting
– Naïve
– Moving Average
– Exponential Smoothing
– Regression
• Good forecasts

What is Forecasting?
 Process of predicting a future
event based on historical data
 Educated Guessing
 Underlying basis of
all business decisions
 Production
 Inventory
 Personnel
 Facilities

In general, forecasts are almost always wrong. So,
Why do we need to forecast?
Throughout the day we forecast very different
things such as weather, traffic, stock market, state
of our company from different perspectives.
Virtually every business attempt is based on
forecasting. Not all of them are derived from
sophisticated methods. However, “Best" educated
guesses about future are more valuable for
purpose of Planning than no forecasts and hence
no planning.

Decisions that Need Forecasts
Forecasting helps to decide -
• Which markets to pursue?
• What products to produce?
• How many people to hire?
• How many units to purchase?
• How many units to produce?
• And so on……

• Departments throughout the organization depend on forecasts to
formulate and execute their plans.
• Finance needs forecasts to project cash flows and capital requirements.
• Human resources need forecasts to anticipate hiring needs.
• Production needs forecasts to plan production levels, workforce, material
requirements, inventories, etc.
• Demand is not the only variable of interest to forecasters.
• Manufacturers also forecast worker absenteeism, machine availability,
material costs, transportation and production lead times, etc.
• Besides demand, service providers are also interested in forecasts of
population, of other demographic variables, of weather, etc.
Importance of Forecasting in OM

Common Characteristics of Forecasting
• Forecasts are rarely perfect
• Forecasts are more accurate for aggregated data
than for individual items
• Forecast are more accurate for shorter than longer
time periods

• Short-range forecast
– Usually < 3 months
• Job scheduling, worker assignments
• Medium-range forecast
– 3 months to 2 years
• Sales/production planning
• Long-range forecast
– > 2 years
• New product planning
Types of Forecasts by Time Horizon
Design
of system
Detailed
use of
system
Quantitative
methods
Qualitative
Methods

Forecasting During the Life
Cycle
Introduction Growth Maturity Decline
Sales
Time
Quantitative models
- Time series analysis
- Regression analysis
Qualitative models
- Executive judgment
- Market research
-Survey of sales force
-Delphi method

Types of Forecasting Models
• Qualitative (technological) methods:
– Forecasts generated subjectively by the forecaster
• Quantitative (statistical) methods:
– Forecasts generated through mathematical
modeling

Qualitative Forecasting Methods
Qualitative
Forecasting
Models
Market
Research/
Survey
Sales
Force
Composite
Executive
Judgement
Delphi
Method
Smoothing

Briefly, the qualitative methods are:
Executive Judgment: Opinion of a group of high level experts or
managers is pooled
Sales Force Composite: Each regional salesperson provides
his/her sales estimates. Those forecasts are then reviewed to
make sure they are realistic. All regional forecasts are then
pooled at the district and national levels to obtain an overall
forecast.
Market Research/Survey: Solicits input from customers
pertaining to their future purchasing plans. It involves the use of
questionnaires, consumer panels and tests of new products and
services.
Qualitative Methods

Delphi Method: As opposed to regular panels where the individuals involved are in
direct communication, this method eliminates the effects of group potential
dominance of the most vocal members. The group involves individuals from inside
as well as outside the organization.
Typically, the procedure consists of the following steps:
Each expert in the group makes his/her own forecasts in form of statements
The coordinator collects all group statements and summarizes them
The coordinator provides this summary and gives another set of
questions to each
group member including feedback as to the input of other experts.
The above steps are repeated until a consensus is reached.
.
Qualitative Methods

Qualitative Methods
Type Characteristics Strengths Weaknesses
Executive
opinion
A group of managers
meet & come up with
a forecast
Good for strategic or
new-product
forecasting
One person's opinion
can dominate the
forecast
Market
research
Uses surveys &
interviews to identify
customer preferences
Good determinant of
customer preferences
It can be difficult to
develop a good
questionnaire
Delphi
method
Seeks to develop a
consensus among a
group of experts
Excellent for
forecasting long-term
product demand,
technological
changes, and
Time consuming to
develop

Quantitative Forecasting Methods
Quantitative
Forecasting
Regression
Models
2. Moving
Average
1. Naive
Time Series
Models
3. Exponential
Smoothing
a) simple
b) weighted
a) level
b) trend
c) seasonality

Quantitative
Forecasting
Regression
Models
2. Moving
Average
1. Naive
Time Series
Models
3. Exponential
Smoothing
a) simple
b) weighted
a) level
b) trend
c) seasonality

Time Series Models
• Try to predict the future based on past data
– Assume that factors influencing the past will
continue to influence the future

Random
Seasonal
Trend
Composite
Time Series Models:
Components

Product Demand over Time
Year
1
Year
2
Year
3
Year
4
Demand
for
product
or
service

Product Demand over Time
Year
1
Year
2
Year
3
Year
4
Demand
for
product
or
service
Trend component
Actual demand
line
Seasonal peaks
Random
variation
Now let’s look at some time series approaches to forecasting…
Borrowed from Heizer/Render - Principles of Operations Management, 5e, and Operations Management, 7e

Quantitative
Models
2. Moving
Average
1. Naive
Time Series
Models
3. Exponential
Smoothing
a) simple
b) weighted
a) level
b) trend
c) seasonality

1. Naive Approach
 Demand in next period is the same as
demand in most recent period
May sales = 48 →
 Usually not good
June forecast = 48

2a. Simple Average
n
A
+
...
+
A
+
A
+
A
=
F 1
n
-
t
2
-
t
1
-
t
t
1
t


• Assumes an average is a good estimator of future
behavior
– Used if little or no trend
– Used for smoothing
Ft+1 = Forecast for the upcoming period, t+1
n = Number of periods to be averaged
A t = Actual occurrence in period t

2b. Simple Moving Average
You’re manager in Amazon’s electronics department.
You want to forecast ipod sales for months 4-6 using a
3-period moving average.
n
A
+
...
+
A
+
A
+
A
=
F 1
n
-
t
2
-
t
1
-
t
t
1
t


Month
Sales
(000)
1 4
2 6
3 5
4 ?
5 ?
6 ?

Month
Sales
(000)
Moving Average
(n=3)
1 4 NA
2 6 NA
3 5 NA
4 ?
5 ?
(4+6+5)/3=5
6 ?
n
A
+
...
+
A
+
A
+
A
=
F 1
n
-
t
2
-
t
1
-
t
t
1
t


You’re manager in Amazon’s electronics
department. You want to forecast ipod sales for
months 4-6 using a 3-period moving average.

What if ipod sales were actually 3 in
month 4
Month
Sales
(000)
Moving Average
(n=3)
1 4 NA
2 6 NA
3 5 NA
4 3
5 ?
5
6 ?
?

Forecast for Month 5?
Month
Sales
(000)
Moving Average
(n=3)
1 4 NA
2 6 NA
3 5 NA
4 3
5 ?
5
6 ?
(6+5+3)/3=4.667

Actual Demand for Month 5 =
7
Month
Sales
(000)
Moving Average
(n=3)
1 4 NA
2 6 NA
3 5 NA
4 3
5 7
5
6 ?
4.667
?

Forecast for Month 6?
Month
Sales
(000)
Moving Average
(n=3)
1 4 NA
2 6 NA
3 5 NA
4 3
5 7
5
6 ?
4.667
(5+3+7)/3=5

• Gives more emphasis to recent data
• Weights
– decrease for older data
– sum to 1.0
2c. Weighted Moving Average
1
n
-
t
n
2
-
t
3
1
-
t
2
t
1
1
t A
w
+
...
+
A
w
+
A
w
+
A
w
=
F 

Simple moving
average models
weight all previous
periods equally

2c. Weighted Moving Average: 3/6,
2/6, 1/6
Month Weighted
Moving
Average
1 4 NA
2 6 NA
3 5 NA
4 31/6 = 5.167
5
6 ?
?
?
1
n
-
t
n
2
-
t
3
1
-
t
2
t
1
1
t A
w
+
...
+
A
w
+
A
w
+
A
w
=
F 

Sales
(000)

2b. Weighted Moving Average: 3/6,
2/6, 1/6
Month Sales
(000)
Weighted
Moving
Average
1 4 NA
2 6 NA
3 5 NA
4 3 31/6 = 5.167
5 7
6
25/6 = 4.167
32/6 = 5.333
1
n
-
t
n
2
-
t
3
1
-
t
2
t
1
1
t A
w
+
...
+
A
w
+
A
w
+
A
w
=
F 


3a. Exponential Smoothing
• Assumes the most recent observations have the
highest predictive value
– gives more weight to recent time periods
Ft+1 = Ft + a(At - Ft)
et
Ft+1 = Forecast value for time t+1
At = Actual value at time t
a = Smoothing constant
Need initial
forecast Ft
to start.

3a. Exponential Smoothing – Example
1
Week Demand
1 820
2 775
3 680
4 655
5 750
6 802
7 798
8 689
9 775
10
Given the weekly demand
data what are the exponential
smoothing forecasts for
periods 2-10 using a=0.10?
Assume F1=D1
i Ai

Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
1
a =
F2 = F1+ a(A1–F1) =820+.1(820–820)
=820
i Ai Fi

Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
1
a =
F3 = F2+ a(A2–F2) =820+.1(775–820)
=815.5
i Ai Fi

Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
This process
continues
through week 10
1
a =
i Ai Fi

Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 793.00
4 655 801.95 725.20
5 750 787.26 683.08
6 802 783.53 723.23
7 798 785.38 770.49
8 689 786.64 787.00
9 775 776.88 728.20
10 776.69 756.28
What if the
a constant
equals 0.6
1
a = a =
i Ai Fi

Month Demand 0.3 0.6
January 120 100.00 100.00
February 90 106.00 112.00
March 101 101.20 98.80
April 91 101.14 100.12
May 115 98.10 94.65
June 83 103.17 106.86
July 97.12 92.54
August
September
What if the
a constant
equals 0.6
2
a = a =
i Ai Fi

Company A, a personal computer producer purchases generic parts
and assembles them to final product. Even though most of the
orders require customization, they have many common
components.
Thus, managers of Company A need a good forecast of demand so
that they can purchase computer parts accordingly to minimize
inventory cost while meeting acceptable service level. Demand data
for its computers for the past 5 months is given in the following
table.
3

Month Demand 0.3 0.5
January 80 84.00 84.00
February 84 82.80 82.00
March 82 83.16 83.00
April 85 82.81 82.50
May 89 83.47 83.75
June 85.13 86.38
July ?? ??
What if the
a constant
equals 0.5
3
a = a =
i Ai Fi

• How to choose α
– depends on the emphasis you want to place on
the most recent data
• Increasing α makes forecast more sensitive to
recent data
3a. Exponential Smoothing

Time Series Problem and
Solution
Simple Simple Weighted Exponential Exponential
Naïve Simple Moving Moving Moving Smoothing Smoothing
Period Orders (A) Forecast Average Average (N=3) Average(N=5) Average (N=3) (a = 0.2) (a = 0.5)
1 122 122 122
2 91 122 122 122 122
3 100 91 107 116 107
4 77 100 104 104 102 113 104
5 115 77 98 89 87 106 91
6 58 115 101 97 101 101 108 103
7 75 58 94 83 88 79 98 81
8 128 75 91 83 85 78 93 78
9 111 128 96 87 91 98 100 103
10 88 111 97 105 97 109 102 107
11 88 97 109 92 103 99 98
Waights Alpha Alpha
0.2 0.2 0.5
0.3
0.5

• Collect historical data
• Select a model
– Moving average methods
• Select n (number of periods)
• For weighted moving average: select weights
– Exponential smoothing
• Select a
• Selections should produce a good forecast
To Use a Forecasting Method
…but what is a good forecast?

A Good Forecast
Has a small error
 Error = Demand - Forecast

Measures of Forecast Error
b. MSE = Mean Squared Error  
n
F
-
A
=
MSE
n
1
=
t
2
t
t

MAD =
A - F
n
t t
t=1
n

et
 Ideal values =0 (i.e., no forecasting error)
MSE
=
RMSE
c. RMSE = Root Mean Squared Error
a. MAD = Mean Absolute Deviation

MAD Example
Month Sales Forecast
1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
What is the MAD value given the forecast
values in the table below?
MAD =
A - F
n
t t
t=1
n

5
5
20
10
|At – Ft|
Ft
At
= 40
= 40
4
=10

MSE/RMSE Example
Month Sales Forecast
1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
What is the MSE value?
5
5
20
10
|At – Ft|
Ft
At
= 550
4
=137.5
(At – Ft)2
25
25
400
100
= 550
 
n
F
-
A
=
MSE
n
1
=
t
2
t
t

RMSE = √137.5
=11.73

Measures of Error
t At Ft et |et| et
2
Jan 120 100 20 20 400
Feb 90 106 256
Mar 101 102
April 91 101
May 115 98
June 83 103
1. Mean Absolute Deviation
(MAD)
n
e
MAD
n
t

 1
2a. Mean Squared Error
(MSE)
 
MSE
e
n
t
n


2
1
2b. Root Mean Squared Error
(RMSE)
RMSE MSE

-16 16
-1 1
-10
17
-20
10
17
20
1
100
289
400
-10 84 1,446
84
6
= 14
1,446
6
= 241
= SQRT(241)
=15.52
An accurate forecasting system will have small MAD, MSE
and RMSE; ideally equal to zero. A large error may indicate
that either the forecasting method used or the parameters
such as α used in the method are wrong.
Note: In the above, n is the number of periods, which is 6 in

deviation
absolute
Mean
)
(
=
MAD
RSFE
=
TS
 
t
t
t forecast
actual
30
• How can we tell if a forecast has a positive or
negative bias?
• TS = Tracking Signal
– Good tracking signal has low values
Forecast Bias
MAD

Quantitative
Forecasting
Regression
Models
2. Moving
Average
1. Naive
Time Series
Models
3. Exponential
Smoothing
a) simple
b) weighted
a) level
b) trend
c) seasonality

Regression Analysis as a Method for
Forecasting
Regression analysis takes advantage of
the relationship between two
variables. Demand is then forecasted
based on the knowledge of this
relationship and for the given value of
the related variable.
Ex: Sale of Tires (Y), Sale of Autos (X) are
obviously related
If we analyze the past data of these
two variables and establish a
relationship between them, we may
use that relationship to forecast the
sales of tires given the sales of
automobiles.
The simplest form of the relationship
is, of course, linear, hence it is
referred to as a regression line. Sales of Autos (100,000)

Formulas
x
b
y
a 





 2
2
x
n
x
y
x
n
xy
b
x
y

x

y
y = a + b x
where,

Month Advertising Sales X 2
XY
January 3 1 9.00 3.00
February 4 2 16.00 8.00
March 2 1 4.00 2.00
April 5 3 25.00 15.00
May 4 2 16.00 8.00
June 2 1 4.00 2.00
July
TOTAL 20 10 74 38
Y = a + b X
Regression – Example




 2
2
x
n
x
y
x
n
xy
b x
b
y
a 


General Guiding Principles for Forecasting
1. Forecasts are more accurate for larger groups of items.
2. Forecasts are more accurate for shorter periods of time.
3. Every forecast should include an estimate of error.
4. Before applying any forecasting method, the total system should be
understood.
5. Before applying any forecasting method, the method should be tested
and evaluated.
6. Be aware of people; they can prove you wrong very easily in forecasting

Problems
• Quarterly consumption of sugar is given as below in the table.
Year Sugar Consumed
In (‘000) tonnes
Population
(millions)
1995 40 10
1996 50 12
1997 60 15
1998 70 20
1999 80 25
2000 90 30
2001 100 40
 Estimate the demand for sugar for year 2002 using regression
method.

108
Which Technique Is Best For Each
Product?
• 1. An industrial product with a
limited market
• 2 A consumer good which has
been on sales for many years
• 3A new product which has been
on sale for many years
• 4 A technically very complex
product, to be sold in a very wide
market
• A Time-series analysis
• B Expert opinion
• C Market testing
• D Survey of buyer’s intentions

109
Which Technique Is Best For Each
Product?
• 1. An industrial product with a
limited market
• 2 A consumer good which has
been on sales for many years
• 3A new product whose full scale
launch will be very expensive
• 4 A technically very complex
product, to be sold in a very wide
market
• A Time-series analysis
• B Expert opinion
• C Market testing
• D Survey of buyer’s intentions
• THIS IS JUST ONE
POSSIBLE ANSWER . YOU
MAY BE ABLE TO JUSTIFY
OTHERS

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