Marketing Science Conference 2000, UCLA FR-A4, 9:00-10:30
Anticipatory (Eagerly-awaited) Good/Service:
Estimating Sales Patterns of Music CDs by
Weibull Distribution Model
Masataka Yamada
Kyoto Sangyo University
myamada@cc.kyoto-su.ac.jp
Ryuji Furukawa
Evergreen Japan Corporation
r.furukawa@evergreen-japan.co.jp
Hiroshi Kato
Iihara Management Institute
JDX01156@nifty.ne.jp
June 23, 2000
(C) Masataka Yamada
1
1 Introduction
• From diffusion theory point of view, we
define anticipatory (eagerly-awaited)
good/service for one of products that
indicate rapidly penetrating sales curves to
give marketers new strategic implications.
• We pick up CD album as one of the
anticipatory goods. Then, we test the
hypothesis that the diffusion pattern of an
anticipatory good/service is a rapidly
penetrating one.
June 23, 2000
(C) Masataka Yamada
2
1 Introduction (continued)
• Second, we found that the diffusion patterns of
anticipatory goods are much sharper than those of
first purchases of groceries comparing the
goodness of fit between Bass diffusion model and
Weibull distribution model on the sales data of
music CDs. Hence, those goods indicating sharper
diffusion curves can be identified as anticipatory
goods.
• Finally, we consider marketing strategy of new
product introductions for anticipatory goods.
June 23, 2000
(C) Masataka Yamada
3
1.1 Classification of Products in Marketing
• Before we proceed to anticipatory
good/service, we would like to review
conventional product classifications.
• What is a product?
A product is anything that can be offered
to a market for attention, acquisition, use,
or consumption that might satisfy a want
or need.
• It includes physical objects, services,
persons, places, organizations, and ideas
(P. Kotler, 1988).
June 23, 2000
(C) Masataka Yamada
4
Physical products:
automobiles, toasters, shoes, eggs and books
Services (Service Products):
haircuts, concerts, and vacations
Persons:
Barbra Streisand, we give her attention, buy her
records, and attend her concerts
Places:
Hawaii can be marketed, in the sense of either
buying some land in Hawaii or taking a vacation
there.
June 23, 2000
(C) Masataka Yamada
5
Organizations:
The American Red Cross can be marketed, in the
sense that we feel positive toward it and will
support it.
Ideas:
family planning, safe driving
June 23, 2000
(C) Masataka Yamada
6
Three Levels of Product
• Core Product: what is the buyer really
buying? Core benefit or service
• Tangible Product: a quality level,
features, styling, a brand name, and
packaging.
• Augmented Product:delivery and
credit, installation, after sale service,
and warranty.
June 23, 2000
(C) Masataka Yamada
7
Some Examples of Product Classifications
• Nondurable goods, Durable goods and
Services based on their durability or
tangibility.
June 23, 2000
(C) Masataka Yamada
8
Consumer goods classification
Consumer goods are classified on the basis
of consumer shopping habits because they
have implications for marketing strategy:
Convenience
goods
Shopping Specialty
goods
goods
Unsought
goods
S taple goods
Im pulse goods
Em ergency goods
June 23, 2000
(C) Masataka Yamada
9
Industrial goods classification
Industrial goods can be classified in terms
of how they enter the production process
and their relative costliness:
Materials
and Parts
Capital
Items
Supplies and
Services
Raw Materials
Installations
Supplies
Manufactured
materials and
parts
Accessory
equipment
Business
services
June 23, 2000
(C) Masataka Yamada
10
What is the purpose of product
classifications?
• Marketers believe that each product
type has an appropriate marketing-mix
strategy. Or it gives marketers
implications for marketing strategy.
June 23, 2000
(C) Masataka Yamada
11
An approach to Product Classification
from Diffusion Theory of New Product
• We would like to add another approach
to classify product for the decision
making of marketing strategy from
diffusion theory of new products .
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(C) Masataka Yamada
12
2 Past Researches of Diffusion Patterns of New
Products
• Fourt and Woodlock (1960), q=0, Exponential Curve, Grocery
Products
• Mansfield (1961), p=0, Logistic Curve, Industrial Products
• Bass(1969), combined the above two
• Lekvall and Wahlbin (1973)
• Gatignon and Robertson (1985), 29 propositions
• Bayus(1993), Consumer Electronics and Electric Appliances
• Sawhney And Eliashberg (1996), Movies
f (t )
f (t )
p
p
0
June 23, 2000
Patterns can be regarded as
being continuous from Sshaped ones to J-shaped
ones.
(C) Masataka Yamada
Time
0
Time
13
Correspondence between Bayus' Segments and the Classes
Product Group
Characteristics
Segment
1
Housewares
and Smaller
Appliances
2
Major
Appliances
3
4
Products with
Large
Production
Efficiency
5
Comparative
Details
#1 has a lower
avarage price
than #2
Products
Electric Toothbrush, Fire
Extinguisher, Hair Setter, Slow
Cooker, Styling Dryer, Trash
Compactor, Turntable
Can Opener, CassetteTape Deck,
Curling Iron, Electric blancket,
Heating Pad, Knife Sharpner,
Lawn Mower, Waffle Iron
B&W TV, Blender, Deep Fryer,
Electric Dryer, Food Processor,
Microwave Oven, Room A/C
#4 is starting out Color TV, Refrigerator, VCR
much higher price
point than #3
Calculator, Digital Watch
large market
potentials, and
high learning and
price trend
coefficients
Basic
Pattern*
Class
(1)
III
(3)
II
(3)
I
(2)
I
(3)
II
* = Three Basic Patterns
(1) fast initial growth with sales peaking quickly (segment #1)
(2) a long introduction growth period (segment #4)
(3) a moderate introduction and growth period, with differences primarily in the market potential size
(segment #2, #3, and #5)
( The original data are taken from Table 5 on p. 1329, Bayus 1993 and all in the US market )
June 23, 2000
(C) Masataka Yamada
14
Name of Movie T j (Wks)
Terminator 2
24
Robin Hood
20
The Rocketeer
17
Dying Young
10
Naked Gun 2-1/2
19
The Doctor
21
V.I. Warshowski
10
Mobsters
10
Hot Shots!
16
Doc Hollywood
19
Die Hard 2
15
Days of Thunder
13
Betsy's Wedding
10
Exorcist III
6
Arachnophobia
10
Ghost
20
Bird on a Wire
19
Cadillac Man
12
Wild at Heart
11
p
q
m
p /q
0.553
0 142.532
#DIV/0!
0.319
0 141.780
#DIV/0!
0.347 0.371
42.804
0.935
0.56
0
32.218
#DIV/0!
0.557
0
73.703
#DIV/0!
***
***
*** #VALUE!
0.553 0.858
9.607
0.645
0.651 0.161
17.801
4.043
0.279
0
73.562
#DIV/0!
0.193
0
65.883
#DIV/0!
0.398 0.149 102.719
2.671
0.295 0.421
71.384
0.701
0.199 0.724
18.949
0.275
0.288 1.353
22.062
0.213
0.181 0.876
42.911
0.207
0.116
1.02
68.601
0.114
***
***
*** #VALUE!
***
***
*** #VALUE!
0.174 1.346
10.498
0.129
Class
Exponential
V
Exponential
V
Gen. Gamma
III
Exponential
V
Exponential
V
***
***
Erlang-2
III
Gen. Gamma
V
Exponential
V
Exponential
V
Gen. Gamma
IV
Gen. Gamma
III
Erlang-2
III
Erlang-2
II
Erlang-2
II
Erlang-2
II
***
***
***
***
Erlang-2
II
Type of Pattern
(made from Table 1 on p. 123, Sawhney and Eliashberg 1996)
June 23, 2000
(C) Masataka Yamada
15
Our Classification Method of Diffusion
Patterns
• Yamada, Masataka, Ruji Furukawa and Mamoru
Ishihara (1997)
Mahajan, Vijay, Eitan Muller and Rajendra K. Srivastava
(1990)
June 23, 2000
(C) Masataka Yamada
16
Figure 1. Class I Pattern: 0 < T IN
0
T
June 23, 2000
T
T *
(C) Masataka Yamada
Laggards
Late Majority
Early Adopters
Innovators
p
Early Majority
f (t )
T
Time
17
Bass Continuous Time Domain Diffusion model
2   p  q t
  p  q t


p

q
e
1 e
F t  
1
 e
p
q
f (t ) 
 p  q t
F TIN   
0
1  e  p  q TIN
1 
p 1
3 1
3 p
f (t )dt 

1

7

4
3








q  2 4 2 4  q
 p   p  q TIN 8  4 3 
1   e
q
p





1 2  3
q

1
p
T1  
ln  2  3 
pq 
q
F T1  
 p
1
T*  
ln  
pq  q 
1
p
F T *  1  
2 q 
T2  
 1  p 
1
 
ln 
p  q  2  3  q 
June 23, 2000
2


1
p
TIN  T * 2T * T1   2T1  T *  
ln  7  4 3 
pq 
q
TIN

1  p  q t 
p 1  p e



q
F T2  
3 3

1 2  3
(C) Masataka Yamada
3 3
 qp
Noting that
F ()  f ( qp ),
we invented
the following
classification
method and
class map.
18
A Typical Pattern for the Respective Class
f (t )
Figure2. Class II Pattern: T IN < 0 < T 1
Figure 3. Class III Pattern: T 1 < 0 < T *
f (t )
p
p
0
f (t )
T1
T*
Time
T2
T*
0
Figure 4. Class IV Pattern: T * < 0 < T 2
f (t )
p
Time
T2
Figure 5. Class V Pattern: T 2 < 0
p
0
T2
June 23, 2000
Time
Time
0
(C) Masataka Yamada
19
Class Map with Iso-Peak Time Curves


Class IV p  q

p  2 3 q
0.3
Class V
0.28
0< T
0.26
0.24
0< T 2
p
*
T * =1
0< T 1
Class III
0.22

p  0.28q** p  2  3 q
0.2

0.18
0.14

p  74 3 q
Class II
0.16
T * =2
0.12
0.1
0< T IN
T * =3
0.08
Class I
T * =4
0.06
0.04
*
T * =5
T =6
*
T
=7
T * =10
0.02
0
0
** p  0.28q
0.5
1
1.5
*
is an orbit of the maximum p's forT fixed
's.
June 23, 2000
(C) Masataka Yamada
q
2
2.5
20
Table 1 Classification Criteria for Diffusion Patterns
Class
I
II
III
IV
V
June 23, 2000
Timing
0  TIN
Lower bound
p/q
Upper bound
0
< p/q <
7  4 3  0.072
< p/q <
2  3  0.268
2  3  0.268
< p/q <
1.000
1.000
< p/q <
2  3  3.732
2  3  3.732
< p/q <
TIN  0  T1 7  4 3  0.072
T1  0  T *
T *  0  T2
T2  0
(C) Masataka Yamada

21
3 Adoption and Diffusion Process of New Product
Announcement
Awareness
Knowledge
Decision
(Intension)
Attitude
Introduction
Initial Value (Attractiveness)
Information,
Involvement
Perceived
Risk
Value (Attractiveness) at the time
of its adoption decision∝ Initial
Value ( Attractiveness) /
Perceived Risk
Action (Adoption)
Time to act from its adoption decision
∝ 1 / Value (Attractiveness) at the time of
its adoption decision
Speed of Supply
Response: Product,
Manufacturing,
Distribution,
Cyberspace
Marketing Mix Setting
Marketing Mix Adjustment
Personality and
Attributes: Five
categories of
Rogers, Lifestyle
Product Characteristics
Inventory of Similar
Products, Existence
of competing
product categories
Market Characteristics
: things that influence indivisual person's adoption decision
: things that firms influence indivisual person's adoption decision or things that are given
Note that this conceptual model is made to answer the question why different diffusion
patterns from S-shaped curve to J-curve exist.
June 23, 2000
(C) Masataka Yamada
22
3 Adoption and Diffusion Process of New Product
Announcement
Awareness
Knowledge
Decision
(Intention)
Attitude
Introduction
Initial Value (Attractiveness)
Excitement / Innovativeness
Information,
Involvement
Perceived Characteristics of
Innovativeness: (1) Relative
Advantage, (2) Compatibility,
(3) Complexity, (4) Trialability,
(5) Observability.
Word-of- mouth
Communications
Price
Tie-up with
multiplemedia
Price decreasing
Sample offering
Country, Region, Organization,
Firm Brand
Popularity: Director, Star, Producer,
Songwriter, Composer, Artist
Marketing Mix Setting
Action (Adoption)
Review, Publicity
Time to act from its adoption decision
∝ 1 / Value (Attractiveness) at the time of
its adoption decision
Advertisement
Series, Junior
Marketing Mix Adjustment
Value (Attractiveness) at the time
of its adoption decision∝ Initial
Value ( Attractiveness) /
Perceived Risk
Perceived
Risk
Speed of Supply
Response: Product,
Manufacturing,
Distribution,
Cyberspace
Personality and
Attributes: Five
categories of
Rogers, Lifestyle
Product Characteristics
Inventory of Similar
Products, Existence
of competing
product categories
Market Characteristics
: things that influence individual person's adoption decision
: things that firms influence individual person's adoption decision or things that are given
Skip
Note that this conceptual model is made to answer the question why different diffusion
patterns from S-shaped curve to J-curve exist.
June 23, 2000
(C) Masataka Yamada
23
Initial Value (Attractiveness)
Excitement / Innovativeness
Perceived Characteristics of
Innovativeness: (1) Relative
Advantage, (2) Compatibility,
(3) Complexity, (4) Trialability,
(5) Observability.
Price
Country, Region, Organization,
Firm Brand
Popularity: Director, Star, Producer,
Songwriter, Composer, Artist
Series, Junior
June 23, 2000
(C) Masataka Yamada
Back
24
Information,
Involvement
Word-of- mouth
Communication
Review, Publicity
Advertisement
Tie-up with
multiple media
Price decreasing
Sample offering
June 23, 2000
(C) Masataka Yamada
Back
25
Value (Attractiveness) at the time
of its adoption decision 
Initial Value ( Attractiveness) /
Perceived Risk
Back
June 23, 2000
(C) Masataka Yamada
26
Time to act from its adoption decision
 1 / Value (Attractiveness) at the time of
its adoption decision
Back
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(C) Masataka Yamada
27
Speed of Supply
Response: Product,
Manufacturing,
Distribution,
Cyberspace
Personality and
Attributes: Five
categories of
Rogers, Lifestyle
Product Characteristics
Inventory of Similar
Products, Existence
of competing
product categories
Market Characteristics
Back
June 23, 2000
(C) Masataka Yamada
28
4. Anticipatory (Eagerly-awaited) Good/Service
• Episode: Tickets for the national singer,
Hikaru Utada’s first whole country concert
tour are put on sale on April 22, 2000 and all
of 70,000 seats are sold out within 90minutes.
Also the sales of her new single “Wait and
See~Risk~” have already exceeded 1.3
million CDs within first three days after its
introduction. Her popularity seems to stop
nowhere. (ZAX 4/23/00).
June 23, 2000
(C) Masataka Yamada
29
4.1 Definition：
• An anticipatory (Eagerly-awaited)
good/service is anything that can be
offered to a market for attention,
acquisition, use, or consumption that
might satisfy an anticipatory want or
need.
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30
Examples:
• Computer software (Windows95), TV
Game software (Final Fantasy), Movies
with Celebrated Stars/Director
(Terminator 2), Music CDs with Famous
Artist/Group (Hikaru Utada).
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(C) Masataka Yamada
31
Properties：
• 1. High Value: Consumers want it eagerly and
obtain it anyway when it becomes available
because they like it. They may be fans,
admirers, and the like.
• 2. Intensive Information Search: Consumers
are willing to make great efforts to search for
information about its content, available time
and date, etc., to travel for obtaining it and so
on. Often times, there are abundant supply of
its information through firms’ marketing efforts.
June 23, 2000
(C) Masataka Yamada
32
Properties (continued)：
• 3. Low Risk: Consumers basically like it because of
their satisfaction with its previous version. Therefore,
they have very little perceived risk on it. They anticipate
the same or more level of satisfaction than before.
• 4. Low Risk: It should be reasonably priced so that
consumers can tolerate its unsatisfactory performance
even if it happens to be the case.
• 5. It may have “out of stock” or “sold out” risk but for
certain products such as music by internet may not
have this risk at all and at the same time it offers
instantaneous supply responses for consumers.
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(C) Masataka Yamada
33
Reasons for Album CD Purchases
My favorite artist
My favorite single in it
Impression through TV,
Radio and Stores
From http://www.ongakudb.com/
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(C) Masataka Yamada
34
4.2 Hypothesis
• Anticipatory good/service should take a
rapid penetration diffusion pattern (Class V).
f (t )
Figure 5. Class V Pattern: T 2 < 0
p
Time
0
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(C) Masataka Yamada
35
Operational Hypotheses
• H1: The rate of CDs whose diffusion
patterns are rapid penetration diffusion
patterns within the album CDs is greater
than that of the single CDs.
• H2: Sales pattern of unknown singer’s
debut single CD (unanticipatory good)
does not take a rapid penetration
diffusion pattern.
June 23, 2000
(C) Masataka Yamada
36
Operational Hypotheses(continued)
• H3: The sales patterns of the debut singles
of new groups and singers who are
produced through a well designed process
such as “ASAYAN” contest program of TV
Tokyo are rapidly penetrating ones.
• The cases of the debut singles of “Sun and
Cisco-moon,” Ami Suzuki and “Morning
Girls” are analyzed.
June 23, 2000
(C) Masataka Yamada
37
Data Used
• Authorized dealers of manufacturers,
wholesaler-related stores, and mail order
companies and companies for business uses
are sharing the distribution channels of music
CDs and records by 45%, 50%, and 5%
respectively(Recording Industry in Japan 1999,
Recording Industry Association of Japan 1999).
• Our data are the sales data of music CDs sold
at one of national chains of convenience stores
obtained through Iihara Management Institute,
related to one of the major wholesalers,
Seikodo(http://www.seikodo.co.jp/index.html).
June 23, 2000
(C) Masataka Yamada
38
Some Details of Convenience Stores
• Usually convenience stores start to sell new
CDs from 3pm on the day before the officially
announced sales date by manufacturers. They
generally open stores for 24 hours.
• The original data are disguised for proprietary
reasons and the day before the announced
sales date is treated as a one half day duration
for our computation.
• Period for data collection:10/14/97-7/09/99
• Number of CDs: 256
• Number of data points: 56 days (eight weeks)
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(C) Masataka Yamada
39
4.3 Results for Hypotheses Testing
• H1: The rate of CDs whose diffusion patterns are
rapid penetration diffusion patterns within the album
CDs is greater than that of the single CDs.
• The rate for album CDs: P1=119/121=0.983
• A001 97/11/11 MAX4 Omnibus Western Music
• A009 97/12/11 Nobuteru Maeda HARD PRESSED
• The rate for single CDs: P2=135/153=0.882
H A : P1  P2  0
H 0 : P1  P2  0,
Z
pp
p (1  p )  p (1  p )
n
n
1
1
2
1
1
F(3.5315)=0.999793
June 23, 2000
2
2

0.983  0.882
 3.5315
0.983(1  0.983) 0.882(1  0.882)

121
153
2
H0 can be rejected at
(C) Masataka Yamada
  0.001
40
A Typical Rapid Penetration Curve
We learned that albums can be
regarded as anticipatory goods
by almost 100%. Because A001
is an omnibus CD which does
not have any particular artist,
and A009 seems to demonstrate
basically a rapid penetration
pattern.
80
60
40
20
0
June 23, 2000
20
0
40
60
(C) Masataka Yamada
20
40
60
40
60
A009
120
100
80
60
40
20
0
100
0
A002 1997/11/11hitomi deja-vu
(%)
A001
(%)
120
(%)
120
100
80
60
40
20
0
0
20
41
H2: Sales pattern of unknown singer’s debut
single CD (unanticipatory good) does not take a
rapid penetration diffusion pattern.
We have only two unknown singers’ debut single CDs in
our data. Their patterns are shown below:
(%) S057 1998/5/12 The Brilliant Green, THERE
100 WILL BE LOVE THERE
80
60
40
20
0
0
20
June 23, 2000
40
60
(%) S079 1998/7/7 CONVERTIBLE OH-DARLING
25
20
15
10
5
0
0
20
40
60
(C) Masataka Yamada
42
H3: The sales patterns of the debut singles of new groups and
singers who are produced through a well designed process
such as “ASAYAN” contest program of TV Tokyo are rapid
penetration ones.
The cases of the debut singles of “Sun and Cisco-moon,” Ami
Suzuki and “Morning Girls” are tested.
S140 1999/4/20 “Sun and Cisco-moon,” Moon and Sun
120
100
80
60
40
20
0
0
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20
(C) Masataka Yamada
40
60
43
Ami Suzuki(from ORICON data)
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
3rd Single 11/5/98
2nd Single 9/17/98
Debut Single 7/1/98
0
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5
(C) Masataka Yamada
10
week
15
44
“Morning Girls” (from ORICON data)
200,000
3rd Single 9/9/98
150,000
Debut Single 1/28/98
100,000
2nd Single 5/27/98
50,000
0
0
June 23, 2000
2
4
(C) Masataka Yamada
6 week 8
45
5. Model Fitting on CD Sales Data for
Further Investigations and Model Finding
for Better Forcasting
• Almost all the sales patterns seem to be taking
rapid penetration curves by eye-ball inspection.
• Usually exponential model is fitted on this type
of data. Note that exponential model is a
special case of Bass diffusion model when the
internal influence parameter, q, is zero.
• Also Weibull distribution model is fitted
because of its better performance for the first
several data points.
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46
Weibull Distribution
• Weibull two parameter probability distribution
function of adoption time (t) is given as follows:
• Ft(t )=1-EXP (-(t/ )c), t >0
• c: shape parameter, : scale parameter
• Let the potential market size be m, then the
cumulative number of adoptions at the end of
time t, Yt, can be given as below:
• Yt=m Ft(t)
• Note for managerial convenience that when t=  ,
regardless of the value of c,
Ft(t= )=1-EXP(-1)=0.63
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Weibull Distribution(continued)
• In order to compute cumulative unit sales:Y1, Y2,
Y3, , , unit sales from t=0 to t=0.5, S1, unit sales from
t=0.5 to t=1.5, S2, unit sales from t=1.5 to t=2.5, S3, , ,
are summed up accordingly and respectively.
• Let  t be an error, then our model becomes as follow:
Yt=m Ft(t )+ t , where, t~N (0,  2) is assumed.
• PROC NLIN of SAS is used for the parameter
estimation.
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48
Model Selection Criteria
• Adjusted R2:
• AIC:
SSE
MSE
n p
2
Ra  1 
 1
SST
MST
n 1
AIC  n ln( SSE n)  2 p
We did not use these criteria. Because we found
that the following graphs better demonstrate the
respective model performance.
June 23, 2000
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49
ALBUM: Average Absolute Percentage Errors, n=121
Average of e t 's
(%)
50.0
40.0
30.0
20.0
10.0
0.0
Absolute Percentage Error:
et =100*|Yt -y^t |/Y t
Y t =Cumulative Sales at t
Bass
y^t =fitted value for Y
Weibull
1
June 23, 2000
11
21 31
41
t
51 t=day
(C) Masataka Yamada
50
ALBUM: Absolute Percentage Errors ofWeibull Model, n=121
(%)
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
mean >median
mean
median
1
6
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11
16
21
26
31
36
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41
46
51
t=day
51
ALBUM: Absolute Percentage Errors ofBass Model, n=121
(%)
mean
median
median
45.0
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
0
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10
20
30
40
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50
52
Weibull Model fits better than Bass
Model on the Music CD Sales Data
• This implies that diffusion patterns of anticipatory
goods take much sharper pattern, especially
during first few periods, than grocery goods
whose first purchase sales patterns are generally
believed to be exponential curves (Fourt and
Woodlock (1960)).
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53
Distribution of c (shape parameter)
mean=0.697066, median=0.684119, N=117
Stem
9
9
8
8
7
7
6
6
5
5
4
4
Leaf
77
02233
559
000111222333344
55666666889999
000001112222334
556666677777777888889999
00111112222334444
6666777789999
000113
589
#
2
5
3
15
14
15
24
17
13
6
3
Boxplot
|
|
|
|
+-----+
| + |
*-----*
+-----+
|
|
|
----+----+----+----+---Multiply Stem.Leaf by 10**-1
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Distribution of alpha (scale parameter)
mean=17.5, median=14.1, N=117
Stem
9
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0
0
Leaf
0
#
1
Boxplot
*
2
1
*
1
5
1
1
*
*
1
1
1
1
4
5
11
25
34
27
3
*
0
0
0
0
|
|
+--+--+
*-----*
+-----+
|
3
5
4
9
0044
55579
00112222334
5555556667777888888899999
0000011111222222233333444444444444
556666777778888888999999999
244
----+----+----+----+----+----+---Multiply Stem.Leaf by 10**+1
June 23, 2000
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Conclusions
• We proposed a new classification for product/service,
namely, anticipatory good/service vs unaticipatory
good/service from new product diffusion pattern perspective.
• We found that the diffusion pattern of anticipatory
good/service takes the rapidly penetrating (J-shaped)
pattern.
• We found that it can not be captured well by Bass diffusion
(=exponential ) curve (ex. first purchase sales patterns of
grocery goods) . They are generally much sharper than
those captured by Bass model. Hence, those goods indicating
sharper rapid penetrating diffusion curves can be identified as
anticipatory goods.
• Therefore, diffusion strategy of new products for anticipatory
good/service must be different from unaticipatory good/service.
June 23, 2000
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56
Conclusions (continued)
• Marketing strategy for a new anticipatory good/service:
(1) One should let consumers be involved from its
development stage.
Ex. (a) ASAYAN project of TV Tokyo; (b) use famous
artists, movie stars, directors; (c) make it series etc.
(2) Before the introduction of a new product, its
promotion and publicity should be done as
intensively and widely as possible into the target
market.
(3) The initial price should be set at the most
reasonable level possible or free if possible.
June 23, 2000
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57
Future Research Directions
• Analyze albums further.
• Analyze singles.
• Models for sales forecasts.
June 23, 2000
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58
References
• Bass, Frank M. (1969), “A New Product Growth Model for
Consumer Durables,” Management Science, Vol. 15
(January), 215-227.
• Bayus, Barry L. (1993), “High-Definition Television:
Assessing Demand Forecasts for a Next Generation
Consumer Durable,” Management Science, Vol. 39
(November), 1319-1333.
• Fourt, L. A. And Woodlock, J. W. (1960), "Early Prediction of
Market Success for New Grocery Products," Journal of
Marketing, Vol. 25 (October), 31-38.
• Gatignon, Hubert, Jehoshua Eliashberg and Thomas S.
Robertson (1989), “Modeling Multinational Diffusion
Patterns: An Efficient Methodology,” Marketing Science,
Vol. 8, No. 3 (Summer), 231-247.
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References(continued)
• Lekvall, Per and Clas Wahlbin (1973), “A Study of Some
Assumptions Underlying Innovation Diffusion Functions,”
Swedish Journal of Economics, 75,362-377.
• Mahajan, Vijay, Eitan Muller and Rajendra K. Srivastava (1990),
“Determination of Adopter Categories by Using Innovation
Diffusion Models,” Journal of Marketing Research, Vol.
XXVII (February), 37-50.
• Mansfield, Edwin (1961), “Technical Change and the Rate of
Innovation,” Econometrica, 29, October, 741-766.
• Sawhney, Mohanbir S. And Jehoshua Eliashberg (1996), “A
Parsimonious Model for Forecasting Gross Box-Office
Revenues of Motion Pictures,” Marketing Science, Vol.15,
No. 2, 113-131.
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References(continued)
• Yamada, Masataka, Ruji Furukawa and Mamoru Ishihara
(1997) “A Classification Method of Diffusion Patterns with
a Class Map,” ACTA HUMANISTICA ET SCIENTIFICA,
UNIVERSITATIS SANGIO KYOTIENSIS, Vol. 28, No. 2,
Social Science Series No. 14 (March), Kyoto Sangyo
University, 59-82.
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