june 23, 2000(c) masataka yamada1 anticipatory (eagerly-awaited) good/service: estimating sales...
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June 23, 2000 (C) Masataka Yamada 1
Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model
Masataka Yamada
Kyoto Sangyo University
Ryuji Furukawa
Evergreen Japan Corporation
Hiroshi Kato
Iihara Management Institute
Marketing Science Conference 2000, UCLA FR-A4, 9:00-10:30
June 23, 2000 (C) Masataka Yamada 2
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 3
1 Introduction (continued)• Second, we found that the diffusion patterns of anti
cipatory 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 4
1.1 Classification of Products in Marketing• Before we proceed to anticipatory good/se
rvice, 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 5
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 6
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 7
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 8
Some Examples of Product Classifications
• Nondurable goods, Durable goods and Services based on their durability or tangibility.
June 23, 2000 (C) Masataka Yamada 9
Consumer goods classification Consumer goods are classified on the basis of consumer shopping habits because they have implications for marketing strategy:
Conveniencegoods
Staple goods
Impulse goods
Emergency goods
Shoppinggoods
Specialtygoods
Unsoughtgoods
June 23, 2000 (C) Masataka Yamada 10
Industrial goods classification Industrial goods can be classified in terms of how they enter the production process and their relative costliness:
Materialsand Parts
Supplies and Services
Raw Materials
Manufacturedmaterials andparts
CapitalItems
Installations
Accessoryequipment
Supplies
Businessservices
June 23, 2000 (C) Masataka Yamada 11
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 12
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 .
June 23, 2000 (C) Masataka Yamada 13
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
Time
f (t )
0
p
p
0 Time
f (t )
Patterns can be regarded as being continuous from S-shaped ones to J-shaped ones.
June 23, 2000 (C) Masataka Yamada 14
Correspondence between Bayus' Segments and the Classes
( The original data are taken from Table 5 on p. 1329, Bayus 1993 and all in the US market )
(1) fast initial growth with sales peaking quickly (segment #1)(2) a long introduction growth period (segment #4)
* = Three Basic Patterns
(3) a moderate introduction and growth period, with differences primarily in the market potential size (segment #2, #3, and #5)
Basic
Pattern*
(1) III
2 (3) II
(3)
(2)
5 (3) II
Products
I
I
Class
Electric Toothbrush, FireExtinguisher, Hair Setter, SlowCooker, Styling Dryer, TrashCompactor, 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
Color TV, Refrigerator, VCR
Calculator, Digital Watch
#1 has a loweravarage pricethan #2Housewares
and SmallerAppliances
MajorAppliances
Products withLargeProductionEfficiency
#4 is starting outmuch higher pricepoint than #3
ComparativeDetails
Product GroupCharacteristics
4
3
1
Segment
large marketpotentials, andhigh learning andprice trendcoefficients
June 23, 2000 (C) Masataka Yamada 15
Name of Movie T j (Wks) p q m p /q Type of Pattern ClassTerminator 2 24 0.553 0 142.532 #DIV/0! Exponential VRobin Hood 20 0.319 0 141.780 #DIV/0! Exponential VThe Rocketeer 17 0.347 0.371 42.804 0.935 Gen. Gamma IIIDying Young 10 0.56 0 32.218 #DIV/0! Exponential VNaked Gun 2-1/2 19 0.557 0 73.703 #DIV/0! Exponential VThe Doctor 21 *** *** *** #VALUE! *** ***V.I. Warshowski 10 0.553 0.858 9.607 0.645 Erlang-2 IIIMobsters 10 0.651 0.161 17.801 4.043 Gen. Gamma VHot Shots! 16 0.279 0 73.562 #DIV/0! Exponential VDoc Hollywood 19 0.193 0 65.883 #DIV/0! Exponential VDie Hard 2 15 0.398 0.149 102.719 2.671 Gen. Gamma IVDays of Thunder 13 0.295 0.421 71.384 0.701 Gen. Gamma IIIBetsy's Wedding 10 0.199 0.724 18.949 0.275 Erlang-2 IIIExorcist III 6 0.288 1.353 22.062 0.213 Erlang-2 IIArachnophobia 10 0.181 0.876 42.911 0.207 Erlang-2 IIGhost 20 0.116 1.02 68.601 0.114 Erlang-2 IIBird on a Wire 19 *** *** *** #VALUE! *** ***Cadillac Man 12 *** *** *** #VALUE! *** ***Wild at Heart 11 0.174 1.346 10.498 0.129 Erlang-2 II
(made from Table 1 on p. 123, Sawhney and Eliashberg 1996)
June 23, 2000 (C) Masataka Yamada 16
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 17
p
0 T IN T 1 T * T 2
Figure 1. Class I Pattern: 0 < T INIn
nova
tors
Early
Ado
pter
s
Early
Maj
ority
Late
Maj
ority
Lagg
ards
Time
f (t )
June 23, 2000 (C) Masataka Yamada 18
Bass Continuous Time Domain Diffusion model
q
p
qpTTTTTTIN 347ln
1*2*2* 11
q
p
q
p
eqp
edttfTF
IN
IN
INT
Tqp
Tqp
IN
4
3
2
1
4
3
2
13471
348
1
1
1)(
0
q
p
qpT 32ln
11
q
p
qpT
32
1ln
12
33
321
1
q
p
TF
33
321
2
q
p
TF
q
p
qpT ln
1*
q
pTF 1
2
1*
tqpqp
tqp
e
etF
1
1
2
2
11
)(
tqp
qp
tqp
ep
eqptf
Noting that
we invented the following classification method and class map.
),()( qpfF
June 23, 2000 (C) Masataka Yamada 19
Time
f (t )
0
p
Figure 5. Class V Pattern: T 2 < 0
p
0 T 2 Time
Figure 4. Class IV Pattern: T * < 0 < T 2f (t )
Time
p
0 T 1 T * T 2
Figure2. Class II Pattern: T IN < 0 < T 1f (t )
p
0
Figure 3. Class III Pattern: T 1 < 0 < T *
T * T 2Time
f (t )
A Typical Pattern for the Respective Class
June 23, 2000 (C) Masataka Yamada 20
Class Map with Iso-Peak Time Curves
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0 0.5 1 1.5 2 2.5
Class I
Class II
Class III
Class IVClass V
q
p
T* =1
T*=2
T*=3
T *=4
T *=10T *=7
T *=6T *=5
0<T I N
0<T1
0<T*
0<T2
** is an orbit of the maximum p's for fixed T*'s.
qp 347
qp 32 qp 32 qp **28.0 qp
qp 28.0
June 23, 2000 (C) Masataka Yamada 21
Table 1 Classification Criteria for Diffusion Patterns
Class Timing Lower bound p/q
I 0 < p/q <
II < p/q <
III < p/q < 1.000
IV 1.000 < p/q <
V < p/q <
*1 0 TT
2* 0 TT
02 T
10 TTIN INT0
072.0347
268.032
732.332
732.332
268.032
072.0347
Upper bound
June 23, 2000 (C) Masataka Yamada 22
3 Adoption and Diffusion Process of New ProductAnnouncement Awareness
Introduction
Knowledge Attitude Decision(Intension)
Action (Adoption)
PerceivedRisk
Marketing Mix Setting Marketing Mix Adjustment
: 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 diffusionpatterns from S-shaped curve to J-curve exist.
Initial Value (Attractiveness) Information,Involvement
Value (Attractiveness) at the timeof its adoption decision∝ InitialValue ( Attractiveness) /Perceived Risk
Time to act from its adoption decision∝ 1 / Value (Attractiveness) at the time ofits adoption decision
Speed of SupplyResponse: Product,Manufacturing,Distribution,Cyberspace
Personality andAttributes: Fivecategories ofRogers, Lifestyle
Inventory of SimilarProducts, Existenceof competingproduct categories
Product Characteristics Market Characteristics
June 23, 2000 (C) Masataka Yamada 23
3 Adoption and Diffusion Process of New Product
Note that this conceptual model is made to answer the question why different diffusionpatterns from S-shaped curve to J-curve exist.
Announcement Awareness
Introduction
Knowledge Attitude Decision(Intention)
Action (Adoption)
Initial Value (Attractiveness)
Perceived Characteristics ofInnovativeness: (1) RelativeAdvantage, (2) Compatibility,(3) Complexity, (4) Trialability,(5) Observability.
Price
Excitement / Innovativeness
Country, Region, Organization,Firm Brand
Popularity: Director, Star, Producer,Songwriter, Composer, Artist
Series, Junior
Marketing Mix Setting
Information,Involvement
Word-of- mouthCommunications
Review, Publicity
Advertisement
Tie-up withmultiple media
Price decreasingSample offering
Marketing Mix Adjustment
PerceivedRisk
Value (Attractiveness) at the timeof its adoption decision∝ InitialValue ( Attractiveness) /Perceived Risk
Time to act from its adoption decision∝ 1 / Value (Attractiveness) at the time ofits adoption decision
Speed of SupplyResponse: Product,Manufacturing,Distribution,Cyberspace
Personality andAttributes: Fivecategories ofRogers, Lifestyle
Inventory of SimilarProducts, Existenceof competingproduct categories
Product Characteristics Market Characteristics
: things that influence individual person's adoption decision
: things that firms influence individual person's adoption decision or things that are givenSkip
June 23, 2000 (C) Masataka Yamada 24
Initial Value (Attractiveness)
Perceived Characteristics ofInnovativeness: (1) RelativeAdvantage, (2) Compatibility,(3) Complexity, (4) Trialability,(5) Observability.
Price
Excitement / Innovativeness
Country, Region, Organization,Firm Brand
Popularity: Director, Star, Producer,Songwriter, Composer, Artist
Series, JuniorBack
June 23, 2000 (C) Masataka Yamada 25
Information,Involvement
Word-of- mouthCommunication
Review, Publicity
Advertisement
Tie-up withmultiple media
Price decreasingSample offering
Back
June 23, 2000 (C) Masataka Yamada 26
Value (Attractiveness) at the timeof its adoption decisionInitial Value ( Attractiveness) /Perceived Risk
Back
June 23, 2000 (C) Masataka Yamada 27
Time to act from its adoption decision1 / Value (Attractiveness) at the time of
its adoption decision
Back
June 23, 2000 (C) Masataka Yamada 28
Speed of SupplyResponse: Product,Manufacturing,Distribution,Cyberspace
Personality andAttributes: Fivecategories ofRogers, Lifestyle
Inventory of SimilarProducts, Existenceof competingproduct categories
Product Characteristics Market Characteristics
Back
June 23, 2000 (C) Masataka Yamada 29
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 30
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.
June 23, 2000 (C) Masataka Yamada 31
Examples:
• Computer software (Windows95), TV Game software (Final Fantasy), Movies with Celebrated Stars/Director (Terminator 2), Music CDs with Famous Artist/Group (Hikaru Utada).
June 23, 2000 (C) Masataka Yamada 32
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 33
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.
June 23, 2000 (C) Masataka Yamada 34
My favorite artist
My favorite single in it
Reasons for Album CD Purchases
Impression through TV, Radio and Stores
From http://www.ongakudb.com/
June 23, 2000 (C) Masataka Yamada 35
4.2 Hypothesis
Time
f (t )
0
p
Figure 5. Class V Pattern: T 2 < 0
• Anticipatory good/service should take a rapid penetration diffusion pattern (Class V).
June 23, 2000 (C) Masataka Yamada 36
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 37
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 38
Data Used• Authorized dealers of manufacturers, wholesale
r-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 39
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)
June 23, 2000 (C) Masataka Yamada 40
4.3 Results for Hypotheses Testing• H1: The rate of CDs whose diffusion patterns are rapi
d 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 PRESS
ED
• The rate for single CDs: P2=135/153=0.882
5315.3
153
)882.01(882.0
121
)983.01(983.0
882.0983.0
)1()1(
2
22
1
11
21
npp
npppp
Z
H0 can be rejected at F(3.5315)=0.999793 001.0
,0: 210 PPH 0: 21 PPH A
June 23, 2000 (C) Masataka Yamada 41
0
20
40
60
80
100
120
0 20 40 60
(%) A001
020406080
100120
0 20 40 60
A002 1997/ 11/ 11hitomi deja- vu(%)
020406080
100120
0 20 40 60
(%) A009
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.
June 23, 2000 (C) Masataka Yamada 42
H2: Sales pattern of unknown singer’s debut single CD (unanticipatory good) does not take a rapid penetration diffusion pattern.
020406080
100
0 20 40 60
S057 1998/ 5/ 12 The Brilliant Green, THEREWILL BE LOVE THERE
(%)
05
10152025
0 20 40 60
S079 1998/ 7/ 7 CONVERTIBLE OH- DARLING(%)
We have only two unknown singers’ debut single CDs in our data. Their patterns are shown below:
June 23, 2000 (C) Masataka Yamada 43
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.
020406080
100120
0 20 40 60
S140 1999/ 4/ 20 “Sun and Cisco- moon,” Moon and Sun
June 23, 2000 (C) Masataka Yamada 44
Ami Suzuki(from ORICON data)
020,00040,00060,00080,000
100,000120,000140,000160,000
0 5 10 15
Debut Single 7/1/982nd Single 9/17/98
3rd Single 11/5/98
week
June 23, 2000 (C) Masataka Yamada 45
0
50,000
100,000
150,000
200,000
0 2 4 6 8week
Debut Single 1/28/98
2nd Single 5/27/98
3rd Single 9/9/98
“Morning Girls” (from ORICON data)
June 23, 2000 (C) Masataka Yamada 46
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.
June 23, 2000 (C) Masataka Yamada 47
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 cumul
ative 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
June 23, 2000 (C) Masataka Yamada 48
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 sum
med up accordingly and respectively.
• Let t be an error, then our model becomes as follow: Y
t=m Ft(t )+ t , where, t~N (0, 2) is assumed.
• PROC NLIN of SAS is used for the parameter estimation.
June 23, 2000 (C) Masataka Yamada 49
• Adjusted R2:
MST
MSE
n
SSTpn
SSE
Ra
1
1
12
pnSSEnAIC 2)ln(
We did not use these criteria. Because we found that the following graphs better demonstrate the respective model performance.
• AIC:
Model Selection Criteria
June 23, 2000 (C) Masataka Yamada 50
0.010.020.030.040.050.0
1 11 21 31 41 51
ALBUM: Average Absolute Percentage Errors, n=121
t=day
Average of et's
(%)
Bass
Weibull
et
=100*|Yt -y^
t |/YtY
t=Cumulative Sales at t
yt̂=fitted value for Yt
Absolute Percentage Error:
June 23, 2000 (C) Masataka Yamada 51
0.01.02.03.04.05.06.07.08.09.0
1 6 11 16 21 26 31 36 41 46 51 t=day
(%)ALBUM: Absolute Percentage Errors of Weibull Model, n=121
mean
median
mean>median
June 23, 2000 (C) Masataka Yamada 52
0.05.0
10.015.020.025.030.035.040.045.0
0 10 20 30 40 50
mean medi a n
ALBUM: Absolute Percentage Errors of Bass Model, n=121
(%) median
June 23, 2000 (C) Masataka Yamada 53
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)).
June 23, 2000 (C) Masataka Yamada 54
Distribution of c (shape parameter)
Stem Leaf # Boxplot 9 77 2 | 9 02233 5 | 8 559 3 | 8 000111222333344 15 | 7 55666666889999 14 +-----+ 7 000001112222334 15 | + | 6 556666677777777888889999 24 *-----* 6 00111112222334444 17 +-----+ 5 6666777789999 13 | 5 000113 6 | 4 589 3 | 4 ----+----+----+----+---- Multiply Stem.Leaf by 10**-1
mean=0.697066, median=0.684119, N=117
June 23, 2000 (C) Masataka Yamada 55
Stem Leaf # Boxplot 9 0 1 * 8 8 2 1 * 7 7 1 1 * 6 5 1 * 6 5 5 3 1 * 4 5 1 0 4 4 1 0 3 9 1 0 3 0044 4 0 2 55579 5 | 2 00112222334 11 | 1 5555556667777888888899999 25 +--+--+ 1 0000011111222222233333444444444444 34 *-----* 0 556666777778888888999999999 27 +-----+ 0 244 3 | ----+----+----+----+----+----+---- Multiply Stem.Leaf by 10**+1
Distribution of alpha (scale parameter) mean=17.5, median=14.1, N=117
June 23, 2000 (C) Masataka Yamada 56
• 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.
Conclusions
• Therefore, diffusion strategy of new products for anticipatory good/service must be different from unaticipatory good/service.
June 23, 2000 (C) Masataka Yamada 57
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 (C) Masataka Yamada 58
Future Research Directions
• Analyze albums further.
• Analyze singles.
• Models for sales forecasts.
June 23, 2000 (C) Masataka Yamada 59
References• Bass, Frank M. (1969), “A New Product Growth Model for C
onsumer 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 Scien
ce, Vol. 8, No. 3 (Summer), 231-247.
June 23, 2000 (C) Masataka Yamada 60
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 Innovati
on Diffusion Models,” Journal of Marketing Research, Vol. XXVII (February), 37-50.
• Mansfield, Edwin (1961), “Technical Change and the Rate of Innovation,” Econometrica, 29, October, 741-76
6.
• 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.
June 23, 2000 (C) Masataka Yamada 61
References(continued)• Yamada, Masataka, Ruji Furukawa and Mamoru Ishihara (1
997) “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.