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 Distributio n Model Masataka Yamada Kyoto Sangyo University [email protected] Ryuji Furukawa Evergreen Japan Corporation [email protected]. jp Hiroshi Kato Iihara Management Institute [email protected] Marketing Science Conference 2000, UCLA FR-A4, 9:00-10:30

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Page 1: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

[email protected]

Ryuji Furukawa

Evergreen Japan Corporation

[email protected]

Hiroshi Kato

Iihara Management Institute

[email protected]

Marketing Science Conference 2000, UCLA FR-A4, 9:00-10:30

Page 2: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 3: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 4: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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).

Page 5: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 6: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 7: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 8: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

June 23, 2000 (C) Masataka Yamada 8

Some Examples of Product Classifications

• Nondurable goods, Durable goods and Services based on their durability or tangibility.

Page 9: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 10: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 11: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 12: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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 .

Page 13: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 14: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 15: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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)

Page 16: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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)

Page 17: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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 )

Page 18: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 19: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 20: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 21: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 22: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 23: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 24: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 25: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

June 23, 2000 (C) Masataka Yamada 25

Information,Involvement

Word-of- mouthCommunication

Review, Publicity

Advertisement

Tie-up withmultiple media

Price decreasingSample offering

Back

Page 26: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

June 23, 2000 (C) Masataka Yamada 26

Value (Attractiveness) at the timeof its adoption decisionInitial Value ( Attractiveness) /Perceived Risk

Back

Page 27: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

June 23, 2000 (C) Masataka Yamada 27

Time to act from its adoption decision1 / Value (Attractiveness) at the time of

its adoption decision

Back

Page 28: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 29: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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).

Page 30: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 31: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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).

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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.

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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.

Page 34: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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/

Page 35: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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).

Page 36: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 37: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 38: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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).

Page 39: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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)

Page 40: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

Page 41: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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.

Page 42: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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:

Page 43: June 23, 2000(C) Masataka Yamada1 Anticipatory (Eagerly-awaited) Good/Service: Estimating Sales Patterns of Music CDs by Weibull Distribution Model Masataka

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

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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

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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)

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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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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

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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 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.

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• 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

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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:

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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

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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

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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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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

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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

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• 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.

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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.

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Future Research Directions

• Analyze albums further.

• Analyze singles.

• Models for sales forecasts.

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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.

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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 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.

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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.