경영 세미나
Variety Seeking & Consumer Choice
문상길 교수
(노스캐롤라이나 주립대학, 마케팅)
일시 : 2007년 6월 18일 (월) 13:00~15:00
장소 : 경상대학 국제회의실
주관 : 경북대학교 BK21사업단
(지역혁신을 위한 창의형 디지털 경영인재 양성 사업단)
경북대학교 경제경영연구소
Modeling Level Changes in
Dynamic Variety Seeking:
Multinomial Probit Hidden
Markov Brand Choice Model
Sangkil Moon
North Carolina State University
Variety Seeking (VS):
Peanut Butter
Monday:
Peter Pan
Creamy
Tuesday:
Jif
Crunchy
Wednesday:
Skippy
Creamy
Variety Seeking (VS):
Movies
Weekend 2:
Weekend 1:
Casino Royale The 6th Sense
(Thriller)
(Action)
Weekend 3:
When Harry
Met Sally
(Comedy)
Presentation Roadmap
Research problem: tracking down
unobserved VS level changes
 Hidden Markov model (HMM) as a
solution model
 Model specification (hidden Markov
multinomial probit choice model) and
estimation (MCMC)
 Application
• Peanut butter data
 Anticipated contributions
 Project No.2: VS in hedonic goods

Variety Seeking (VS) and Choice
 VS has an impact on choice in a
seemingly random but inherently
systematic way.
 Since the pattern is not directly
observable, tracking down VS is
not straightforward.
Variety Seeking (VS) Literature
 Brand-based VS
• Changes in brand choices over
time (e.g., Peter Pan  Jif  Peter
Pan in peanut butter)
• Does not specify the source of VS
in terms of attributes (e.g., crunch
vs. creamy in peanut butter).
Variety Seeking (VS) Literature
 Attribute-based VS
• Focus on VS changes in attributes (e.g.,
crunchy vs. creamy in peanut butter,
“fruitness” in yogurt) (Trivedi, Bass &
Rao 1994 MS; Erdem 1996 MS;
Chintagunta 1999 Management Science)
• Consumers are more likely to switch
between sensory attributes (e.g., flavor)
than nonsensory attributes (e.g., brand)
(Inman 2001 JCR).
Research Problem:
Unobserved Variety Seeking Level Changes
 We can observe VS changes in
brands or attributes within the
same consumer given the
consumer’s purchase history in
the product category.
• John’s Peanut butter history: Peter
Pan (PP, favorite brand)  PP  Jif
 PP  Skippy  PP
Research Problem:
Unobserved Variety Seeking Level Changes
 We don’t observe VS level
changes that drive the
consumer’s wants and needs for
different brands or attributes.
Research Problem:
Unobserved Variety Seeking Level Changes
 Unobserved VS level changes
• In the high VS level, consumers tend to try
something different from their favorite
alternatives.
(e.g.) I usually go to a Chinese restaurant for
lunch. But, I want to eat something different
today like pizza.
• In the low VS level, consumers tend to stick
with their favorite alternatives.
(e.g.) I’ll eat Chinese food today as usual.
Research Problem:
Unobserved Variety Seeking Level Changes
 Research Question?: Consumers
are expected to react to price cuts
or promotion more strongly in the
high VS state (because they want
to try something different from
their favorites) than in the low VS
state (because they tend to stick
with their favorites).
Research Problem:
Unobserved Variety Seeking Level Changes
 Tracking down unobserved VS level
changes can reveal an unobserved and
inherent motivation for observed VS in
terms of brands or alternatives.
 No study has investigated the problem.
 The hidden Markov model (HMM) is an
effective tool to track down the
unobserved VS level changes over time
within the same consumer.
Why Hidden Markov Model (HMM)?
Markov Chain

Tomorrow Tomorrow
rain
no rain
The Markov chain
can detect transition
probabilities between
observed states.
Today .3
rain
(Table Example)
 States: rain or
Today .1
no rain
no rain
 States are
observable.
.7
.9
Why Hidden Markov Model (HMM)?
Markov Chain

The Markov
chain can detect
transition
probabilities
between
observed states.
(Table Example)
 States:
choice of
different foods
 States are
observable.
Tomorrow
Chinese
food
Tomorrow
other
foods
Today
.3
Chinese
food
.7
Today
other
foods
.2
.8
Why Hidden Markov Model (HMM)?
Hidden Markov Model
Tomorrow Tomorrow
high VS
low VS

What if states of
interest are not
directly observable?
(Table Example)
 States: internal
VS level (high vs.
low)
 States are
unobservable.
Today
high
VS
.2
.8
Today .6
low VS
.4
Why Hidden Markov Model (HMM)?
Hidden Markov Model
 Unlike the Markov chain that deals with
observed states, the HMM deals with a
series of unobserved states (e.g. VS level)
based on another series of relevant and
observed states (e.g., brand choice).
Time
1
2
3
4
Brand choice
(observed)
Jif
Jif
Skippy Jif
VS level
(unobserved)
Low
Low High
Low
--- T
--- Peter
Pan
--- High
Hidden Markov Model (HMM)
in Literature
 Traditional applications of HMM:
• Automatic speech recognition (engineering)
• Modeling incomplete DNA sequences
(genomics)
 Applications of HMM in marketing are
relatively new but has been intensive for the
past few years.
• Montgomery et al. (2004, MS) to identify
unobservable goals driving web browsing
behavior
• Du & Kamakura (2006, JMR) to define
household purchase lifecycles as latent
states
• Moon, Kamakura & Ledolter (2007, JMR) to
estimate unobserved competitor promotions
Proposed Model:
Hidden Markov Multinomial Probit Choice Model

My proposed model has three components
as follows:
1) Multinomial probit model component as a
choice model,
2) HMM to track down consumers’
unobserved internal level of VS over time,
and
3) Random coefficients model (hierarchical
Bayesian model) to account for customer
heterogeneity. In other words, VS level
changes are investigated for each
individual consumer.
Model Development:
Hidden Markov Multinomial Probit Choice Model
U hjt = hj + hVVhjt + hIIhjt + hP(s)ln(Phjt) + hF(s)Fhjt +hD(s)Dhjt + hjt,
 ht   h1t  hJt  ~ iid N(0, )
(s)
(s)
(s)
 ~ iid N(  , V) [regression parameters]
 h   hj ,  hV ,  hI ,  hP
,  hF
,  hD
U = Utility
h = household; j = alternative (brand); t = time
(purchase occasion)
P = price; F = feature (ad); D = display
Model Development:
Hidden Markov Multinomial Probit Choice Model
U hjt = hj + hVVhjt + hIIhjt + hP(s)ln(Phjt) + hF(s)Fhjt +hD(s)Dhjt + hjt,
 ht   h1t  hJt  ~ iid N(0, )
(s)
(s)
(s)
 ~ iid N(  , V) [regression parameters]
 h   hj ,  hV ,  hI ,  hP
,  hF
,  hD
V = Variety Seeking
Reference Brand = brand purchased on last purchase occasion
(time = t-1)
V(hjt) = 1 for reference brand j if Variety Seeking is in the High
state
= -1 for the case j is not reference brand if Variety Seeking
is in the High state, and
= 0 for all brands if Variety Seeking is in the Low state.
Model Development:
Hidden Markov Multinomial Probit Choice Model
U hjt = hj + hVVhjt + hIIhjt + hP(s)ln(Phjt) + hF(s)Fhjt +hD(s)Dhjt + hjt,
 ht   h1t  hJt  ~ iid N(0, )
(s)
(s)
(s)
 ~ iid N(  , V) [regression parameters]
 h   hj ,  hV ,  hI ,  hP
,  hF
,  hD
I = Inertia
Reference Brand = brand purchased on last purchase occasion
(time = t-1)
I(hjt) = 1 for reference brand j if Variety Seeking is in the Low state
= -1 for the case j is not reference brand if Variety Seeking is
in the Low state, and
= 0 for all brands if Variety Seeking is in the High state.
Hidden Markov Multinomial Probit Choice Model
(2-State Transition Probabilities)
q(VS )  L
H
L H
 q LL q LH 


q
q
HL
HH 

Q = transition probabilities between states
L = low VS state
H = high VS state
Model Estimation: MCMC
 The model will be estimated using
MCMC, which will have the following
three components:
1) Multinomial probit model
component,
2) Hidden Markov model (HMM)
(Kim and Nelson 1999), and
3) Hierarchical Bayesian structure.
Peanut Butter Data
 Grocery panel data (ERIM data)
from Professor Peter Rossi at
University of Chicago.
 Peanut butter has been used in
prior VS research (Kahn, Kalwani,
and Morrison 1986 JMR; Erdem
1996 MS)
Peanut Butter Data
 I selected households with 16+ purchases during
the data period (198505-198723, 2 years 19
weeks).
• I selected top 52% cases.
• 46,654 observations
• 1,755 households
 The whole period is divided into two periods:
• Estimation period (198505  198704, the
first 2 full years, 40,078 observations, 1,755
households)
• Validation period (198705  198723, the last
19 weeks, 6,576 observations, 1,523
households).
Peanut Butter Data
Brand Share
Average Price Average Feature Average Display
Brand
(%)
on Purchase (cents) on Purchase (%) on Purchase (%)
Peter Pan Creamy
13.5
178
6.8
4.3
Peter Pan Crunchy
7.9
178
6.7
4.2
Jif Creamy
16.6
183
2.0
1.0
Jif Crunchy
7.6
181
2.2
1.3
Skippy Creamy
15.1
181
3.3
1.8
Skippy Crunchy
11.1
181
3.3
1.6
Other Creamy
17.3
141
8.2
5.0
Other Crunchy
11.0
138
7.6
4.0
Anticipated Contributions
 Methodological contribution 1: first
application of HMM to VS. The proposed
model captures unobserved VS level
changes instead of the usual and observed
VS in choice.
 Methodological contribution 2: first model
that combines HMM and multinomial
probit model (+hierarchical Bayesian)
 Substantive contribution: price/promotion
sensitivity comparison between the high
and low VS states
Project No. 2:
Variety Seeking in Hedonic Goods

Idea: empirical VS study in hedonic
goods
 There are a lot of behavioral studies
but little empirical research on hedonic
VS using a choice model.
Project No. 2:
Variety Seeking in Hedonic Goods
 Definition: Hedonic goods offer the
experiences of fun, pleasure, or
excitement.
(e.g.) movies, music, designer clothers,
sports cars
 Data: Empirically, netflix.com movie
data are being used in the context of
movie category choice (e.g., action,
drama, comedy).
Project No. 2:
Variety Seeking in Hedonic Goods
 Research Question: Consumer
satisfaction (consumers’ own movie
ratings) and community opinions
(community’s overall ratings) are
important factors to influence choice
but we don’ know under what
conditions which factor has a bigger
influence than the other one.
Project No. 2:
Variety Seeking in Hedonic Goods

This project specifies the conditions in
association with the VS level.
• In the low VS state, consumer satisfaction
plays a bigger role because consumers rely
on their own expertise when selecting
movies from their favorite and familiar
categories.
• In the high VS state, consumers are likely
to explore less familiar categories, which
will make them more reliant on community
opinions.
NCSU 개요

설립: 1887년

위치: North Carolina 주, 랄리 (Raleigh)

학생수: 29,957명

교원수: 1,825명

총 연구지출: $2.9억

총 미국 연방정부 연구지원금: $1.4억

특허보유: 500건
NCSU 개요

단과대학 (10개):
1. 농업생명과학 대학
2. Design 대학
3. 사범 대학
4. 공대
5. 자연자원 대학
NCSU 개요

단과대학 (10개):
6. 인문사회 대학
7. 경영 대학
8. 물리수학 대학
9. 섬유대학
10. 수의대
NCSU Centennial Campus
대학, 산업, 정부 협동체
 연구개발과 교육 기능
 1,334 acre (540만 제곱미터)
 사무실, 실험실 임대
 현재 1,600명의 산업, 정부 직원과 1,350명의
대학 교직원 근무
 장래 12,500명의 산업, 정부 직원과
12,500명의 대학 교직원 근무지로 확대 계획

NCSU 경영대학

학위 과정
 회계학 석사
 경영학 석사 (MBA)
 경제학 석사, 박사
 농경제학 석사
 학사 – 회계학, 경영학, 경제학
NCSU 경영대학
 순위
MBA 51~75위권 (US News & World
Report)
 학부 73위 (US News & World
Report)
 학부 71위 (BusinessWeek)
 회계학 석사: 20위 (Public Account
Report)
 회계학 학부: 20위 (Public Account
Report)

NCSU 경영대학

역사
 1992년 단대 설립
 2002년 MBA 과정 신설

교원: 110명
지역 한인 사회

살기 좋은 생활 환경
 저렴한 생활 환경과 집값
 최상의 고등교육 환경 – 3개 주요 대학
(Duke, UNC, NCST)
 양질의 초중등 교육 환경
 낮은 범죄율
 중규모 도시로서 쾌적한 생활 환경
(교통 혼잡이 별로 없으면서 문화
혜택이 많음)
지역 한인 사회

성장하는 한인 사회
 다수의 한인 유학생과 기업 취업자
 최근 이주민이 빠르게 증가하고 있음
 다수의 한인 전문 식품점과 음식점
 다수의 한인 교회. 많은 한인교민들의
활동이 교회 중심으로 이뤄짐.
 테니스와 골프 동호회 활성화