문상길 교수 ( 노스캐롤라이나 주립대학 , 마케팅 ) 일시 : 2007 년 6 월 18 일 ( 월 ) 13:00~15:00 장소 : 경상대학 국제회의실

1. 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

2. Modeling Level Changes in Dynamic Variety Seeking: Multinomial Probit Hidden Markov Brand Choice Model Sangkil Moon North Carolina State University

3. Variety Seeking (VS):Peanut Butter Monday: Peter Pan Creamy Tuesday: Jif Crunchy Wednesday: Skippy Creamy

4. Variety Seeking (VS):Movies Weekend 3: When Harry Met Sally (Comedy) Weekend 2: The 6th Sense (Thriller) Weekend 1: Casino Royale (Action)

5. 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

6. 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.

7. 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).

8. 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).

9. 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

10. Research Problem:Unobserved Variety Seeking Level Changes • We don’t observeVS level changes that drive the consumer’s wants and needs for different brands or attributes.

11. 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.

12. 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).

13. 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.

14. Why Hidden Markov Model (HMM)?Markov Chain • The Markov chain can detect transition probabilities between observed states. (Table Example) • States: rain or no rain • States are observable.

15. 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.

16. Why Hidden Markov Model (HMM)?Hidden Markov Model • What if states of interest are not directly observable? (Table Example) • States: internal VS level (high vs. low) • States are unobservable.

17. 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).

18. 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

19. Proposed Model: Hidden MarkovMultinomial ProbitChoice Model • My proposed model has three components as follows: • Multinomial probit model component as a choice model, • HMM to track down consumers’ unobserved internal level of VS over time, and • Random coefficients model (hierarchical Bayesian model) to account for customer heterogeneity. In other words, VS level changes are investigated for each individual consumer.

20. Model Development:Hidden Markov Multinomial Probit Choice Model U = Utility h = household; j = alternative (brand); t = time (purchase occasion) P = price; F = feature (ad); D = display

21. Model Development:Hidden Markov Multinomial Probit Choice Model 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.

22. Model Development:Hidden Markov Multinomial Probit Choice Model 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.

23. Hidden Markov Multinomial Probit Choice Model(2-State Transition Probabilities) Q = transition probabilities between states L = low VS state H = high VS state

24. Model Estimation: MCMC • The model will be estimated using MCMC, which will have the following three components: • Multinomial probit model component, • Hidden Markov model (HMM) (Kim and Nelson 1999), and • Hierarchical Bayesian structure.

25. 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)

26. 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).

27. Peanut Butter Data

28. 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

29. 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.

30. 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).

31. 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.

32. 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.

33. 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

34. NCSU 개요 • 단과대학 (10개): • 농업생명과학 대학 • Design 대학 • 사범 대학 • 공대 • 자연자원 대학

35. NCSU 개요 • 단과대학 (10개): • 인문사회 대학 • 경영 대학 • 물리수학 대학 • 섬유대학 • 수의대

36. 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

37. NCSU 경영대학 • 학위 과정 • 회계학 석사 • 경영학 석사 (MBA) • 경제학 석사, 박사 • 농경제학 석사 • 학사 – 회계학, 경영학, 경제학

38. 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)

39. NCSU 경영대학 • 역사 • 1992년 단대 설립 • 2002년 MBA 과정 신설 • 교원: 110명

40. 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)

41. 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