The Ohio State University
Fisher College of Business
BA 951 Ph.D. Seminar in Marketing Models
Prof. Greg M. Allenby
2012 Syllabus
BA 951 will focus on recent developments of quantitative methods in marketing. The course is
targeted to students interested in developing a conceptual understanding of quantitative models
and an appreciation of the literature in this area. Quantitative models aim to explain consumer
and firm behaviors and their relationship to managerial decision making. This course surveys
quantitative research in marketing, with a focus on statistical and game-theoretic models. The
goal of the course is to a) raise students' awareness of this literature and b) stimulate new
research interests. By the end of the course, students should be familiar with the key issues and
approaches in quantitative marketing, the strengths of these research streams, and the
opportunities to extend them.
Approach
The course meets each week for 3-4 hours. For each topic, I will list a number of papers. We
will spend the majority of time reviewing three papers in depth. The last portion of the class will
be spent integrating the days’ readings. The class will be largely discussion oriented, though I
will at times interject to give a brief lecture. These lectures will range from technical discussions
to research taxonomies. For suggestions on reading these articles, please see the Appendix to this
syllabus, provided by Vithala Rao at Cornell.
Course Requirements
Each student is expected to read the required reading to be discussed. In addition, they are
expected to pursue additional optional readings as time permits to obtain a broader sense of
research in the area. Every week, students will be assigned to write a one page summary of a
given paper for the edification of themselves and their peers. These should be distributed to all
persons in the class, and include: objective of the paper, its unique contribution, why it is
important, hypotheses if any, assumptions in the model, key equations, key findings, key
limitations, and opportunities to extend the work. Each person will also be required to hand in a
one page summary of all the required readings for the week (how they inter-relate, what the key
questions are, what issues have been resolved, and what issues remain open). In addition, the
write-up should contain answers to each of the questions (A-K) listed in the Appendix on the last
page of this syllabus.
Finally, at the end of the quarter, students will hand in a research proposal that extends the work
of a paper presented at the 2012 Frank M. Bass UTD FORMS conference. The papers will be
available from the instructor. The proposal should outline why the idea is important, how it is
different from existing work, and present a model to implement the idea.
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Grading
Students will be graded using the following criteria: class participation (30%), paper summaries
(20%), and the final project (50%).
Course Framework
Marketing models can be categorized along two dimensions – topics and approaches. The
approaches used to model marketing phenomenon include statistical models (e.g., stochastic
models, hazard models, time series models, new empirical I/O, and spatial models) and
analytical models such as game theory and operations research. Topics can be categorized into
“external to the firm” marketing environments (e.g., Industry Such as Internet, Pharmaceutical,
etc., Competition, and Customers), and internal to the firm marketing policies (Price, Promotion,
Advertising, Distribution and Product) used in those contexts. As research issues drive the tools
used to solve them, we will organize the course by the topics as opposed to the approaches.
Acknowledgements
I would like to thank Carl Mela (Duke) from whom I have obtained the extensive reading list
below. His compilation of the material was done with assistance from Asim Ansari (Columbia);
Bart Bronnenberg (UCLA); Yuxin Chen (NYU); Pradeep Chintagunta (Chicago); Michaela
Draganska (Stanford); Wes Hartmann (Stanford); Rajeev Kohli (Columbia); Vithala Rao
(Cornell); Michel Wedel (Maryland); Christophe Van den Bulte (Wharton); and John Zhang
(Wharton).
Session Details
Specifically the course will be organized as follows (* denotes required, all other readings are
optional in case you would like more information or background on the topics). The papers can
be downloaded from Carl Mela's website:
http://faculty.fuqua.duke.edu/~mela/BA561/
Week 1: Course Introduction and Overview
Week 2: Economic and Descriptive Marketing Models
Statistical Models (Stochastic Models, Hazard Models, Time Series, Spatial, Market-level,
NEIO)
*Reiss, Peter C. (2011), “Descriptive Structural and Experimental Methods in Marketing
Research,” Marketing Science, forthcoming.
*Lehmann, Donald R., Leigh McAlister and Richard Staelin (2011), “Sophistication in
Research in Marketing,” Journal of Marketing, 75, July, 155-165.
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Chintagunta, Pradeep, Tulin Erdem, Peter Rossi, and Michel Wedel (2006), "Structural
Modeling in Marketing: Review and Assessment," 25, 6 (November-December), 604-616
(please also read attached commentaries by Mazzeo and Srinivasan).
Leeflang, P. S. H. and D. R. Wittink (2000), "Building Models for Marketing Decisions:
Past, Present and Future", International Journal of Research in Marketing, 17, 105-126.
Winer, R. S. (2000), "Comment on Leeflang and Wittink", International Journal of Research
in Marketing, 17, 141-145.
Carroll, J.D. and P.E. Green (1997), “Psychometric Methods in Marketing Research: Part II,
Multidimensional Scaling,” Journal of Marketing Research, 34 (May), 193-204.
Chapters 1 and 2 from An Introduction to Statistical Modelling, by Wojtek J Krzanowski,
Arnold publishers, 1998.
Elements of Model Building, Chapter 5 from Building Models for Marketing Decisions, by
Leeflang, Wittink, Wedel and Naert, Kluwer Academic Press, 2000.
Hanssens, Dominique M., Peter S.H. Leeflang, Dick R. Wittink, Market Response Models
and Marketing Practice Forthcoming, Applied Stochastic Models in Business and
Industry, 2004
Varian, Hal R. (1994), “How to Build an Economic Model in Your Spare Time,” working
paper, University of California, Berkeley.
Wedel, M., W. Kamakura, and U. Böckenholt (2000), "Marketing Data, Models and
Decisions", International Journal of Research in Marketing, 17, 203-208.
Analytical Models
*Moorthy, K. S. (1993), “Theoretical Modeling in Marketing,” Journal of Marketing, 57
(April), 92-106.
Gibbons, R. (1997), “An Introduction to Applicable Game Theory,” Journal of Economic
Perspectives, 11 (1), 127-149.
Moorthy, K. S. (1985), “Using Game Theory to Model Competition,” Journal of Marketing
Research, 22 (August), 262-282.
Brandenburger, A. (1992), “Knowledge and Equilibrium in Games,” Journal of Economic
Perspectives, 6(4), 83-101*
Goeree, Jacob K and Charles Holt (2001), “Ten Little Treasures of Game Theory and Ten
Intuitive Contradictions,” American Economic Review, vol. 91, no. 5, pp. 1402-22.
Week 3: Models of Consumer Behavior, Choice Models
Economic Foundations of Choice
*Chandukala, Sandeep R., Jaehwan Kim, Thomas Otter, Peter E. Rossi and Greg M. Allenby
(2008) "Choice Models in Marketing," in Foundations and Trends in Marketing, Now
Publishers.
*Satomura, Takuya, Jaehwan Kim and Greg M. Allenby (2011) "Multiple Constraint Choice
Models with Corner and Interior Solutions," Marketing Science, 30, 3, 481-490.
Classical Choice Models
Kamakura and Russell (1989), “A Probabilistic Choice Model for Market Segmentation and
Elasticity Structure,” Journal of Marketing Research, 26 (November), 379-90.
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Gudagni, P.M. and J.D.C. Little (1983), "A Logit Model of Brand Choice Calibrated on
Scanner Data," Marketing Science, 2 (Summer), 203-238.
Chapter 2-5, Train, Kenneth (2003), Discrete Choice Methods with Simulation, Cambridge
University Press.
Gupta, Sunil (1988), “Impact of Sales Promotions on When, What, and How Much to Buy,”
Journal of Marketing Research, 25 (4), 342-355.
Bayesian Choice Models
Review of Classical and Bayesian Inference, Chapters 1&2 from Ordinal Data Modeling, by
Johnson and Albert, Springer-Verlag, 1999.
*Gilbride, Timothy J. and Greg M. Allenby (2004), “A Choice Model with Conjunctive,
Disjunctive, and Compensatory Screening Rules,” Marketing Science, 23, 3 (Summer),
391-406.
Gilbride, Timothy J. and Greg M. Allenby (2006), “Estimating Heterogeneous EBA and
Economic Screening Rule Choice Models,” Marketing Science, 25, September-October,
494-509.
Allenby, Greg, and Peter E. Rossi (2003), Bayesian Statistics and Marketing, Marketing
Science, 304-328.
Aggregate Choice Models
Bodapati, Anand V., and Sachin Gupta (2004), “The Recoverability of Segmentation
Structure from Store-level Aggregate Data,” forthcoming, Journal of Marketing
Research.
Lifetime Value Models
Gupta, Sunil, Donald R. Lehmann, and Jennifer Stuart (2004), “Valuing Customers,” Journal
of Marketing Research, Journal of Marketing Research, 41, 1(February), 7-18.
Fader, Peter S., Bruce G.S. Hardie and Ka Kok Lee (2005), “RFM and CLV: Using IsoValue Curves for Customer Base Analysis,” Journal of Marketing Research, 42 (4), 415430.
Oded Netzer, James M. Lattin, and V. Srinivasan (2008), "A Hidden Markov Model of
Customer Relationship Dynamics," Marketing Science, 27(March-April): 185 - 204.
Week 4: Consumer Models: Social Choice
*Wes Hartmann, Puneet Manchanda, Harikesh Nair, Matt Bothner, Peter Dodds, Dave
Godes, Karthik Hosanagar and Catherine Tucker (2008), “Modeling Social Interactions:
Identification, Empirical Methods and Policy Implications,” Seventh Triennial Choice
Symposium Session paper, Marketing Letters, 19(4), pg. 287-304.
*Godes, David and Dina Mayzlin (2004), “Using Online Conversations to Study Word-ofMouth Communication,” Marketing Science, 23, 4, 545-560.
*Ahn, Dae-yong, Jason Duan and Carl F. Mela (2011), “A Dynamic General Equilibrium
Model of User Generated Content,” working paper, Duke.
Chevalier and Mayzlin (2006), “The Effect of Word of Mouth on Sales: Online Book
Reviews,” Journal of Marketing Research, 43, August, 345-354.
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Zubcsek, Paul and Miklos Sarvary (2011), “Advertising to a Social Network,” Quantitative
Marketing and Economics, 9, 71-107.
Du, Rex and Wagner Kamajura (2011), “Measuring Contagion in the Diffusion of Consumer
Packaged Goods,” Journal of Marketing Research, 43, February, 28-47.
Van der Lars, Ralf, et al. (2010), “A Viral Branching Model for Predicting the Spread of
Electronic Word of Mouth,” Marketing Science, 29, 2, 348-365.
Jing, Bing (2011), “Social Learning and Dynamic Pricing of Durable Goods,” Marketing
Science, forthcoming.
Stephen, Andrew and Olivier Toubia (2010), “Deriving Value from Social Commerce
Networks,” Journal of Marketing Research, 47, 215-228.
Nair, Harikesh, Puneet Manchanda, and Tulikaa Bhatia (2010), “Asymmetric Social
Interactions in Physician Prescription Behavior: The Role of Opinion Leaders,” Journal
of Marketing Research, 47, 883-895.
Ansari, Asim, Oded Koenigsberg and Florian Stahl (2011), “Modeling Multiple
Relationships in Social Networks,” Journal of Marketing Research, 713-728.
Amaldoss, Wilfred and Sanjay Jain (2009), “Reference Groups and Product Line Decisions:
An Experimental Investigation of Limited Editions and Product Proliferation,”
Management Science, forthcoming.
Week 5: Consumer Models: Dynamics and Search
*Ching, Andrew, Susumu Imai, Masakazu Ishihara and Neelam Jain (2009) "A Practitioner's
Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models,"
Quantitative Marketing and Economics, forthcoming.
*De Los Santos, Barbur, Ali Hortacsu, and Matthijs Wildenbeest (2009), "Testing Models of
Consumer Search using Data on Web Browsing and Purchasing Behavior," working
paper, Indiana University
*Moorthy, Sridhar, Ratchford Brian T., and Debabrata Talukdar (1997), "Consumer
Information Search Revisited: Theory and Empirical Analysis," Journal of Consumer
Research, 23 (4), 263-77.
*Stigler, George J. (1961), "The Economics of Information," The Journal of Political
Economy, 69 (3), 213-25.
Katona, Zsolt and Miklos Sarvary (2009), "The Race for Sponsored Links: Bidding Patterns
for Search Advertising, Marketing Science, Articles in Advance.
Chen, Xiaohong, Han Hong, and Matthew Shum: "Nonparametric likelihood ratio model
section tests between parametric likelihood and moment condition models," Journal of
Econometrics 141, 109-40, 2007.
Hong, Han and Matthew Shum: Using price distributions to estimate search costs," RAND
Journal of Economics, 37, 257-75, 2006.
Kim, Jun, Paulo Albuquerque and Bart Bronnenberg (2009), "Online Demand Under Limited
Consumer Search," working paper, SSRN.
Weitzman, Martin L. (1979), "Optimal Search for the Best Alternative," Econometrica, 47,
641-54.
Kenneth C. Wilbur and Yi Zhu, (2009), "Click Fraud," Marketing Science, 28, 293-308.
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Yao, Song, and Carl F. Mela (2009), "A Dynamic Model of Sponsored Search Advertising,"
working paper, Duke University.
Week 6: Competition and Positioning
Market Structure and Brand Differentiation
Bijmolt ,Tammo H. A.and Michel Wedel (1999), “A Comparison of Multidimensional
Scaling Methods for Perceptual Mapping,” Journal of Marketing Research, 36(2), 277285.
Elrod, Terry (1991), “Internal Analysis of Market Structure: Recent Developments and
Future Prospects,” Marketing Letters, 2, 253-266.
Van Heerde, Harald, Carl F. Mela, and Puneet Manchanda (2004), “The Dynamic Effect of
Innovation on Market Structure,” Journal of Marketing Research, 41, 2 (May), 166-183.
Market Entry and Location: Economic Foundations
*Hotelling, H., 1929, “Stability in Competition,” Economic Journal, 39, pp. 41-57.
D'Aspremont, C., Gabszewicz, J. J., and Thisse, J. F. (1979), “On Hotelling's ‘Stability in
Competition’,” Econometrica, 47, pp.1145-1150.
*Cabral, Luís and Miguel Villas-Boas (2005), “Bertrand Supertraps,” Management Science,
51, 4 (April), 599-613. (skim)
Narasimhan, C. and Z. J. Zhang (2000), “Market Entry Strategy Under Firm Heterogeneity
and Asymmetric Payoffs,” Marketing Science, 19 (4), 313-327.
NEIO Models
*Reiss, Peter C. and Frank A. Wolak (2005), “Structural Econometric Modeling: Rationale
and Examples from Industrial Organization,” prepared for the Handbook of
Econometrics, Vol. 6. Pages 1-37 only.
Goettler, Ronald and Ron Shachar (2001) “Spatial Competition in the Network Television
Industry,” RAND Journal of Economics, 32 (4), 624-656.
Nevo, Aviv (2000), “A Practitioner’s Guide to Estimation of Random Coefficients Logit
Models of Demand,” Journal of Economics & Management Strategy, 9(4), 513-548.
Chintagunta, Pradeep, Vrinda Kadiyali, and Naufel J. Vilcassim (2004), “Structural
Modeling of Competition: A Marketing Perspective, in Assessing Marketing
Performance, Christine Moorman and Donald R. Lehman (Editors), Marketing Science
Institute, Boston, MA.
Franses, Philip Hans (2005), “On the Use of Marketing Models for Policy Simulation in
Marketing, Journal of Marketing Research, 42, 1 (February), 4-14.
Dubé, Jean-Pierre, K. Sudhir, Andrew Ching, Gregory Crawford, Michaela Draganska,
Jeremy Fox, Wesley Hartmann, Günter J. Hitsch, V. Viard, Miguel Villas-Boas and
Naufel Vilcassim (2005), “Recent Advances in Structural Econometric Modeling:
Dynamics, Product Positioning and Entry,” Marketing Letters, 16(3-4), 209-24.
Entry and Location: NIEO Models
*Zhu, Ting, Vishal Singh, and Mark D. Manuszak (2009), "Market Structure and
Competition in the Retail Discount Industry," Journal of Marketing Research, 46, 4
(August), 453-466.
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Hartmann, Wesley R. (2009), Demand Estimation with Social Interactions and the
Implications for Targeted Marketing, forthcoming, Marketing Science.
Mazzeo, Michael J (2002), “Product Choice and Oligopoly Market Structure,” The Rand
Journal of Economics, 2002 (Summer), 33, 2, 221-242.
Duan, Jason, and Carl F. Mela (2008), “The Role of Spatial Demand on Outlet Location and
Pricing,” working paper, Duke University.
Chan, Tat Y., V. Padmanabhan and P.B. Seetharaman (2007), “Modeling Location and
Pricing Decisions in the Gasoline Market: A Structural Approach,” Journal of Marketing
Research, 44, 4, 622-635.
Vitorino, Maria Ana (2007), “Empirical Entry Games with Complementarities: An
Application to the Shopping Center Industry,” working paper, Graduate School of
Business, University of Chicago.
Beresteanu, Arie and Paul Ellickson (2006), "The Dynamics of Retail Oligopolies."
Seim, Katja (2006), "An Empirical Model of Firm Entry with Endogenous Product-type
Choices," Rand Journal of Economics, 37, 3, 619-640.
Draganska, Michaela, Michael Mazzeo and Katja Seim (2007), "Beyond Plain Vanilla:
Modeling Joint Product Assortment and Pricing Decisions," working paper, Stanford
Univeristy.
Singh, Vishal and Ting Zhu (2008), "Pricing and Market Concentration in Oligopoly
Markets," Marketing Science, 27, 1020-1035.
Week 7: Product
Idea Generation
Goldenberg Jacob, David Mazursky and Solomon Sorin (1999), “Creativity Templates:
Towards Identifying the Fundamental Schemes of Quality Advertisements, Marketing
Science, Vol.18, No. 3 p. 333-51.
Toubia, Oliver (2006), “Idea Generation, Creativity, and Incentives,” Marketing Science, 25,
September-October, 411-425.
Toubia, Olivier (2007), and Laurent Florès, “Adaptive Idea Screening Using Consumers,”
Marketing Science, 26, May-June, 342-360.
Conjoint & Optimal Product Design & Concept Testing
*Satomura, Takuya, Jeff D. Brazell and Greg M. Allenby (2012) "Choice Models for
Budgeted Demand and Constrained Allocation," working paper, Ohio State University.
Green, P.E. and V. Srinivasan (1990), "Conjoint Analysis in Marketing: New Developments
with Implications for Research and Practice," Journal of Marketing, October, 3-19.
Neeraj Arora and Joel Huber (2001) “Improving Parameter Estimates and Model Prediction
by Aggregate Customization of Choice Experiments,” Journal of Consumer Research,
26, 2 (September) 273-283.
Toubia, Oliver, Duncan Simester, and John R. Hauser (2003), “Fast Polyhedral Adaptive
Conjoint Estimation,” Marketing Science, 22:3, pp. 273-303, 2003.
Ding, Min (2007), “An Incentive-Aligned Mechanism for Conjoint Analysis,” Journal of
Marketing Research, 44, 2 (May), 214-223.
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Luo, Lan, P.K. Kannan, and Brian T. Ratchford (2008), " Incorporating Subjective
Characteristics in Product Design and Evaluations.Preview ," Journal of Marketing
Research, 45, 2, 182-194.
Across Products: Product Lines and Market Baskets
Moorthy, K. (1984), “Market Segmentation, Self-selection, and Product Line Design,”
Marketing Science, 3(4): 288-307.
Amaldoss, Wilfred and Sanjay Jain (2009), “Reference Groups and Product Line Decisions:
An Experimental Investigation of Limited Editions and Product Proliferation,”
Management Science, forthcoming.
Desai, Preyas S. (2001), "Quality Segmentation in Spatial Markets: When Does
Cannibalization Affect Product Line Design? Marketing Science, 20, 3, 265–283.
Dobson, G. and S. Kalish (1988), "Pricing and Positioning a Product Line," Marketing
Science, 7 (Spring), 107-125.
Bell, David R, and James M. Lattin (1998), “Shopping Behavior and Consumer Preference
for Store Price Format: Why "Large Basket" Shoppers Prefer EDLP,” Marketing Science.
17, 1, 66-88.
Hitsch, Gunter (2006), “An Empirical Model of Optimal Dynamic Product Launch and Exit
Under Demand Uncertainty,” Marketing Science, 25, 1, 25-50.
Jing, Bing (2007), “Product Differentiation Under Imperfect Information: When Does
Offering a Lower Quality Pay?” Quantitative Marketing and Economics, 26, 3 (May–
June) 400–421.
Diffusion of Innovations
*Ansari, Asim (2004), Bass Diffusion Model Note
Mahajan, Vijay, Eitan Muller, and Frank M. Bass (1990), “New Product Diffusion Models In
Marketing: A Review And Directions for Research,” Journal of Marketing, 54 (1), 1-26.
Rao, Ambar G. Rao and Masataka Yamada (1988), “Forecasting with a Repeat Purchase
Diffusion Model”, Management Science, 34, 6 (Jun), 734-752.
Van den Bulte, Christophe and Gary L. Lilien (2001), “Medical Innovation Revisited: Social
Contagion versus Marketing Effort,” American Journal of Sociology, 106 (5), 1409-1435.
Song, I., and P. K. Chintagunta (2003): A Micromodel of New Product Adoption with
Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera
Category," working paper, University of Florida.
Godes, David and Dina Mayzlin (2004), “Using Online Conversations to Study Word-ofMouth Communication,” Marketing Science, 23, 4, 545-560.
Van den Bulte, Christophe and Yogesh V. Joshi (2007), “New Product Diffusion with
Influentials and Imitators,” Marketing Science, 26, 3 (May–June), 400–421.
Product Lifecycle and Pioneering Advantage
*Brett R. Gordon (2009), "A Dynamic Model of Consumer Replacement Cycles in the PC
Processor Industry," Marketing Science, Articles in Advance.
Boulding, W. and M. Christen (2003), “Sustainable Pioneering Advantage? Profit
Implications of Market Entry Order,” Marketing Science, 22 (3), 371-392.
Boulding, W. and Markus Christen (2008), "Disentangling Pioneering Cost Advantages and
Disadvantages," Marketing Science.
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Robinson, William T., and Sungwook Min (2002), “Is the First to Market the First to Fail?
Empirical Evidence for Industrial Goods Businesses,” Journal of Marketing Research,
39, 1 (February), 120-128.
Golder, Peter, and Gerard Tellis (2004), “Growing, Growing, Gone: Cascades, Diffusion, and
Turning Points in the Product Lifecycle,” Marketing Science, 23, 2(Spring), 207-218.
Gowrisankaran, Gautam and Marc Rysman (2007), “Dynamics of Consumer Demand for
New Durable Goods,” working paper, Washington University in St. Louis.
Goettler, Ronald and Brett Gordon (2008), "Durable Goods Oligopoly with Innovation:
Theory and Empirics," working paper, Columbia University.
Product Roll-out
*Fader, Peter S., Bruce G.S. Hardie (2004), “A Dynamic Changepoint Model for New
Product Sales Forecasting,” Marketing Science, 23, 1 (Winter), 50-65.
Fader, Peter S. and Bruce G.S. Hardie (2000), Applied Probability Models in Marketing
Research (Supplementary Materials for the A/R/T Forum Tutorial), Working Paper,
London Business School
Bronnenberg, Bart J. and Carl F. Mela (2004), “Market Roll-out and Retailer Adoption for
New Brands,” Marketing Science, 23, 4, 500-519.
Banerjee, Sumitro and Miklos Sarvary (2009), "How incumbent firms foster consumer
expectations, delay launch but still win the markets for next generation products,"
Quantitive Marketing and Economics, forthcoming
Week 8: Pricing
Price-matching and Price Discrimination
*Shin, Jiwoong and K. Sudhir (2010), “A Customer Management Dilemma: When Is It
Profitable to Reward One’s Own Customers,” Marketing Science, 29, 4, 671-689.
*Dong, Xiaojing; Punet Manchanda and Pradeep K Chintagunta (2009), "Quantifying the
Benefits of Individual-Level Targeting in the Presence of Firm Strategic Behavior,"
Journal of Marketing Research, 46, 2 (May), 207-22.
Chen, Yuxin and Z. John Zhang (2009), "Dynamic Targeted Pricing with Strategic
Consumers," International Journal of Industrial Organization, 27, 1. 43-50.
Jain, Sanjay, "Digital Piracy: A Competitive Analysis," Marketing Science, forthcoming.
Rossi, Peter E., Robert E. McCulloch and Greg M. Allenby (1996), “The Value of Purchase
History Data in Target Marketing” Marketing Science, 15(4), 321-340.
Shaffer, Greg and Z. John Zhang (2002), “Competitive One-to-One Promotions,”
Management Science, 48 (No. 9), pp. 1143-1160.
Liu, Yunchuan and Z. John Zhang (2005), "The Benefits of Personalized Pricing in a
Channel," Marketing Science.
Asymmetric Information, Signaling and Screening,
Akerlof, George A. (1970), “The Market for ‘Lemons’: Quality Uncertainty and the Market
Mechanism,” Quarterly Journal of Economics, vol. 84, no. 3, pp. 488-500.
Chu, Wujin (1992), “Demand Signaling and Screening in Channels of Distribution,”
Marketing Science, 11 (No. 4), pp. 327-347.
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Milgrom, Paul and John Roberts (1986), “Price and Advertising Signals of Product Quality,”
Journal of Political Economy, 94 (No. 4), 796-821.
Auctions
Reiss, Peter C. and Frank A. Wolak (2005), “Structural Econometric Modeling: Rationale
and Examples from Industrial Organization,” prepared for the Handbook of
Econometrics, Vol. 6. Pages 88-110 only.
Park, Young Hoon, and Eric Bradlow (2005), “An Integrated Model for Bidding Behavior in
Internet Auctions: Whether, Who, When and How Much,” Journal of Marketing
Research, 42, 4 (November), 470-482.
Bradlow, E.T. and Park, Y-H. (2007), “Bayesian Estimation of Bid Sequences in Internet
Auctions Using a Generalized Record Breaking Model,” Marketing Science, 26, MarchApril, 218-229.
Yao, Song and Carl F. Mela (2007), “Online Auction Demand,” working paper, Duke
University.
Zeithammer, Robert (2007), “Forward-Looking Bidding in Online Auctions,” Journal of
Marketing Research, 43, 3 (August), 462-476.
Jap, Sandy and Prasad Naik (2008), Bid Analyzer; A Method for Estimation and Selection of
Dynamic Bidding Models," Marketing Science, forthcoming.
Dynamics in Pricing
*Dube, Jean-Pierre, Gunter Hitsch, and Peter E. Rossi (2009), “Do Switching Costs Make
Markets Less Competitive,” Journal of Marketing Research, 46, 435-445.
Dube, J.P., Guenter J., Hitsch and Pradeep K. Chintagunta (2008), "Tipping and
Concentration in Markets with Indirect Network Effects," working paper, University of
Chicago.
Gijsenberg, Maarten, Harald van Heerde, Marnik Dekimpe, and Jan-Benedict Steenkamp
(2009), "Advertising and Price Effectiveness Over the Business Cycle," working paper,
Leuven.
Dekimpe, Marnik and Dominique M. Hanssens (1999), “Sustained Spending and Persistent
Response: A New Look at Long-Term Marketing Profitability,” Journal of Marketing
Research, 36, 4 (November), 387-412.
Ailawadi, Kusum, Praveen K. Kopalle, and Scott A. Neslin (2005), “Predicting Competitive
Response to a Major Policy Change: Combining Game Theoretic and Empirical
Analyses,” Marketing Science, 24, 1 (Winter), 12-24.
Keane, M. (1997), “Modeling Heterogeneity and State Dependence in Consumer Choice
Behavior,” Journal of Business Economics and Statistics, 15(3), 310-327.
Jedidi, Kamel, Carl F. Mela, and Sunil Gupta (1999), “Managing Advertising and Promotion
for Long-Run Profitability,” Marketing Science, 18, 1 (Winter), 1-22.
Chapters 4 and 6, Hanssens Dominique M., Leonard J. Parsons and Randall L. Schultz
(2001), Market Response Models, Kluwer, Boston.
Baohong Sun, Scott A. Neslin, and Kannan Srinivasan (2003), “Measuring the Impact of
Promotions on Brand Switching When Consumers Are Forward-Looking,” Journal of
Marketing Research, 40 (4) 389-405.
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Dekimpe MG, Hanssens DM (2004), Persistence modeling for assessing marketing strategy
performance, in Lehmann D, Moorman C,ed.: Assessing Marketing Strategy Perfomance,
(Marketing Science Institute).
Decision Support Systems
Smith, Stephen A. and Dale D. Achabal (1998), “Clearance Pricing and Inventory Policies
for Retail Chains,” Management Science,44, 3 (Mar), 285-300.
Schmittlein, David C. and Donald G. Morrison, “A Live Baby or Your Money Back: The
Marketing of In Vitro Fertilization Procedures,” Management Science, 49, 12, 1617 1635
Aviv, Yossi and Amit Pazgal (2005), “A Partially Observed Markov Decision Process for
Dynamic Pricing,” Management Science, 51, 9 (September), 1400–1416.
Wierenga, Berend, Gerrit H. Van Brugen, and Richard Staelin (1999), “The Success of
Marketing Management Support Systems,” Marketing Science, 18, 3, 196-207.
Lewis, Michael (2005), “A Dynamic Programming Approach to Customer Relationship
Pricing,” Management Science, 51, 6(June), 986–994.
Price Promotions
Paul B. Ellickson and Sanjog Misra (2008), "Supermarket Pricing Strategies," Marketing
Science, 27, 811-828.
Desai, Preyas S., Oded Koenigsberg, and Devavrat Purohit (2009), "Forward Buying by
Retailers," Journal of Marketing, forthcoming.
Rajiv Lal (1990), “Price Promotions: Limiting Competitive Encroachment,” Marketing
Science, 9 (3), 247-262.
Musalem, Andres, Eric Bradlow and Jagmohan Raju (2006) "Estimating consumer
preferences and coupon usage from aggregate data," forthcoming, Marketing Science.
Varian, H (1980) “A Model of Sales,” American Economic Review, (September), 651-659.
Narasimhan, Chakravarthi (1988), “Competitive Promotion Strategies,” Journal of Business,
427-449.
Raju, Jagmohan , V. Srinivasan and Rajiv Lal (1990), “The Effects of Brand Loyalty on
Competitive Price Promotional Strategies,” Management Science, 36 (3), 276-304.
Lal, Rajiv, John D.C. Little and Miguel Villas-Boas (1996), “A Theory of Forward Buying
and Trade Deals,” Marketing Science, 15, 1 (Winter), 21-37.
Neslin, Scott (2002), Sales Promotion, MSI Monograph Series, Cambridge, MA.
Van Heerde, Harald J., Sachin Gupta, and Dick R. Wittink (2003), “Is 75% of the Sales
Promotion Bump due to Brand Switching? No, Only 33% Is,” Journal of Marketing
Research, 40, No. 4, 481-491.
Bijmolt, Tammo, H.J. van Heerde, and Rik G.M. Pieters (2005), “New Empirical
Generalizations on the Determinants of Price Elasticity, Journal of Marketing Research,
42, 2 (May), 141-156.
Ailawadi, Kusum L., Karen Gedenk, Christian Lutzky, and Scott A. Neslin (2007),
“Decomposition of the Sales Impact of Promotion-Induced Stockpiling.” Journal of
Marketing Research, 44, 3 (August), 450-467.
Vincent Nijs, Kanishka Misra, Eric T. Anderson, Karsten Hansen, and Lakshman
Krishnamurthi (2009), Channel Pass-Through of Trade Promotions, Marketing Science,
Articles in Advance.
11
Reference Price
Kopalle, P., A. Rao and J. Assuncao (1996), “Asymmetric Reference Price Effects and
Dynamic Pricing Policies,” Marketing Science, 15 (1), 60-85.
Kalyanaram, Gurumurthy, and Russell S. Winer (1995), “Empirical Generalizations from
Reference Price Research,” Marketing Science, 14, 3 Part 2, G161-G169.
Bell, David R. and James M. Lattin (2000), “Looking for Loss Aversion in Scanner Panel
Data: The Confounding Effect of Price Response Heterogeneity,” Marketing Science, 19
(2), 185-200.
Reward Programs
Kim, Byung-Do, Mengze Shi, and Kanaan Srinivasan (2001), “Reward Programs and Tacit
Collusion,” Marketing Science, 20, 2 (Spring) 99-120.
Leenheer, Jorna, Tammo H.A. Bijmolt, Harald J. van Heerde, Ale Smidts (2004), “Do
Loyalty Programs Enhance Behavioral Loyalty? A Market-Wide Analysis Accounting
for Endogeneity,” working paper, Tilburg University.
Week 9: Distribution/Channel
Store Brands
Geylani, Tansev, Anthony J. Dukes, and Kannan Srinivasan (2007), “Strategic Manufacturer
Response to a Dominant Retailer,” Marketing Science, 26, March-April, 164 - 178.
Performance of Store Brands: A Cross-Country Analysis of Consumer Store-Brand
Preferences, Perceptions, and Risk Tulin Erdem, Ying Zhao, Ana Valenzuela, Journal of
Marketing Research, (February), 86Sayman, Serdar, Stephen J. Hoch, and Jagmohan Raju (2002), “Positioning of Store Brands,”
Marketing Science, 21, 4 (Fall), 378-397.
Corstjens, Marcel and Rajiv Lal (2000), “Building Store Loyalty Through Store Brands,”
Journal of Marketing Research, 37, 3 (August), 281-282.
Dhar, Sanjay K, Hoch, Stephen J. (1997), “Why Store Brand Penetration Varies by Retailer,
Marketing Science, 16, 3, 208-217.
Vertical Games
*McGuire, T. and R. Staelin (1983), “An Industry Equilibrium Analysis of Downstream
Vertical Integration,” Marketing Science, 2 (No. 2), pp. 161-191.
*Draganska, Michaela, Daniel Klapper, and Sofia B. Villas-Boas, "A Larger Slice or a
Larger Pie? An Empirical Investigation of Bargaining Power in the Distribution
Channel," Marketing Science, 39, 2, 57-74.
Desai, Preyas, Oded Koenigsberg and Devavrat Purohit (2004), “Strategic Decentralization
and Channel Coordination,” Quantitative Marketing and Economics, 2 (1), 5-22.
Lal, Rajiv (1990), "Manufacturer Trade Deals and Retail Price Promotions," Journal of
Marketing Research, Vol. 27 (November), pp. 428-44.
Shankar, Venkatesh, and Ruth N. Bolton (2004), “An Empirical Analysis of Determinants of
Retailer Pricing Strategy, Marketing Science, 23, 1 (Winter), 28-49.
12
Purohit, Debu (2004) “Channel Economics.” Chapter in Customer Relationship
Management: State of the Art. Forthcoming.
Sudhir, K. and Vithala R. Rao (2006), “Do Slotting Allowances Enhance Efficiency or
Hinder Competition?” Journal of Marketing Research (JMR), 43, 2 (May), 137-155.
Kumar, Nanda and Ranran Ruan (2006), “On Manufacturers Complementing the Traditional
Retail Channel with a Direct Online Channel,” Quantitative Marketing and Economics, 4,
3 (September), 289–323.
Vilas-Boas, Sofia (2007), "Vertical Relationships Between Manufacturers and Retailers:
Inference With Limited Data," The Review of Economic Studies, 74, 2 625-652 (see also
the technical appendix).
Bhaskaran, Sreekumar R. and Stephen M. Gilbert (2009), "Implications of Channel Structure
for Leasing or Selling Durable Goods," Marketing Science, Articles in Advance.
Asymmetric Information - Principal Agent
*Misra, Sanjog and Harikesh Nair (2009), "Quota Dynamics and the Intertemporal
Allocation of Salesforce Effort," Quantitative Marketing and Economics, forthcoming.
Basu, Amiya L., Rajiv Lal, V. Srinivasan, and Richard Staelin (1985), “Salesforce
Compensation Plans: An Agency Theoretic Perspective,” Marketing Science, 4
(Autumn), 267-291.
Chung, Doug, Thomas Steenburgh annd K. Sudhir (2009), "Do Bonuses Enhance Sales
Productivity," working paper, Yale.
Holmstrom, Bengt (1979), “Moral Hazard and Observability,” Bell Journal of Economics,
vol. 10, no. 1, pp. 74-91.
Lal, Rajiv and Richard Staelin (1986), Salesforce Compensation Plans in Environments with
Asymmetric Information,” Marketing Science, 5, Summer, 179-198.
Mishra, Birenda K. and Ahsutosh Prasad (2004), “Centralized Pricing Versus Delegating
Pricing to the Salesforce Under Information Asymmetry,” Marketing Science, 23, 1
(Winter), 21-27.
Sales Force Issues (Sales Call Planning, Territory Alignment, Compensation)
Godes, David (2003), “In the Eye of the Beholder: An Analysis of the Relative Value of a
Top Sales Rep Across Firms and Products, Marketing Science, 22, 2 (Spring), 161
Retail Assortment
Anthony J. Dukes, Tansev Geylani, and Kannan Srinivasan (2009), "Strategic Assortment
Reduction by a Dominant Retailer," Marketing Science, 28, 309-319.
Briesch, Richard A, Pradeep K Chintagunta, and Edward J Fox, "How Does Assortment
Affect Grocery Store Choice?" Journal of Marketing Research, 46, 2 (May), 176-189.
International
Ter Hofstede, Frenkel, Jan-Benedict E M Steenkamp, and Michel Wedel (1999),
“International Market Segmentation Based on Consumer-product Relations,” Journal of
Marketing Research, Vol. 36, Iss. 1 (February); 1-17.
Steenkamp, Jan-Benedict E M, and Frenkel Ter Hofstede (2002), “International Market
Segmentation: Issues and Perspectives, International Journal of Research in Marketing,
19, 3, (September), p. 185
13
Week 10: Advertising
Budgeting
*Chen, Yuxin, Yogesh Joshi, Jagmohan Raju, and Z. John Zhang (2009), “A Theory of
Combative Advertising,” Marketing Science, 28, 1, 1-19.
*Wilbur, Kenneth (2008), "A Two-Sided, Empirical Model of Television Advertising and
Viewing Markets, Marketing Science, 27, 3 (May-June), 356-378.
Godes, David, Elie Ofek, and Miklos Sarvary (2009), "Content vs. Advertising: The Impact
of Competition on Media Firm Strategy," Marketing Science, 28, 20-35.
Little, John D. C. (1979), “Aggregate Advertising Models: The State of the Art,” Operations
Research, 27, 4 (Jul. – Aug), 629-667.
Chintagunta, Pradeep K. (1993), “Investigating the Sensitivity of Equilibrium Profits to
Advertising Dynamics and Competitive Effects,” Management Science, 39, 9(Sep), 11461162.
Bass, Frank M., Anand Krishnamoorthy, Ashutosh Prasad, and Suresh P. Sethi (2005),
“Generic and Brand Advertising Strategies in a Dynamic Duopoly,” Marketing Science,
24, 4, 556 – 568.
Villas-Boas, J Miguel (1993), “Predicting Advertising Pulsing Policies in an Oligopoly: A
Model and Empirical Test,” Marketing Science, 12, 1 (Winter), 88-102.
Feichtinger, Gustav, Richard F Hartl, and Suresh P Sethi (1994), Dynamic Optimal Control
Models in Advertising: Recent Developments,” Management Science, 40, 2 (Feb), 195216.
Erickson, Gary M. (1997), “Note: Dynamic Conjectural Variations in a Lancaster
Oligopoly,” Management Science, 43, 11 (November), 1603-1608.
Erickson, Gary M (1995), “Advertising Strategies in a Dynamic Oligopoly,” Journal of
Marketing Research, 32, 2 (May), 233-237.
Fruchter, Gila E. and Shlomo Kalish, “Closed-Loop Advertising Strategies in a Duopoly,”
Management Science, Vol. 43, No. 1. (Jan., 1997), pp. 54-63
Feinberg, Fred M. (2001), “On Continuous-time Optimal Advertising Under S-shaped
Response,” Management Science, 47, 11(Nov), 1476Media Planning
*Naik, Mantrala and Alan Sawyer (1998), “Planning Media Schedules in the Presence of
Dynamic Advertising Quality,” Marketing Science, 17 (3), 214-235
Simester, Duncan I., Peng Sun and John N. Tsitsiklis, “Dynamic Catalog Mailing Policies,”
Management Science, 52, 5 (May), 683-696.
Chessa , Antonio G. and Jaap M. J. Murre (2007), “A Neurocognitive Model of
Advertisement Content and Brand Name Recall,” Marketing Science, 26, JanuaryFebruary, 130-141.
Cui, Geng, Man Leung Wong and Hon-Kwong Lui (2006), “Machine Learning for Direct
Marketing Response Models: Bayesian Networks with Evolutionary Programming,”
Management Science, 52, 4 (April), 597–612.
Gonul, Fusun and Frenkel Ter Hofstede (2006), "How to Compute Optimal Catalog Mailing
Decisions ," Marketing Science, 25 (1) 65-74.
14
Advertising Message
Lodish, Leonard M, Abraham, Magid, Kalmenson, Stuart, Livelsberger, Jeanne, et al. (1995),
“How T.V. Advertising Works: A Meta-analysis of 389 Real World Split Cable T.V.
Advertising Experiments, Journal of Marketing Research, 32, (May), 125-139.
Mizik, Natalie and Robert Jacobson (2008), "The Financial Value Impact of Perceptual
Brand Attributes," Journal of Marketing Research, forthcoming.
Tellis, Gerard, Rajesh Chandy and Pattana Thaivanich (2000), “Which Ad Works, When,
Where and How Often? Modeling the Effects of Direct Television Advertising,” Journal
of Marketing Research, 37, 1 (February), 32-46.
Elsner, Ralf, Manfred Krafft, and Arnd Huchzermeier (2004), “Optimizing Rheania’s Direct
Marketing Business Through Multi-level Direct Modeling (DMLM) in a Multi-catalogBrand Environment, Marketing Science, 23, 2 (Spring), 192-206.
Ansari, Asim, and Carl F. Mela (2003), “E-Customization,” Journal of Marketing Research,
40, 2 (May), 2003, 131-145.
Gal-Or, Esther, Mordechai Gal-Or, Jerrold H. May and William E. Spangler (2006),
“Targeted Advertising Strategies on Television,” Management Science, 52, 5 (May), 713725.
Advertising v. Promotions
Agrawal, Deepak (1996), “Effect of Brand Loyalty on Advertising and Trade Promotions: A
Game Theoretic Analysis with Empirical Evidence,” Marketing Science, 15, 1 (Winter),
86-104.
Jedidi, Kamel, Carl F. Mela, and Sunil Gupta (1999), “Managing Advertising and Promotion
for Long-Run Profitability,” Marketing Science, 18, 1 (Winter), 1-22.
15
Appendix: A Suggested Guide for "Reading" Journal Articles, by Vithala Rao, Cornell
Allow enough time to read the article at least twice. In the first reading, which may be quite superficial,
try to get a general idea of the subject matter examined, uniqueness of the approach, and significant
results. In the second reading, try to be critical of the concepts, assumptions, models, and application. If
necessary, look over the article for a third time to seek a sharper understanding of the article and to
evaluate where else the results and models can be applied.
While reading the article try and answer the questions indicated below for yourself. Doing so should
significantly enhance your understanding of the research reported and your ability to critique the work.
Note that some published articles may not fit this format.
A. What aspect(s) of the business system is (are) being studied by the author? (E.g., relationship
between a firm and competitor, consumer choices over time.)
B. What are some significant research issues addressed in the paper? Reflect upon why they are
significant.
C. What specific managerial decisions can be addressed by the results reported in the paper? Are these
decisions made better when the recommendations from this research are adopted?
D1. What is (are) the microunit(s) whose "behavior" is (are) being addressed in the paper?
D2. State the basic model of the behavior of the microunit in words or as a flow chart. State the premises
and assumptions of the model. Identify major constructs.
D3. State the basic model of the behavior of the microunit in a mathematical form and identify the
variables (predictor or criterion) and the parameters (unknown) of the model.
E. Does the paper deal with aggregation of the model across various microunits or segments? If so, how
is this aggregation accomplished? If aggregation is not considered, what are the effects of the
assumption of homogeneity?
F. How are the variables of the model measured? Are these measures appropriate? What are the
sources of data? How reliable are these measures? What are some alternative ways of measuring the
variables?
G. How are the parameters of the model estimated? Are the properties of the estimates discussed? (For
example, are they unbiased and/or consistent?)
H. Is the empirical application discussed in the papers appropriate? Are the results validated? (This
aspect may not be relevant for some articles.)
I.
Are the results interpreted well? Are there any alternative explanations of the results?
J. Identify one or two other applications of the basic model?
K. What general conclusions can be drawn? In what ways does this article contribute to (or extend) our
understanding of marketing science in the substantive area(s) examined by the article?
16