IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v29y2010i4p585-601.html
   My bibliography  Save this article

Demand Estimation with Social Interactions and the Implications for Targeted Marketing

Author

Listed:
  • Wesley R. Hartmann

    () (Stanford Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

This paper develops a model for the estimation and analysis of demand in the context of social interactions. Decisions made by a group of customers are modeled to be an equilibrium outcome of an empirical discrete game, such that all group members must be satisfied with chosen outcomes. The game-theoretic approach assists estimation by allowing us to account for the endogeneity of group members' decisions while also serving as a managerial tool that can simulate equilibrium outcomes for the group when the firm alters the marketing mix to the group. The model builds upon the existing literature on empirical models of discrete games by introducing a random coefficients heterogeneity distribution. Monte Carlo simulations reveal that including the heterogeneity resolves the endogenous group formation bias commonly noted in the social interactions literature. By estimating the heterogeneous equilibrium model using Bayesian hierarchical Markov chain Monte Carlo, we can also recover some parameters at the individual level to evaluate group-specific characteristics and targeted marketing strategies. To validate the model and illustrate its implications, we apply it to a data set of groups of golfers. We find significant social interaction effects, such that 65% of the median customer value is attributable to the customer and the other 35% is attributable to the customer's affect on members of his group. We also consider targeted marketing strategies and show that group-level targeting increases profit by 1%, whereas targeting within groups can increase profitability by 20%. We recognize that customer backlashes to targeting could be greater when group members receive different offers, so we suggest some alternatives that could retain some of the profitability of within group targeting while avoiding customer backlashes.

Suggested Citation

  • Wesley R. Hartmann, 2010. "Demand Estimation with Social Interactions and the Implications for Targeted Marketing," Marketing Science, INFORMS, vol. 29(4), pages 585-601, 07-08.
  • Handle: RePEc:inm:ormksc:v:29:y:2010:i:4:p:585-601
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1100.0559
    Download Restriction: no

    References listed on IDEAS

    as
    1. Peter Kooreman & Adriaan R. Soetevent, 2007. "A discrete-choice model with social interactions: with an application to high school teen behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 599-624.
    2. Ernst Fehr & Klaus M. Schmidt, 1999. "A Theory of Fairness, Competition, and Cooperation," The Quarterly Journal of Economics, Oxford University Press, vol. 114(3), pages 817-868.
    3. Sha Yang & Vishal Narayan & Henry Assael, 2006. "Estimating the Interdependence of Television Program Viewership Between Spouses: A Bayesian Simultaneous Equation Model," Marketing Science, INFORMS, vol. 25(4), pages 336-349, July.
    4. Che‐Lin Su & Kenneth L. Judd, 2012. "Constrained Optimization Approaches to Estimation of Structural Models," Econometrica, Econometric Society, vol. 80(5), pages 2213-2230, September.
    5. William A. Brock & Steven N. Durlauf, 2001. "Discrete Choice with Social Interactions," Review of Economic Studies, Oxford University Press, vol. 68(2), pages 235-260.
    6. Berry, Steven T, 1992. "Estimation of a Model of Entry in the Airline Industry," Econometrica, Econometric Society, vol. 60(4), pages 889-917, July.
    7. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    8. Jean-Pierre H. Dubé & Günter J. Hitsch & Pradeep K. Chintagunta, 2010. "Tipping and Concentration in Markets with Indirect Network Effects," Marketing Science, INFORMS, vol. 29(2), pages 216-249, 03-04.
    9. Daniel A. Ackerberg & Gautam Gowrisankaran, 2006. "Quantifying equilibrium network externalities in the ACH banking industry," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 738-761, September.
    10. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    11. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
    12. repec:rje:randje:v:37:y:2006:3:p:738-761 is not listed on IDEAS
    13. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    14. Wesley Hartmann, 2006. "Intertemporal effects of consumption and their implications for demand elasticity estimates," Quantitative Marketing and Economics (QME), Springer, vol. 4(4), pages 325-349, December.
    15. van den Bulte, C. & Stremersch, S., 2003. "Contagion and heterogeneity in new product diffusion: An emperical test," ERIM Report Series Research in Management ERS-2003-077-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    16. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
    17. Bresnahan, Timothy F. & Reiss, Peter C., 1991. "Empirical models of discrete games," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 57-81.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paul B. Ellickson & Sanjog Misra, 2011. "Structural Workshop Paper --Estimating Discrete Games," Marketing Science, INFORMS, vol. 30(6), pages 997-1010, November.
    2. repec:eee:joinma:v:27:y:2013:i:4:p:281-298 is not listed on IDEAS
    3. Hai Che & Tülin Erdem & T. Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    4. Sridhar Narayanan, 2013. "Bayesian estimation of discrete games of complete information," Quantitative Marketing and Economics (QME), Springer, vol. 11(1), pages 39-81, March.
    5. Anton Badev, 2014. "Discrete Games in Endogenous Networks: Theory and Policy," 2014 Meeting Papers 901, Society for Economic Dynamics.
    6. Paul Ellickson & Sanjog Misra, 2012. "Enriching interactions: Incorporating outcome data into static discrete games," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 1-26, March.
    7. Dae-Yong Ahn & Jason A. Duan & Carl F. Mela, 2011. "An Equilibrium Model of User Generated Content," Working Papers 11-13, NET Institute, revised Dec 2011.
    8. repec:kap:qmktec:v:11:y:2013:i:2:d:10.1007_s11129-012-9130-y is not listed on IDEAS
    9. Joseph Pancras & Xia Wang & Dipak K. Dey, 2016. "Investigating the impact of customer stochasticity on firm price discrimination strategies using a new Bayesian mixture scale heterogeneity model," Marketing Letters, Springer, vol. 27(3), pages 537-552, September.
    10. Neeraj Arora & Ty Henderson & Qing Liu, 2011. "Noncompensatory Dyadic Choices," Marketing Science, INFORMS, vol. 30(6), pages 1028-1047, November.
    11. Subramanian Balachander & Bikram Ghosh, 2013. "Bayesian estimation of a simultaneous probit model using error augmentation: An application to multi-buying and churning behavior," Quantitative Marketing and Economics (QME), Springer, vol. 11(4), pages 437-458, December.
    12. A. Orhun, 2013. "Spatial differentiation in the supermarket industry: The role of common information," Quantitative Marketing and Economics (QME), Springer, vol. 11(1), pages 3-37, March.
    13. Vishal Narayan & Vithala R. Rao & Carolyne Saunders, 2011. "How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism," Marketing Science, INFORMS, vol. 30(2), pages 368-384, 03-04.
    14. Jeremy T. Fox & Natalia Lazzati, 2012. "Identification of Potential Games and Demand Models for Bundles," NBER Working Papers 18155, National Bureau of Economic Research, Inc.
    15. Ching-I Huang, 2013. "Intra-household effects on demand for telephone service: Empirical evidence," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 231-261, June.
    16. Adam D. Rennhoff & Mark F. Owens, 2012. "Competition and the Strategic Choices of Churches," American Economic Journal: Microeconomics, American Economic Association, vol. 4(3), pages 152-170, August.
    17. repec:eee:ijrema:v:30:y:2013:i:3:p:236-248 is not listed on IDEAS
    18. Praveen K. Kopalle & Yacheng Sun & Scott A. Neslin & Baohong Sun & Vanitha Swaminathan, 2012. "The Joint Sales Impact of Frequency Reward and Customer Tier Components of Loyalty Programs," Marketing Science, INFORMS, vol. 31(2), pages 216-235, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:29:y:2010:i:4:p:585-601. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc). General contact details of provider: http://edirc.repec.org/data/inforea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.