IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v1y2003i4p425-465.html
   My bibliography  Save this article

Leveraging Information Across Categories

Author

Listed:
  • Raghuram Iyengar
  • Asim Ansari
  • Sunil Gupta

Abstract

Companies are collecting increasing amounts of information about their customers. This effort is based on the assumption that more information is better and that this information can be leveraged to predict customers' behavior in a variety of situations and product categories. For example, information about a customer's purchase behavior in one category can be helpful in predicting his potential behavior in a related category, which in turn could help a firm in its cross-selling efforts. In this paper, we present a model to better understand and predict a consumer's purchases and preferences when we may have limited or no information about him in one or more product categories. Conceptually this involves leveraging information from purchases of other consumers in multiple categories as well as partial information (e.g., purchase in one of the categories) of the target consumer. Our approach builds on the pioneering work of (Rossi et al. (1996)) who demonstrate the value of purchase information in the context of a single product category. We present results from an extensive simulation as well as an application on scanner panel data. Our simulation shows many interesting and somewhat surprising results. Specifically, we find that compared to a single-category analysis, a cross-category analysis does not lead to any significant improvement in data likelihood in most cases. Therefore, the single-category analysis of (Rossi et al. (1996)) is even more powerful than previously thought. However, we also find that a cross-category analysis does improve parameter recovery in many situations as compared to a single-category analysis. It is in these conditions that retailers can use cross-category information to better implement micro marketing programs. We demonstrate the transfer of information across categories in an application of two grocery products—Breakfast Foods and Table Syrup. In spite of a reasonable correlation (0.21) in the price parameter across these two categories, our simulation guidelines predict very little benefit of cross-category analysis over single-category analysis. Our empirical results confirm this prediction. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Raghuram Iyengar & Asim Ansari & Sunil Gupta, 2003. "Leveraging Information Across Categories," Quantitative Marketing and Economics (QME), Springer, vol. 1(4), pages 425-465, December.
    • Handle: RePEc:kap:qmktec:v:1:y:2003:i:4:p:425-465
    DOI: 10.1023/B:QMEC.0000004845.25649.6c
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/B:QMEC.0000004845.25649.6c
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/B:QMEC.0000004845.25649.6c?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    2. Kasulis, Jack J & Lusch, Robert F & Stafford, Edward F, Jr, 1979. "Consumer Acquisition Patterns for Durable Goods," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 6(1), pages 47-57, June.
    3. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    4. 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.
    5. Birger Wernerfelt, 1988. "Umbrella Branding as a Signal of New Product Quality: An Example of Signalling by Posting a Bond," RAND Journal of Economics, The RAND Corporation, vol. 19(3), pages 458-466, Autumn.
    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. P. Seetharaman & Siddhartha Chib & Andrew Ainslie & Peter Boatwright & Tat Chan & Sachin Gupta & Nitin Mehta & Vithala Rao & Andrei Strijnev, 2005. "Models of Multi-Category Choice Behavior," Marketing Letters, Springer, vol. 16(3), pages 239-254, December.
    2. Rakesh Niraj & V. Padmanabhan & P. B. Seetharaman, 2008. "Research Note—A Cross-Category Model of Households' Incidence and Quantity Decisions," Marketing Science, INFORMS, vol. 27(2), pages 225-235, 03-04.
    3. Nitin Mehta, 2007. "Investigating Consumers' Purchase Incidence and Brand Choice Decisions Across Multiple Product Categories: A Theoretical and Empirical Analysis," Marketing Science, INFORMS, vol. 26(2), pages 196-217, 03-04.
    4. Clement, Jesper & Aastrup, Jesper & Charlotte Forsberg, Signe, 2015. "Decisive visual saliency and consumers׳ in-store decisions," Journal of Retailing and Consumer Services, Elsevier, vol. 22(C), pages 187-194.
    5. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    6. Sri Devi Duvvuri & Asim Ansari & Sunil Gupta, 2007. "Consumers' Price Sensitivities Across Complementary Categories," Management Science, INFORMS, vol. 53(12), pages 1933-1945, December.
    7. Jang, Sungha & Prasad, Ashutosh & Ratchford, Brian T., 2016. "Consumer spending patterns across firms and categories: Application to the size- and share-of-wallet," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 123-139.
    8. Ma, Yu & Seetharaman, P.B. & Narasimhan, Chakravarthi, 2012. "Modeling Dependencies in Brand Choice Outcomes Across Complementary Categories," Journal of Retailing, Elsevier, vol. 88(1), pages 47-62.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    2. Timothy Gilbride & Sha Yang & Greg Allenby, 2005. "Modeling Simultaneity in Survey Data," Quantitative Marketing and Economics (QME), Springer, vol. 3(4), pages 311-335, December.
    3. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    4. Troske, Kenneth R. & Voicu, Alexandru, 2010. "Joint estimation of sequential labor force participation and fertility decisions using Markov chain Monte Carlo techniques," Labour Economics, Elsevier, vol. 17(1), pages 150-169, January.
    5. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    6. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    7. Siddhartha Chib & Edward Greenberg & Yuxin Chen, 1998. "MCMC Methods for Fitting and Comparing Multinomial Response Models," Econometrics 9802001, University Library of Munich, Germany, revised 06 May 1998.
    8. Susan Athey & Guido W. Imbens, 2007. "Discrete Choice Models With Multiple Unobserved Choice Characteristics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1159-1192, November.
    9. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    10. Sanjog Misra, 2005. "Generalized Reverse Discrete Choice Models," Quantitative Marketing and Economics (QME), Springer, vol. 3(2), pages 175-200, June.
    11. Lynd Bacon & Peter Lenk, 2012. "Augmenting discrete-choice data to identify common preference scales for inter-subject analyses," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 453-474, December.
    12. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    13. González-Benito, Óscar, 2004. "Random effects choice models: seeking latent predisposition segments in the context of retail store format selection," Omega, Elsevier, vol. 32(2), pages 167-177, April.
    14. Ricardo A. Daziano & Martin Achtnicht, 2014. "Forecasting Adoption of Ultra-Low-Emission Vehicles Using Bayes Estimates of a Multinomial Probit Model and the GHK Simulator," Transportation Science, INFORMS, vol. 48(4), pages 671-683, November.
    15. Keane, Michael P. & Wasi, Nada, 2016. "How to model consumer heterogeneity? Lessons from three case studies on SP and RP data," Research in Economics, Elsevier, vol. 70(2), pages 197-231.
    16. Troske, Kenneth & Voicu, Alexandru, 2009. "The Effect of Children on the Level of Labor Market Involvement of Married Women: What is the Role of Education?," IZA Discussion Papers 4074, Institute of Labor Economics (IZA).
    17. Tatiana Dyachenko & Rebecca Walker Reczek & Greg M. Allenby, 2014. "Models of Sequential Evaluation in Best-Worst Choice Tasks," Marketing Science, INFORMS, vol. 33(6), pages 828-848, November.
    18. Brynjolfsson, Erik & Dick, Astrid Andrea & Smith, Michael D., 2004. "Search and Product Differentiation at an Internet Shopbot," Working papers 4441-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    19. Lamoureux, Christopher G. & Nejadmalayeri, Ali, 2015. "Costs of capital and public issuance choice," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 27-45.
    20. Chintagunta, Pradeep & Kyriazidou, Ekaterini & Perktold, Josef, 2001. "Panel data analysis of household brand choices," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 111-153, July.

    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:kap:qmktec:v:1:y:2003:i:4:p:425-465. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.