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Inertia and Variety Seeking in a Model of Brand-Purchase Timing


  • Pradeep K. Chintagunta

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)


Previous research on state dependence indicates that a brand's purchase probabilities vary over time and depend on the levels of inertia and variety seeking and on the identity of the previously purchased brand. Brand-choice probabilities obtained from models such as the logit and the probit are, however, fixed over time, conditional on the previous brand purchased and on the levels of marketing variables. Consequently, state dependence has largely been studied as a time-invariant phenomenon in brand-choice models, with the levels of inertia and variety seeking assumed to be constant over time. To account for the time-varying nature of state dependence would require a model in which brand-switching probabilities depend upon interpurchase times. One modeling framework that can account for this dependence is based on the hazard function approach. The proposed approach works as follows. All other factors being equal, an inertial household purchasing a brand on a particular occasion is most likely to repurchase that brand on the next occasion. If the household switches, it will be to a brand located perceptually close, in attribute space, to the previously purchased brand. In other words, an inertial household has the highest switching hazard for the same origin and destination brands, with a progressively lower hazard rate for brands perceptually located farther and farther away from the origin brand. The amount by which the hazard is lowered depends upon the perceptual distance and the inertia level of the household. On the other hand, if the household is variety seeking, the most likely brand purchased would be a brand located farthest away from the previously purchased brand in attribute space. In other words, the hazard rate of repurchase is the lowest, with the rate increasing with the distance of the destination brand from the origin brand and the level of that household's variety-seeking tendency. The effects of inertia and variety seeking are, therefore, incorporated at the attribute level into a brand-purchase timing model. In doing so, we attempt to provide greater insight into the nature of state dependence in models of purchase timing. Our model and estimation procedure will enable us to distinguish between households that are inertial and those that are variety prone. In addition to accounting for state dependence, the model also accounts for the effects of unobserved heterogeneity among households in their brand preferences and in their sensitivities to marketing activities. A majority of studies in marketing using the hazard function approach to investigate purchase timing have not accounted for heterogeneity in marketing-mix effects. The study integrates recent methods that incorporate the effects of inertia and variety seeking in brand-choice models with a semi-Markov model of purchase timing and brand switching. The proposed model enables us to (1) infer market structure via a perceptual map for the sample households, and (2) investigate implications for the introduction of a line extension. We provide empirical applications of the proposed method using three different household-level scanner panel data sets. We find that differing levels of inertia and variety seeking characterize the three data sets. The findings are consistent with prior beliefs regarding these categories. In addition, our results indicate that the nature of interbrand purchase timing behavior depends upon the extent of inertia or variety seeking in the data. We are also able to characterize the structure of the three product markets studied. This provides implications for interbrand rivalry in the market. Further, we demonstrate how the model and results can be used to predict the location of a line extension in the perceptual space of households. Finally, we obtain implications for the timing of brand promotions.

Suggested Citation

  • Pradeep K. Chintagunta, 1998. "Inertia and Variety Seeking in a Model of Brand-Purchase Timing," Marketing Science, INFORMS, vol. 17(3), pages 253-270.
  • Handle: RePEc:inm:ormksc:v:17:y:1998:i:3:p:253-270

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    References listed on IDEAS

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    Cited by:

    1. Urmee Khan & Maxwell Stinchcombe, 2012. "The Virtues of Hesitation," Working Papers 201425, University of California at Riverside, Department of Economics, revised Sep 2014.
    2. Nobuhiko Terui & Shohei Hasegawa & Greg M. Allenby, 2015. "A Threshold Model for Discontinuous Preference Change and Satiation," TMARG Discussion Papers 122, Graduate School of Economics and Management, Tohoku University.
    3. Meade, Nigel & Islam, Towhidul, 2010. "Using copulas to model repeat purchase behaviour - An exploratory analysis via a case study," European Journal of Operational Research, Elsevier, vol. 200(3), pages 908-917, February.
    4. S. Sajeesh & Jagmohan S. Raju, 2010. "Positioning and Pricing in a Variety Seeking Market," Management Science, INFORMS, vol. 56(6), pages 949-961, June.
    5. Zakaria Babutsidze & Robin Cowan, 2014. "Showing or telling? Local interaction and organization of behavior," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(2), pages 151-181, October.
    6. Arnd Huchzermeier & Ananth Iyer & Julia Freiheit, 2002. "The Supply Chain Impact of Smart Customers in a Promotional Environment," Manufacturing & Service Operations Management, INFORMS, vol. 4(3), pages 228-240, November.
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    8. Gábor Kézdi & Gergely Csorba, 2013. "Estimating Consumer Lock-In Effects from Firm-Level Data," Journal of Industry, Competition and Trade, Springer, vol. 13(3), pages 431-452, September.
    9. Laura Lucia-Palacios & Raúl Pérez-López & Yolanda Polo-Redondo, 2016. "Enemies of cloud services usage: inertia and switching costs," Service Business, Springer;Pan-Pacific Business Association, vol. 10(2), pages 447-467, June.
    10. Elaine Zanutto & Eric Bradlow, 2006. "Data pruning in consumer choice models," Quantitative Marketing and Economics (QME), Springer, vol. 4(3), pages 267-287, September.
    11. Greg M. Allenby & Thomas S. Shively & Sha Yang & Mark J. Garratt, 2004. "A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts," Marketing Science, INFORMS, vol. 23(1), pages 95-108, June.
    12. Michael Braun & David A. Schweidel, 2011. "Modeling Customer Lifetimes with Multiple Causes of Churn," Marketing Science, INFORMS, vol. 30(5), pages 881-902, September.
    13. Steven M. Shugan, 2006. "Editorial: Errors in the Variables, Unobserved Heterogeneity, and Other Ways of Hiding Statistical Error," Marketing Science, INFORMS, vol. 25(3), pages 203-216, 05-06.
    14. Marko Sarstedt & Sebastian Scharf & Alexander Thamm & Michael Wolff, 2010. "Die Prognose von Serviceintervallen mit der Hazard-Raten-Analyse – Ergebnisse einer empirischen Studie im Automobilmarkt," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 20(3), pages 269-283, April.
    15. Karsten Hansen & Vishal Singh, 2009. "Market Structure Across Retail Formats," Marketing Science, INFORMS, vol. 28(4), pages 656-673, 07-08.
    16. Felipe Caro & Victor Martínez-de-Albéniz, 2012. "Product and Price Competition with Satiation Effects," Management Science, INFORMS, vol. 58(7), pages 1357-1373, July.
    17. Richards, Timothy J., 2004. "Price and Product-Line Rivalry Among Supermarket Retailers," Working Papers 28535, Arizona State University, Morrison School of Agribusiness and Resource Management.
    18. K. Sudhir & Nathan Yang, 2014. "Exploiting the Choice-Consumption Mismatch: A New Approach to Disentangle State Dependence and Heterogeneity," Cowles Foundation Discussion Papers 1941, Cowles Foundation for Research in Economics, Yale University.
    19. P. B. Seetharaman, 2004. "The Additive Risk Model for Purchase Timing," Marketing Science, INFORMS, vol. 23(2), pages 234-242, March.
    20. Kai Kopperschmidt & Winfried Stute, 2009. "Purchase timing models in marketing: a review," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 93(2), pages 123-149, June.
    21. repec:eee:ijrema:v:28:y:2011:i:4:p:352-366 is not listed on IDEAS
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    24. Martijn G. de Jong & Donald R. Lehmann & Oded Netzer, 2012. "State-Dependence Effects in Surveys," Marketing Science, INFORMS, vol. 31(5), pages 838-854, September.

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