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A Framework for Investigating Habits, “The Hand of the Past,” and Heterogeneity in Dynamic Brand Choice


Author Info

  • Rishin Roy

    (Paribas Capital Markets)

  • Pradeep K. Chintagunta

    (The University of Chicago)

  • Sudeep Haldar

    (McKinsey & Company)


In this paper we develop a general class of dynamic brand choice models, called (LB) models, which are consistent with the theory of random utility maximization of consumer choice behavior. The underlying random utility process is Markov, and the inter-temporal evolution of the (utility-maximizing) brand choice process is also Markov. The models permit parsimonious parameterizations of the random utility process in brand choices with the resulting switching probabilities being functionally related to explanatory variables. The model allows for structural state dependence (feedback), habit persistence (inertia), and unobserved heterogeneity. The theoretical development shows that several well known stochastic brand choice models can be deduced from random utility maximization theory. From a managerial perspective, the usefulness of the proposed model stems from its ability to separate out the effects of habits, state dependence and heterogeneity. Strong state dependence effects imply incentives for inducing trial of a brand (e.g., product sampling). In contrast with state dependence, a strong habit persistence effect may be indicative of buyer behavior where inducement of trial of a different brand may not be sufficient to maintain a (sustained) defection of the consumer from the habitually purchased brand to the trial brand. Failure to distinguish between these two effects has important implications. For example, a model that only accounts for state dependence effects would, in the presence of only habit persistence, incorrectly attribute it to state dependence. Based on this the manager could decide to embark on an expensive sampling program that might prove ineffective due to the absence of state dependence. It is also important to distinguish between the effects of unobserved heterogeneity and state dependence. In the absence of true state dependence, failing to account for unobservable variations across households (such as differences in price sensitivities), results in the temporally persistent unobservable elements showing up as state dependence in the model. Hence, a manager may incorrectly opt for sampling as the appropriate marketing action, whereas, a couponing or price promotion strategy should have been preferred. We estimate the model parameters using the AC Nielsen household scanner panel data set on catsup purchases. Further, we investigate empirical techniques for overcoming the “initial conditions” problem that affects many dynamic models of brand choice. Through simulation analysis using the estimated parameters, we show that the calculated profitability of a promotion must take into account the multi-period impact due to state dependence. Further, we demonstrate how ignoring heterogeneity can result in spurious state dependence, thereby making marketing tactics such as product sampling appear far more attractive than they actually are. Specifically, the model that accounts only for habit persistence and state dependence effects predicts a share increase of 2.4% points through sampling for one of the brands in the empirical analysis. Once the effects of unobserved heterogeneity are accounted for, however, this number drops precipitously to 0.3%. While even this low number might fulfill the objectives of the brand manager, it ensures that expectations are not eight times that number. It is important to note that this paper is a first attempt at analyzing the three fundamental dimensions of dynamic brand choice behavior. Our formulation represents a “reduced form” approach as opposed to a “structural” approach. Other limitations of the model include our focus on time-invariant choice sets. Extensions of the model to situations where choice sets vary over time are possible, but are difficult. Another limitation is the dependence on multivariate extreme value distributions. An alternative would be to use multivariate normal distributions, i.e., a probit-like structure which has certain useful properties. A comparison of the two approaches would be useful. Empirical extensions of the model to allow for higher-order processes and nested logit type model structures could also be fruitful. Explicitly incorporating variety-seeking behavior in the model would further enrich the theoretical framework proposed in this paper.

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Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 15 (1996)
Issue (Month): 3 ()
Pages: 280-299

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Handle: RePEc:inm:ormksc:v:15:y:1996:i:3:p:280-299

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Related research

Keywords: brand choice; econometric models; estimation and other statistical techniques;


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Cited by:
  1. Mohamed Lachaab & Asim Ansari & Kamel Jedidi & Abdelwahed Trabelsi, 2006. "Modeling preference evolution in discrete choice models: A Bayesian state-space approach," Quantitative Marketing and Economics, Springer, vol. 4(1), pages 57-81, March.
  2. Drichoutis, Andreas & Lusk, Jayson, 2012. "Judging statistical models of individual decision making under risk using in- and out-of-sample criteria," MPRA Paper 38951, University Library of Munich, Germany.
  3. Sridhar Narayanan & Puneet Manchanda, 2012. "An empirical analysis of individual level casino gambling behavior," Quantitative Marketing and Economics, Springer, vol. 10(1), pages 27-62, March.
  4. José M. Labeaga & Mercedes Martos-Partal, 2007. "A Proposal to Distinguish State Dependence and Unobserved Heterogeneity in Binary Brand Choice Models," Working Papers 2007-02, FEDEA.
  5. Morten Ravn & Stephanie Schmitt-Grohe & Martin Uribe, 2008. "Incomplete Cost Pass-Through Under Deep Habits," Economics Working Papers ECO2008/06, European University Institute.
  6. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
  7. Nanarpuzha, Rajesh, . "Modeling Situational Factors in Variety Seeking Behaviour: An Extension of the Lightning Bolt Model," IIMA Working Papers WP2013-12-04, Indian Institute of Management Ahmedabad, Research and Publication Department.
  8. Itzhak Gilboa & Amit Pazgal, 1995. "History Dependent Brand Switching: Theory and Evidence," Discussion Papers 1146, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  9. Kiron Chatterjee, 2011. "Modelling the dynamics of bus use in a changing travel environment using panel data," Transportation, Springer, vol. 38(3), pages 487-509, May.
  10. Sergi Jiménez-Martín & Antonio Ladrón de Guevara-Martínez, 2009. "A state-dependent model of hybrid behavior with rational consumers in the attribute space," Investigaciones Economicas, Fundación SEPI, vol. 33(3), pages 347-383, September.
  11. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics, Springer, vol. 9(1), pages 25-70, March.
  12. Narayanan, Sridhar & Manchanda, Puneet, 2008. "An Empirical Analysis of Individual Level Casino Gambling Behavior," Research Papers 2003, Stanford University, Graduate School of Business.
  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. Richards, Timothy J., 2000. "The Impact Of Promotion And Advertising: A Latent Class Approach," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 32(03), December.


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