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- W2297388654 abstract "The forces that influence a consumer in brandchoice: preferences, experiences, and marketing mix have been ofgreat interest to marketing scientists. The vast literature basedon static survey data points to consumers using multi-attributeutilities in making a choice. At the same time the vast literatureof scanner panel data based studies points to the existence ofstate dependence (in various functional forms) in brand choices infrequently purchased product markets. Using a unique dataset thatcontains stated preferences (survey) and actual purchase data(scanner panel) for the same group of consumers I attempt tountangle the effects of preference heterogeneity and statedependence, and to determine the exact nature of the latter. Ipropose a hierarchical model in which consumers within the sameproduct category are heterogeneous in the order of the brand choiceprocess and its parameters as well as in their preferences andresponsiveness to marketing mix. The proposed model is designed toencompass side by side three different types of consumer experiencebased behavior: zero-order, inertia and learning. Two sources ofstate dependence, inertia and learning, are operationalized using adummy for lagged choice and adopting the Bayesian learning process,respectively. I apply a Reversible Jump MCMC sampling scheme tosample across component processes and a Metropolis-Hastings/Gibbsstep within each component process. My results are striking andsuggest that all three processes exist but that the extent of statedependence is spuriously overestimated in the absence of preferenceinformation. Both the inertia and in particular the Bayesianlearning components are overstated, and the nature of learningsignificantly changes. A substantial segment of consumers who donot exhibit state dependence is uncovered and its size increaseswhen preferences are available. Using individual-level outcomes Iillustrate why the lack of preference information leads to faultyinferences. Furthermore, I find that sensitivity to marketing mixvariables is biased. I also conduct various counterfactualsimulations to assess the managerial implications of myfindings." @default.
- W2297388654 created "2016-06-24" @default.
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- W2297388654 date "2008-01-01" @default.
- W2297388654 modified "2023-09-27" @default.
- W2297388654 title "Disentangling preferences, inertia and learning in brand choice models" @default.
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