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ABSTRACT: A large literature demonstrates the empirical importance of internal reference price effects. There are several theories regarding
how and why these effects arise. We offer a simple test that distinguishes between the two leading theories based on economically
rational behavior: price as a signal of quality and price as a predictor of future prices. Our test builds on differences
in how past consumer purchases interact with internal reference prices. We first validate the reliability of our test by applying
it to synthetic data. We then apply our test to purchases of ketchup and diapers and find: (1) quality signaling is the dominant
mechanism behind reference price effects in both categories; (2) consistent with the quality-signaling theory, reference price
effects diminish as various measures of consumer experience increase; but (3) in both categories there are many individuals
for whom price-prediction effects dominate quality-signaling effects.
KeywordsPrice expectations-Price as a signal of quality-Reference price-Brand choice
JEL classificationsC23-D12-D83-M31
Quantitative Marketing and Economics 04/2012; 8(3):303-332. · 1.50 Impact Factor
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ABSTRACT: This paper discusses the role of agents’ beliefs and their implications for the economic modeling of their behavior, in particular,
their behavior over time. The paper also discusses the corresponding planning problems facing both firms and consumers in
their current decision making. After a general discussion of the consumer and firm problem, we discuss recent examples of
some of the emerging empirical literature on dynamic choice behavior in marketing.
Marketing Letters 04/2012; 19(3):367-382. · 0.63 Impact Factor
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ABSTRACT: We examine purchases of extended service contracts, which are essentially insurance products, for electronic products in a retail setting. The primary insurance purchase determinants are perceived probability of loss, extent of loss, risk aversion, and amount of insurance premium. We examine how product characteristics (hedonic/utilitarian, manufacturer's warranty) and retailer actions (promotions, feature advertising) influence the purchase of extended service contracts. We also investigate the impact of consumer characteristics (income, gender, and prior usage) on these insurance purchase determinants. To test the predictions, we use revealed preferences from panel data of electronic purchases across several product categories. (c) 2009 by JOURNAL OF CONSUMER RESEARCH, Inc..
Journal of Consumer Research. 01/2009; 36(4):611-623.
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Marketing Science. 01/2009; 28:740-758.
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Quantitative Marketing and Economics 02/2008; 6(2):139-176. · 1.50 Impact Factor
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Marketing Letters 02/2008; 19(3):367-382. · 0.63 Impact Factor
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Marketing Science. 01/2008; 27:1111-1125.
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Peter C. Verhoef,
Wagner Kamakura,
Carl Mela,
Asim Ansari,
Anand Bodapati,
Pete Fader,
Raghuram Iyengar,
Prasad Naik,
Scott Neslin, Baohong Sun,
Michel Wedel,
Ron Wilcox
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ABSTRACT: Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendors' needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm's products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplace A shift in focus in many organizations, towards increasing the share of requirements among their current customers rather than fighting for new customers. An explosion in data acquired about customers, through the integration of internal databases and acquisition of external syndicated data. Computing power is increasing exponentially. Software and tools are being developed to exploit these data and computers, bringing the analytical tools to the decision maker, rather than restricting their access to analysts. In spite of this growth in marketing practice, CRM research in academia remains nascent. This paper provides a framework for CRM research and describes recent advances as well as key research opportunities. See http://faculty.fuqua.duke.edu/~mela for a more complete version of this paper Copyright Springer Science + Business Media, Inc. 2005
Marketing Letters 02/2005; 16(3):279-291. · 0.63 Impact Factor
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ABSTRACT: Logit choice models have been used extensively to study consumer choice behavior and promotion response. A common finding is that promotion has a strong effect on brand choice. This paper examines whether brand switching elasticities derived from these models may be over-estimated due to rational consumer adjustment of purchase timing to coincide with promotion schedules, and whether this bias can be addressed by a dynamic structural model. We develop a dynamic structural model of choice/incidence that traces the process by which consumers make optimal buying decisions. We then conduct three analyses. First is a simulation based on synthetic data. We show that if the structural model is correct, brand switching elasticities are over-estimated by stand-alone logit choice models. Adding a nested logit model of choice and incidence improves the estimates, but not completely. Second we estimate these models on real data. The results indicate that the structural model fits better and produces sensible coefficient estimates. We then observe the same pattern in choice elasticities as we did with the simulation. Third, we predict sales for a major change in promotion policy - a 50% increase in promotion frequency. The reduced form models predict much higher sales levels than the dynamic structural model. This is a prime illustration of the Lucas Critique. Our overall conclusion is that reduced form model estimates of brand switching elasticities can be overstated, and that a dynamic structural model is best for addressing the problem. Reduced form models that try to capture the same phenomena as the dynamic model, especially if they model incidence, can partially, although not completely, address the issue. We discuss the implications of these findings for researchers and managers.
Tuck School of Business at Dartmouth Research Paper Series. 05/2002;
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ABSTRACT: This article applies different approaches to distinguish state dependence from unobserved heterogeneity and serial correlation and, hence, test for state dependence in consumer brand choices. First, we apply a simple method proposed by Chamberlain, which involves lagged exogenous variables only. Second, we also estimate a lagged-dependent-variable specification proposed by Wooldridge. Third, we use the estimation approach suggested by Wooldridge to estimate a model with both lagged dependent and exogenous variables to distinguish between the two different sources of choice dynamics, state dependence and lagged effects of the exogenous variables. Our analysis reveals that the best approach is to use models with both lagged dependent and exogenous variables. Our findings include strong evidence for state dependence in five out of the six product categories studied in this article.
Journal of Business and Economic Statistics 02/2001; 19(2):142-52. · 1.78 Impact Factor
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ABSTRACT: Discrete choice models have been widely estimated on scanner panel data to study consumer choice. One challenge in scanner panel research is that only the prices of the items bought are recorded. The ad hoc models used to fill in the missing prices of non-purchased brands may create a self-selection bias in estimating consumer price sensitivities. This type of bias is also present in existing studies of coupon effects. To obtain consistent estimates of price elasticities in the presence of missing price and coupon values, we estimate a brand choice model jointly with models for the price and coupon processes.
Journal of Econometrics. 02/1998;
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Wagner A. Kamakura,
Carl F. Mela,
Asim Ansari,
Anand Bodapati,
Peter S. Fader,
Raghuram Iyengar,
Prasad Naik,
Scott Neslin, Baohong Sun,
Peter C. Verhoef,
Michel Wedel,
Ron Wilcox
[show abstract]
[hide abstract]
ABSTRACT: Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendors' needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm's products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplace Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/47023/1/11002_2005_Article_5892.pdf
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