2004
DOI: 10.1287/mksc.1030.0028
|View full text |Cite
|
Sign up to set email alerts
|

Consumer Learning and Brand Valuation: An Application on Over-the-Counter Drugs

Abstract: We develop a brand choice model with learning based on the Kalman filter methodology. The model enables us to separate the effects of contemporaneous marketing promotions from the impact of the perceived quality valuation accrued through product usage over time. We also account for idiosyncratic consumer learning and preferences. The results point to the presence of heterogeneity in the valuation carryover coefficients across consumers and brands. In contrast to our expectations, a higher price is not importan… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

2
26
1
1

Year Published

2006
2006
2020
2020

Publication Types

Select...
8
2

Relationship

0
10

Authors

Journals

citations
Cited by 61 publications
(30 citation statements)
references
References 19 publications
(13 reference statements)
2
26
1
1
Order By: Relevance
“…In order to obtain estimates of this unobserved brand equity for each period, we use the Kalman filter algorithm, which has been used extensively in control engineering and has been recently applied in the marketing literature (for example, Xie, Sirbu, and Wang 1997;Putsis 1998;Naik, Mantrala, and Sawyer 1998;Akcura, Gonul, and Petrova 2004). The Kalman filter is a recursive algorithm that is used to obtain efficient estimates of an unobserved state variable (which happens to be brand equity in our case) in each period based on the information observed in that period.…”
Section: Note That In Equation 6bmentioning
confidence: 99%
“…In order to obtain estimates of this unobserved brand equity for each period, we use the Kalman filter algorithm, which has been used extensively in control engineering and has been recently applied in the marketing literature (for example, Xie, Sirbu, and Wang 1997;Putsis 1998;Naik, Mantrala, and Sawyer 1998;Akcura, Gonul, and Petrova 2004). The Kalman filter is a recursive algorithm that is used to obtain efficient estimates of an unobserved state variable (which happens to be brand equity in our case) in each period based on the information observed in that period.…”
Section: Note That In Equation 6bmentioning
confidence: 99%
“…Other papers that have modeled dynamics using the Kalman filter includeNaik et al (1998),Akcura et al (2004), andNaik et al (2005).2 Although the presence of holidays may not be brand specific, we use the brand subscript for the environmental factors for the sake of generalizability.Downloaded from informs.org by [129.115.103.99] on 23 August 2015, at 22:01 . For personal use only, all rights reserved.…”
mentioning
confidence: 99%
“…Here 01   , and  is the carryover coefficient, which is assumed to be identical across varieties (Akcura et al, 2004). (3) ij  are coefficients.…”
Section: The Dynamic Multiple-variety Choice (Dmc) Modelmentioning
confidence: 99%