On Using Demographic Variables to Determine Segment Membership in Logit Mixture Models

1994 ◽  
Vol 31 (1) ◽  
pp. 128-136 ◽  
Author(s):  
Sachin Gupta ◽  
Pradeep K. Chintagunta
Keyword(s):  
Panel Data ◽  
Mixture Models ◽  
Mixture Model ◽  
Demographic Variables ◽  
Demographic Characteristics ◽  
Empirical Application ◽  
Scanner Panel Data ◽  
Scanner Panel ◽  
Specific Profile ◽  
Brand Preferences

The authors propose an extension of the logit-mixture model that defines prior segment membership probabilities as functions of concomitant (demographic) variables. Using this approach it is possible to describe how membership in each of the segments, segments being characterized by a specific profile of brand preferences and marketing variable sensitivities, is related to household demographic characteristics. An empirical application of the methodology is provided using A.C. Nielsen scanner panel data on catsup. The authors provide a comparison with the results obtained using the extant methodology in estimation and validation samples of households.

Nonstationary Conditional Trend Analysis: An Application to Scanner Panel Data

1993 ◽  
Vol 30 (3) ◽  
pp. 288-304 ◽  
Author(s):  
Peter J. Lenk ◽  
Ambar G. Rao ◽  
Vikas Tibrewala
Keyword(s):  
Panel Data ◽  
Trend Analysis ◽  
Negative Binomial Distribution ◽  
Negative Binomial ◽  
Model Misspecification ◽  
Base Period ◽  
Marketing Environment ◽  
Marketing Activities ◽  
Scanner Panel Data ◽  
Scanner Panel

Planning and evaluating consumer promotions is facilitated by knowledge of the types of consumers who contribute to incremental sales. In particular, interest may focus on identifying the contributions of buyers segmented on the basis of their prior purchase history. When the distribution of the number of purchase occasions in a base period can be described by the negative binomial distribution (NBD), conditional trend analysis (CTA) is a simple and effective approach for identifying the sources of incremental sales during a test (promotional) period. As currently implemented, CTA assumes a stationary marketing environment. The authors propose an extension of CTA that explicitly incorporates varying marketing activities. They also show that the often observed underprediction of purchases in the test period by nonbuyers in the base period is a consequence of the skewness of the NBD and is not necessarily due to model misspecification. An illustration with scanner panel data is provided.

Reciprocal Spillover Effects: A Strategic Benefit of Brand Extensions

2003 ◽  
Vol 67 (1) ◽  
pp. 4-13 ◽  
Author(s):  
Subramanian Balachander ◽  
Sanjoy Ghose
Keyword(s):  
Panel Data ◽  
Brand Choice ◽  
Brand Extension ◽  
Spillover Effects ◽  
Product Line ◽  
Brand Extensions ◽  
Parent Brand ◽  
Product Line Management ◽  
Scanner Panel Data ◽  
Scanner Panel

A commonly advanced rationale for the proliferation of brand extensions is companies’ motivation to leverage the equity in established brands, thereby developing profitable products relatively easily. A more interesting strategic argument for brand extensions that has been advanced is that extensions would favorably affect the image of the parent brand and thereby influence its choice. In this research, the authors investigate the existence of such reciprocal spillover effects emanating from the advertising of a brand extension. The authors use scanner panel data and study spillover effects of advertising on brand choice. They develop implications for brand and product line management.

Heterogeneous Logit Model Implications for Brand Positioning

1994 ◽  
Vol 31 (2) ◽  
pp. 304-311 ◽  
Author(s):  
Pradeep K. Chintagunta
Keyword(s):  
Panel Data ◽  
Logit Model ◽  
Choice Behavior ◽  
Brand Choice ◽  
Brand Positioning ◽  
Household Level ◽  
Proposed Model ◽  
Scanner Panel Data ◽  
Marketing Variables ◽  
Scanner Panel

The author discusses the implications of a heterogeneous logit model for brand positioning. The methodology presented is a restricted version of a mixture-of-logits model and obtains brand positions on a product-market map and the distribution of preferences across households while accounting for the effects of marketing variables on household brand choice behavior. The restriction involves imposing a factor structure on the covariance matrix of the distribution of intrinsic brand preferences. An empirical application of the methodology is presented using A.C. Nielsen household-level scanner panel data on the purchases of liquid laundry detergents. The results indicate that the proposed model provides a better fit to the data than the unrestricted mixture-of-logits model or the Choice Map methodology.

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