Making the Cut: Modeling and Analyzing Choice Set Restriction in Scanner Panel Data

1995 ◽  
Vol 32 (3) ◽  
pp. 255-266 ◽  
Author(s):  
S. Siddarth ◽  
Randolph E. Bucklin ◽  
Donald G. Morrison
Keyword(s):  
Panel Data ◽  
Laundry Detergent ◽  
Multinomial Logit Model ◽  
Product Line ◽  
Secondary Source ◽  
Source Information ◽  
Choice Sets ◽  
Scanner Panel Data ◽  
Scanner Panel ◽  
Choice Set

The authors develop an approach to determine and analyze choice set restriction on the basis of secondary source information on consumer purchase histories. Individual-level choice sets are estimated using a Bayesian updating procedure in conjunction with the multinomial logit model. The authors apply the procedure to scanner panel data for the liquid laundry detergent category. An analysis of estimated choice sets across panelists reveals that market share does not “go hand-in-hand” with choice set share (the percentage of choice sets in which a brand is a member). Examining choice set membership patterns, such as the cooccurrence of brands in the same product line, also provides insight into sister-brand cannibalization. Estimation results also show that promotions can expand choice sets, providing excluded brands a means to gain entry and long-term sales benefits.

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.

Making the Cut: Modeling and Analyzing Choice Set Restriction in Scanner Panel Data

1995 ◽  
Vol 32 (3) ◽  
pp. 255 ◽  
Author(s):  
S. Siddarth ◽  
Randolph E. Bucklin ◽  
Donald G. Morrison
Keyword(s):  
Panel Data ◽  
Scanner Panel Data ◽  
Scanner Panel ◽  
Choice Set

A Relationship between Market Share Elasticities and Brand Switching Probabilities

1998 ◽  
Vol 35 (1) ◽  
pp. 99-113 ◽  
Author(s):  
Randolph E. Bucklin ◽  
Gary J. Russell ◽  
V. Srinivasan
Keyword(s):  
Market Share ◽  
Logit Model ◽  
Laundry Detergent ◽  
Brand Switching ◽  
Switching Matrix ◽  
Scanner Panel Data ◽  
Liquid Laundry Detergent ◽  
Heterogeneous Consumers ◽  
Scanner Panel ◽  
The Relationship

The authors derive a theoretical relationship between the aggregate market share elasticity matrix and the aggregate brand switching matrix on the basis of a logit model of heterogeneous consumers choosing among competing brands in a product class. Aggregate cross-elasticities are shown to be proportional (through a single scaling constant) to their corresponding aggregate row-conditional brand switching probabilities. Aggregate own-elasticities are shown to be proportional (through the negative of the same scaling constant) to one minus their corresponding aggregate row-conditional repeat purchase probabilities. An empirical analysis conducted on household scanner panel data in the liquid laundry detergent category shows that the theoretical correspondence holds as a very good approximation. An illustrative use of the relationship in estimating aggregate (store-level) models of market share indicates that the relationship helps improve predictive validity in a holdout period.

Brand Choice, Purchase Incidence, and Segmentation: An Integrated Modeling Approach

1992 ◽  
Vol 29 (2) ◽  
pp. 201-215 ◽  
Author(s):  
Randolph E. Bucklin ◽  
Sunil Gupta
Keyword(s):  
Market Segmentation ◽  
Laundry Detergent ◽  
Brand Choice ◽  
Purchase Behavior ◽  
Brand Preference ◽  
Purchase Incidence ◽  
Scanner Panel Data ◽  
Liquid Laundry Detergent ◽  
Marketing Variables ◽  
Scanner Panel

The authors develop an approach to market segmentation based on consumer response to marketing variables in both brand choice and category purchase incidence. The approach reveals segmentation as well as the nature of choice and incidence response for each segment. Brand choice and purchase incidence decisions are modeled at the segment level with the disaggregate multinomial logit and nested logit models; segment sizes are estimated simultaneously with the choice and incidence probabilities. Households are assigned to segments by using their posterior probabilities of segment membership based on their purchase histories. The procedure thereby permits an analysis of the demographic, purchase behavior, and brand preference characteristics of each response segment. The authors illustrate their approach with scanner panel data on the liquid laundry detergent category and find segmentation in price and promotion sensitivity for both brand choice and category purchase incidence. The results suggest that many households that switch brands on the basis of price and promotion do not also accelerate their category purchases and that households that accelerate purchases do not necessarily switch brands.

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.

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.

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