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

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.

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Studying Consideration Effects in Empirical Choice Models Using Scanner Panel Data

Journal of Marketing Research ◽

10.1177/002224379503200105 ◽

1995 ◽

Vol 32 (1) ◽

pp. 30-41 ◽

Cited By ~ 57

Author(s):

Rick L. Andrews ◽

T.C. Srinivasan

Keyword(s):

Panel Data ◽

Choice Models ◽

Scanner Panel Data ◽

Scanner Panel

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Nonstationary Conditional Trend Analysis: An Application to Scanner Panel Data

Journal of Marketing Research ◽

10.1177/002224379303000302 ◽

1993 ◽

Vol 30 (3) ◽

pp. 288-304 ◽

Cited By ~ 2

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.

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

Journal of Marketing Research ◽

10.1177/002224379403100111 ◽

1994 ◽

Vol 31 (1) ◽

pp. 128-136 ◽

Cited By ~ 74

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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