Managing Variety on the Retail Shelf: Using Household Scanner Panel Data to Rationalize Assortments

2008 ◽  
pp. 155-182 ◽  
Author(s):  
Ravi Anupindi ◽  
Sachin Gupta ◽  
M.A. Venkataramanan
Keyword(s):  
Panel Data ◽  
Scanner Panel Data ◽  
Scanner Panel

Studying Consideration Effects in Empirical Choice Models Using Scanner Panel Data

1995 ◽  
Vol 32 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Rick L. Andrews ◽  
T.C. Srinivasan
Keyword(s):  
Panel Data ◽  
Choice Models ◽  
Scanner Panel Data ◽  
Scanner Panel

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.

Targeted promotions using scanner panel data

1997 ◽  
Vol 6 (6) ◽  
pp. 405-416 ◽  
Author(s):  
Amit K. Ghosh
Keyword(s):  
Panel Data ◽  
Scanner Panel Data ◽  
Scanner Panel

Price customization using price thresholds estimated from scanner panel data

2006 ◽  
Vol 20 (3-4) ◽  
pp. 58-70 ◽  
Author(s):  
Nobuhiko Terui ◽  
Wirawan Dony Dahana
Keyword(s):  
Panel Data ◽  
Scanner Panel Data ◽  
Scanner Panel ◽  
Price Thresholds

MDS Maps for Product Attributes and Market Response: An Application to Scanner Panel Data

1999 ◽  
Vol 18 (4) ◽  
pp. 584-604 ◽  
Author(s):  
Rick L. Andrews ◽  
Ajay K. Manrai
Keyword(s):  
Panel Data ◽  
Market Response ◽  
Product Attributes ◽  
Scanner Panel Data ◽  
Scanner Panel

Identifying purchase-history sensitive shopper segments using scanner panel data and sequence alignment methods

2003 ◽  
Vol 10 (3) ◽  
pp. 135-144 ◽  
Author(s):  
Chang-Hyeon Joh ◽  
Harry J.P Timmermans ◽  
Peter T.L Popkowski-Leszczyc
Keyword(s):  
Panel Data ◽  
Sequence Alignment ◽  
Scanner Panel Data ◽  
Scanner Panel

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.

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