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.

Model of Brand Choice with a No-Purchase Option Calibrated to Scanner-Panel Data

2004 ◽  
Vol 41 (2) ◽  
pp. 184-196 ◽  
Author(s):  
Siddhartha Chib ◽  
P.B. Seetharaman ◽  
Andrei Strijnev
Keyword(s):  
Panel Data ◽  
Brand Choice ◽  
Scanner Panel Data ◽  
Scanner Panel

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.

The Effects of a Direct Mail Coupon on Brand Choice Behavior

1987 ◽  
Vol 24 (4) ◽  
pp. 370-376 ◽  
Author(s):  
Kapil Bawa ◽  
Robert W. Shoemaker
Keyword(s):  
Panel Data ◽  
Field Experiment ◽  
Choice Behavior ◽  
Prior Probability ◽  
Brand Choice ◽  
Direct Mail ◽  
The Past ◽  
Coupon Redemption ◽  
Scanner Panel Data ◽  
Scanner Panel

The authors examine the effects of a manufacturer coupon on brand choice behavior. The level of coupon redemption and changes in brand choice behavior after redemption are examined as a function of the household's prior probability of purchasing the promoted brand, likelihood of buying a favorite competitive brand, and coupon face value. A model of the coupon redemption decision is developed to predict response to the coupon promotion by different consumer segments. Predictions from the model are tested by using scanner panel data from a field experiment on coupon face values. Coupon redemption rates are found to be much higher among households that have purchased the brand on a regular basis in the past. The results also suggest that most consumers revert to their precoupon choice behavior immediately after their redemption purchase. These and other findings have important implications for the profitability of coupon promotions.

Quality Tier Competition: How Price Change Influences Brand Choice and Category Choice

1997 ◽  
Vol 61 (3) ◽  
pp. 71-84 ◽  
Author(s):  
K. Sivakumar ◽  
S. P. Raj
Keyword(s):  
Panel Data ◽  
Price Change ◽  
Brand Choice ◽  
Price Reduction ◽  
Data Sets ◽  
High Quality ◽  
Scanner Panel Data ◽  
Promotional Strategies ◽  
Category Choice ◽  
Scanner Panel

The authors demonstrate that competition among brands in different quality tiers can be asymmetric both in brand choice (“what”) and in category choice (“whether”). They also investigate how competition among quality tiers is affected by the direction of price changes (increase or decrease). Empirical findings from four scanner panel data sets consistently show that with price reduction, high-quality brands gain more than do low-quality brands both in “what” and “whether” decisions. Furthermore, high-quality brands are less vulnerable to losses when prices are increased. The authors conclude with implications for understanding quality tier competition and developing price promotional strategies.

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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