The effects of in-store displays and feature advertising on consideration sets

Abstract A heteroscedastic random utility model which allows for a flexible pattern of cross elasticities at the household level is explored. This flexibility enables the model to describe patterns of price sensitivity among competing brands which correspond to the competitive structure reflected in consideration sets. The effects of displays and features on these price sensitivities and the consideration sets are examined. The model is applied to scanner panel data of tuna purchases, where in-store displays and feature advertisements are found to increase product net utility and decrease price sensitivity for the promoted item.

[1]  Allan D. Shocker,et al.  Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions , 1991 .

[2]  B. Wernerfelt,et al.  An Evaluation Cost Model of Consideration Sets , 1990 .

[3]  H. Akaike.  A new look at the statistical model identification , 1974 .

[4]  Donald R. Lehmann,et al.  A Paired Comparison Nested Logit Model of Individual Preference Structures , 1989 .

[5]  Imran S. Currim.  Predictive Testing of Consumer Choice Models Not Subject to Independence of Irrelevant Alternatives , 1982 .

[6]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[7]  Pradeep K. Chintagunta,et al.  Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data , 1991 .

[8]  Gary J. Russell,et al.  A Probabilistic Choice Model for Market Segmentation and Elasticity Structure , 1989 .

[9]  D. McFadden.  Econometric analysis of qualitative response models , 1984 .

[10]  Dominique M. Hanssens,et al.  Modeling Asymmetric Competition , 1988 .

[11]  Joseph L. Zinnes,et al.  Theory and Methods of Scaling. , 1958 .

[12]  Jerry A. Hausman and Paul A. Ruud.,et al.  Specifying and Testing Econometric Models for Rank-ordered Data with an Application to the Demand for Mobile and Portable Telephones , 1986 .

[13]  Moshe Ben-Akiva,et al.  Incorporating random constraints in discrete models of choice set generation , 1987 .

[14]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[15]  Pradeep K. Chintagunta,et al.  Estimating a Multinomial Probit Model of Brand Choice Using the Method of Simulated Moments , 1992 .

[16]  J. Louviere,et al.  The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models , 1993 .

[17]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[18]  D. Gensch.  A Two-Stage Disaggregate Attribute Choice Model , 1987 .

[19]  Peter E. Rossi,et al.  Quality perceptions and asymmetric switching between brands , 1991 .

[20]  Peter E. Rossi,et al.  An exact likelihood analysis of the multinomial probit model , 1994 .

[21]  P. Nedungadi.  Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations , 1990 .

[22]  Peter E. Rossi,et al.  A Bayesian Approach to Estimating Household Parameters , 1993 .

[23]  M. Ben-Akiva,et al.  EMPIRICAL TEST OF A CONSTRAINED CHOICE DISCRETE MODEL : MODE CHOICE IN SAO PAULO, BRAZIL , 1987 .

[24]  Richard R. Batsell,et al.  A New Class of Market Share Models , 1985 .

[25]  John Roberts,et al.  Development and Testing of a Model of Consideration Set Composition , 1991 .

[26]  D. McFadden.  A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration , 1989 .

[27]  Wagner A. Kamakura,et al.  Predicting Choice Shares under Conditions of Brand Interdependence , 1984 .

[28]  J. Muellbauer,et al.  Economics and consumer behavior , 1980 .

[29]  G. Urban,et al.  Pre-Test-Market Evaluation of New Packaged Goods: A Model and Measurement Methodology , 1978 .

[30]  D. Wise,et al.  A CONDITIONAL PROBIT MODEL FOR QUALITATIVE CHOICE: DISCRETE DECISIONS RECOGNIZING INTERDEPENDENCE AND HETEROGENEOUS PREFERENCES' , 1978 .

[31]  Greg M. Allenby.  A Unified Approach to Identifying, Estimating and Testing Demand Structures with Aggregate Scanner Data , 1989 .

[32]  Wagner A. Kamakura,et al.  The Estimation of Multinomial Probit Models: A New Calibration Algorithm , 1989, Transp. Sci..

[33]  Greg M. Allenby,et al.  Modeling Household Purchase Behavior with Logistic Normal Regression , 1994 .