A Probabilistic Choice Model for Market Segmentation and Elasticity Structure

By Wagner A. Kamakura, Gary J. Russell. 1989

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Citations in the corpus

A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category (2003, background): “The three-segment solution implies three segments of size 0.026, 0.003 and 0.971 respectively for segments 1, 2 and 3. In the context of scanner data research (see, for example, Kamakura and Russell, 1989 ),”
Modeling Variation in Brand Preference: The Roles of Objective Environment and Motivating Conditions (2002, methodology): “Common random-effect models of sales data using hierarchical Bayes (Allenby and Rossi 1999) and ﬁnite mixture models (Kamakura and Russell 1989) have ignored the second component in Equation (9) and have spec-iﬁed that variation in brand preference is entirely driven by the respondent effect, r .”
Latent class nested logit model for analyzing high-speed rail access mode choice (2012, methodology): “…the joint modeling selection of discrete alternatives and market segmentation, produces segment-specific parameter estimates to capture heterogeneous preferences for choice alternatives and identify decision-maker profiles for each segment (Kamakura and Russell, 1989; Gupta and Chintagunta, 1994).”
Modeling Household Purchase Behavior with Logistic Normal Regression (1994, background): “The most common of these is to simplify the evaluation of the likelihood (Chintagunta, Jain, and Vilcassim 1991; Kamakura and Russell 1989) by using a finite mixture model.”
Empirical Tests of Optimal Cognitive Distance (2004, methodology): “More specifically, we reestimated the logit model using a latent class modelling technique (e.g., Kamakura and Russell, 1989, Wedel and DeSarbo, 1994) in order to identify the number of different underlying latent segments among the pharmaceutical firms.”
Distinguishing taste variation from error structure in discrete choice data (2000, background): “Such latent class and random parameter speci®cations have a relatively long history in certain other disciplines (see review in Longford, 1993), but are relative newcomers to transportation (though see Cardell and Reddy, 1977) and marketing (see Cardell et al., 1978; Kamakura and Russell, 1989; Dill”
Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games (2007, unknown): “The specification of finite types is the aggregate analogue to the latent-class models used for individual-level data (Kamakura and Russell 1989).”
A Hidden Markov Model of Customer Relationship Dynamics (2008, background): “Within the marketing literature, HMMs are mostly related to the family of latent class models (Kamakura and Russell 1989).”
Learning from Experience. (2014, unknown): “For example, Keane and Wolpin (1994) use Monte Carlo integration and interpolation, Rust (1997a) introduces a randomization approach, and Imai et al.”
Weathering product-harm crises (2008, unknown): “Using aggregate analyses, and focusing on the total sales impact rather than trial, van Heerde et al. (2007) also obtained a non-significant own advertising effect for Eta, and a positive post-crisis advertising effectiveness for Kraft.”
Looking for Loss Aversion in Scanner Panel Data: The Confounding Effect of Price Response Heterogeneity (2000, methodology): “light of the concerns raised by our example, we allow for preference and response heterogeneity using a finite-mixture model (Kamakura and Russell 1989).”