Módszerek összehasonlítása

	Consideration-Set Model	Nested Logit Brand Choice
Tudományterület	Marketing	Marketing
Módszercsalád	Regression model	Regression model
Keletkezés éve	1991	1978
Megalkotó	John H. Roberts & James M. Lattin	Daniel McFadden
Típus	Two-stage discrete-choice model with latent consideration	Generalized-extreme-value discrete-choice model
Alapmű	Roberts, J. H., & Lattin, J. M. (1991). Development and Testing of a Model of Consideration Set Composition. Journal of Marketing Research, 28(4), 429-440.	McFadden, D. (1978). Modelling the Choice of Residential Location. In A. Karlqvist, L. Lundqvist, F. Snickars, & J. Weibull (Eds.), Spatial Interaction Theory and Planning Models (pp. 75-96). North-Holland. ISBN: 9780444851826
Alternatív nevek	Consideration Set Composition Model, Consider-Then-Choose Model, Two-Stage Choice Model, Evoked Set Model	Nested Multinomial Logit, Hierarchical Choice Model, Tree-Structured Logit, GEV Nested Logit
Kapcsolódó	3	3
Összefoglaló	Consideration-set models formalize the empirical fact that consumers do not evaluate every available brand but choose from a small subset they actively consider. Choice is decomposed into two stages: first a brand is screened into the consideration (or evoked) set, then it competes for selection only against the other considered brands. John Roberts and James Lattin's 1991 model gave this idea a rigorous, estimable form by treating consideration as the outcome of a benefit-cost calculus — a brand is added to the set when the expected incremental benefit of including it exceeds a cost of consideration. The conditional second stage is typically a logit over the considered brands, so the unconditional choice probability is a weighted sum over possible consideration sets. Modeling the first stage matters because ignoring it biases estimated brand effects and substitution patterns: a brand can lose because it is never considered, not because it loses head-to-head. The framework underlies modern thinking about awareness, screening, and the upper funnel in brand competition.	The nested logit model of brand choice relaxes the restrictive independence-of-irrelevant-alternatives (IIA) assumption of the standard multinomial logit by grouping similar alternatives into nests. Developed by Daniel McFadden as a member of the generalized-extreme-value (GEV) family, it allows the unobserved utilities of alternatives within the same nest to be correlated while keeping a tractable closed form. In a brand-choice setting the natural structure is a tree: consumers first effectively choose a category, sub-category, or product form and then a brand within it, with an inclusive-value term carrying the expected utility of the lower level up to the upper level. The dissimilarity parameter on each nest measures within-nest correlation and reduces to ordinary logit when it equals one. The result is a model whose substitution patterns are far more realistic than plain logit — a price cut on one brand draws disproportionately from its nest-mates — while remaining estimable by maximum likelihood. It is a workhorse for choice analysis when alternatives fall into obvious clusters.