Methoden vergleichen

	Consideration-Set Model	Brand-Switching Markov Model
Fachgebiet	Marketing	Marketing
Familie	Regression model	Regression model
Entstehungsjahr	1991	1992
Urheber	John H. Roberts & James M. Lattin	Stochastic-choice marketing tradition; codified in Lilien, Kotler & Moorthy
Typ	Two-stage discrete-choice model with latent consideration	Discrete-time Markov chain of brand purchasing
Wegweisende Quelle	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.	Lilien, G. L., Kotler, P., & Moorthy, K. S. (1992). Marketing Models. Prentice Hall. ISBN: 9780135456415
Aliasnamen	Consideration Set Composition Model, Consider-Then-Choose Model, Two-Stage Choice Model, Evoked Set Model	Brand Loyalty Markov Chain, Brand-Switching Matrix Model, Stochastic Brand-Choice Model, Markov Brand-Switching Analysis
Verwandt	3	3
Zusammenfassung	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 brand-switching Markov model treats a consumer's sequence of brand purchases as a Markov chain, in which the probability of buying a given brand next depends only on the brand bought last. Its central object is the brand-to-brand transition matrix, whose rows record, for buyers of each brand, the probabilities of staying loyal or switching to each competitor on the next purchase occasion. Estimated from panel purchase histories by simple frequency counts, the matrix can be propagated forward to forecast how shares evolve and solved for its steady-state distribution to predict long-run equilibrium market shares. The diagonal of the matrix measures repeat-purchase loyalty while the off-diagonals measure switching, giving managers a structural picture of competitive churn. The model is the classic stochastic-choice representation of brand dynamics and a conceptual precursor to the loyalty variables used in scanner-panel logit models. It is most useful where purchases are frequent, the brand set is stable, and the first-order memory assumption is approximately satisfied.