Search Conferences

Type in any word, words or author name. This searchs through the abstract title, keywords and abstract text and authors. You may search all conferences or just select one conference.

 All Conferences
 EMAC 2019 Annual Conference
 EMAC 2020 Annual Conference
 EMAC 2020 Regional Conference
 EMAC 2021 Annual Conference
 EMAC 2021 Regional Conference
 EMAC 2022 Annual
 EMAC 2022 Regional Conference
 EMAC 2023 Annual
 EMAC 2023 Regional Conference

EMAC 2023 Annual

Brand Positioning Maps: Latent Allocation Model versus Correspondence Analysis

Published: May 24, 2023


pablo marshall, puc


Correspondence analysis is a methodology that reduces the dimensions of a contingency table to enable visualizing, in a perceptions map, the relationship between brands of a category and certain attributes or concepts. In this context, contingency tables have the form of word frequency, similar to the frequency matrices used in text analytics. This study proposes the use of topic modelling, particularly the Latent Dirichlet Allocation family of models, as an alternative methodology to reduce the dimension of a contingency table and represent brands and attributes or concepts in a perception map. In relation to correspondence analysis, the proposed methodology allows a simpler interpretation of the latent dimensions, uses ternary maps to represent three dimensions in a two-dimensional plane, and is based on a model that permits predicting and tracking brand perceptions. A simulation exercise and an application show the gains from using the proposed methodology.