Visual conjoint analysis (VCA): a topology of preferences in multi-attribute decision making

A1 Originalartikel i en vetenskaplig tidskrift (referentgranskad)

Interna författare/redaktörer

Publikationens författare: Peter Sarlin , Shahrokh Nikou, József Mezei, Harry Bouwman
Förläggare: Springer Netherlands
Publiceringsår: 2015
Tidskrift: Quality and Quantity
Volym: 49
Nummer: 1
Artikelns första sida, sidnummer: 385
Artikelns sista sida, sidnummer: 405


This paper proposes an approach denoted visual conjoint analysis (VCA). Conjoint analysis is commonly used in marketing to understand consumers’ decision criteria, particularly why consumers prefer and select certain products and their variations. Yet, little efforts have been made to provide visual means for exploring and visualizing preferences and utilities of consumers. In this paper, we propose an approach that enables identifying a low-dimensional topology of consumer profiles and their demographic characteristics. Through a two-step approach, VCA makes use of techniques for (i) data reduction and (ii) dimension reduction in combination with conjoint analysis. It provides a two-dimensional representation (dimension reduction) of a small number of respondent segments (data reduction). This provides means for two key tasks: (i) identifying the topology of multivariate respondent profiles in a lower dimension, focusing on neighborhood relations, and (ii) visual representations of information describing the respondent profiles, as well as the combination of the two tasks. The approach is applied to a real-world case of consumers’ preferences of mobile platform ecosystems.


Cluster analysis, Conjoint analysis, Data reduction, Dimension reduction, Visual conjoint analysis

Senast uppdaterad 2020-25-02 vid 05:41