Simultaneous positioning and segmentation analysis with topologically ordered feature maps: a tour operator example

Topologically ordered feature maps (self-organizing maps, SOM) are neural network models for feature extraction and classification that may be relevant for deriving low-dimensional and parsimonious representations of multidimensional profile data.

Abstract

Positioning analysis seeks spatial representations of brands. Segmentation analysis searches for perceptually or preferentially homogeneous consumer segments. While these analytical steps often are taken sequentially, the positioning and segmentation problems are interrelated and need to be treated simultaneously. Topologically ordered feature maps (self-organizing maps, SOM) are neural network models for feature extraction and classification. Extracting prototypes corresponds with finding market segments; ordering them topologically resembles a perceptual mapping exercise. SOM modeling may thus be relevant for deriving low-dimensional and parsimonious representations of multidimensional profile data. The application of SOMs is explored in a case study on tour operator images. The results simultaneously inform about the firms’ image positions and their perceptually homogeneous customer segments.