Positioning Analysis with Self-Organizing Maps: An Exploratory Study on Luxury Hotels

Positioning analysis examines the consumers' evaluative processes involving perceptions and preferences. Consumer behavior theory provides the hypotheses linking the perceptual and preferential positions of product brands or businesses to the expected brand choice decisions. For better visualization, various techniques of multivariate analysis assist in locating these positions in a space of low dimensionality. This data-processing step is necessary for bridging the gap between the fuzzy and redundant language of the consumer and the more abstract vocabulary of managers and service engineers. Hospitality services tend to be described with a rather large number of emotional and functional attributes. Condensing them into a compact spatial representation requires a significant data reduction effort. But the analyst faces still other challenges. Measuring perceptions and preferences turns out to be a segment-specific exercise. Strategic decisions on product positioning and market segmentation are interrelated. Contemporary decision-support models run the risk of becoming too complex to meet with managerial acceptance. Therefore, a simple method such as self-organizing maps (SOMs) may be attractive for exploratory purposes. SOMs are a variant of neural network models capable of detecting symptomatic brand positions. Managers may learn about typical combinations of product attributes, how they are assigned to brands, and how many and which consumers share similar views about brand profiles. A sample application of image and satisfaction measurement for luxury hotels demonstrates the technique. Strengths and weaknesses of this new neurocomputing approach to joint positioning/segmentation analysis are cautiously assessed. More experience from real-world applications is needed, and the industry is encouraged to experiment with SOMs on a larger scale.