Market Segmentation and Competitive Analysis for Supermarket Retailing (original) (raw)

Competitive market structure and segmentation analysis with self-organizing feature maps

Proceedings of the 27th EMAC Conference. Track, 1998

The simultaneous treatment of two interrelated and well-known tasks from strategic marketing planning, namely the determination of competitive market structure (CMS) and market segmentation, is addressed via application of the "Self-Organizing (Feature) Map" (SOM) methodology, as originally proposed by . In the present paper, some major aspects of the methodological basis of the SOM method are outlined and an SOM-based joint CMS-(preference-)segmentation analysis is illustrated using individual brand choice probabilities derived from diary household panel data.

A Probabilistic Choice Model for Market Segmentation and Elasticity Structure

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Neural market structure analysis: Novel topology-sensitive methodology

European Journal of Marketing, 2001

Over the last decade various parametric approaches toward modelling segmented perception-preference structures such as combined MDS and latent class procedures have been introduced. These methods, however, are not tailored for qualitative data describing consumers' redundant and fuzzy perceptions of brand images. A completely different method is based on topology-sensitive vector quantization (VQ) for consumers-by-brands-by-attributes data. It maps the segment-specific perceptual structures into bubble-pie-bar charts with multiple brand positions demonstrating perceptual distinctiveness or similarity. Though the analysis proceeds without any distributional assumptions, it allows for significance testing. The application of exploratory and inferential data processing steps to the same database is statistically sound and particularly attractive for market structure analysts. A brief outline of the VQ method is followed by a sample study with travel market data, which proved to be particularly troublesome for conventional processing tools.

Segmentation-based competitive analysis with MULTICLUS and topology representing networks

Computers & Operations Research, 2000

Two neural network approaches, Kohonen's self-organizing (feature) map (SOM) and the topology representing network (TRN) of Martinetz and Schulten are employed in the context of competitive market structuring and segmentation analysis. In an empirical study using brands preferences derived from household panel data, we compare the SOM and TRN approach to MULTICLUS, a parametric latent vector multi-dimensional scaling (MDS) model approach which also simultaneously solves the market structuring and segmentation problem. Our empirical analysis shows several bene"ts and shortcomings of the three methodologies under investigation. As compared to MULTICLUS, we "nd that the nonparametric neural network approaches show a higher robustness against any kind of data preprocessing and a higher stability of partitioning results. As compared to SOM, we "nd advantages of TRN which uses a more #exible concept of adjacency structure. In TRN, no rigid grid of units must be prespeci"ed. A further advantage of TRN lies in the possibility to exploit the information of the neighborhood graph for adjacent prototypes which supports ex-post decisions about the segment con"guration at both the micro and the macro level. However, SOM and TRN also have some drawbacks as compared to MULTICLUS. The network approaches are, for instance, not directly accessible to inferential statistics. Our empirical study indicates that especially TRN may represent a useful expansion of the marketing analyst's tool box.

Multisegment analysis of supermarket patronage

Journal of Business Research, 1990

This study evaluates the efficacy of different customer segmentation approaches in developing a better understanding of consumer choice among competing supermarkets in an urban setting. The analysis focuses on the differential impact of patronage-influencing variables within and across three different segmentation alternatives in a choice modeling context. A key objective is to determine whether one segmentation approach can yield a better understanding of retail choice than other segmentation approaches or a single aggregate model. If "best" approaches for segmenting the market are discovered for different situations, the findings will enable retail offerings to be targeted to key groups of customers more flexibly and effectively.

Clusterwise regression and market segmentation: developments and applications

Related methods 8.3 Monte Carlo analysis of performance 8.4 Empirical comparisons 8.4.1 Empirical comparison with clusterwise regression 8.4.2 Empirical comparison with optimal weighting 8.5 An investigation into the cross-validity ofFCR 8.5.1 Data 8.5.2 Results 8.6 Applications 8.6.1 An analysis of preferences for meat products 8.6.2 8.7 An analysis of attitudes' for outlets selling meat Conclusions 127 135