A latent variable model for market segmentation (original) (raw)
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Inferring Market Structure with Aggregate Data: A Latent Segment Logit Approach
Journal of Marketing Research, 1993
In this paper, the authors introduce a "latent segment logit" (lSl) model that allows the identification of latent market segments when only macro-level time-series data (e.g., market share or sales, not individual choices) are available. The proposed model provides a paramorphic representation of market structure, based on the notion that "structure" implies heterogeneity in preferences and/or respanse to marketing mix elements. It assumes that independence of irrelevant alternatives (IIA) holds within latent segments (i.e., segments are homogeneous) but allows for heterogeneity across segments. Estimates for segment characteristics (including size, brand preferences, and sensitivity to marketing mix variables) are obtained byapplying the model to aggregated longitudinal panel data. Validation tests are conducted on both the aggregated and disaggregated panel data. Aggregate validation demonstrates that the model is superior to standard market share models in terms of calibration and predictive fit. Disaggregated validation demonstrates that the latent segments recovered by the model account for much of the variation across household purchase histories, even though these data were not utilized in the estimation.
A Probabilistic Choice Model for Market Segmentation and Elasticity Structure
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Factor Data Analysis and econophysics: application in market segmentation
Journal of Engineering Science and Technology Review, 2011
In this paper, an overview of factor data analysis methods is presented, as an alternative approach to classic statistical methods and it is shown that they are a powerful tool for analyzing economic phenomena. The principles on which data analysis methods are based are in a large degree inspired by physics, not only as general considerations but also as specific concepts, terminologies and methods. The notions of energy, entropy and inertia are matched with information theory, linear algebra and statistics to provide powerful tools for modeling and analyzing non-linear economic phenomena. Considering that any phenomenon under study is a complex open dynamic system where a large number of factors interact with each other, factor data analysis methods are able to examine such interactions as a whole, instead of a set of independent pair-wise comparisons of factors. The mechanism underlying these methods is to map the problem to a multidimensional vector space and based on the data themselves, to discover the underlying patterns, to find out how series of figures organize and which variables or group of variables are correlated. Model construction is thus not restricted to any initial assumption and is entirely driven by the data (Greenacre, 2007). In order to depict the potential of such methods in economic analysis, we present the application of multiple correspondence analysis to the market segmentation in the business plan for an internet radio venture.
Market Segmentation and Dynamic Analysis of Sparkling Wine Purchases in Italy
Journal of Wine Economics
The Italian market of sparkling wines increases as volume and assortment (such as brands, appellations, typologies) mainly because of sparkling Prosecco consumption. We investigate the repeated purchase behavior of sparkling wines in two years within the supermarket channel through scanner data collected from a consumer panel. We propose a Hidden Markov Model to analyze these data, assuming an unobservable process to capture consumers’ preferences and allowing us to consider purchases sparsity over time. We consider multivariate responses defining types of purchases, namely price, appellation, and sugar content. Customers’ covariates influence the initial and transition probabilities of the latent process. We identify five market segments, and we track their evolution over time. One segment includes Prosecco-oriented consumers, and we show that loyalty to Prosecco changes strongly over time according to the region of residence, income, and family type. The findings improve the under...
Recovering and profiling the true segmentation structure in markets: an empirical investigation
International Journal of Research in Marketing, 2003
Although a variety of approaches for inferring market segments exist, little, if any, effort has been devoted to comparing the relative validity of these approaches. This study conducts two extensive simulation experiments in a scanner data setting to empirically compare and validate alternative mixture model-based procedures for segmenting households using choice behaviors and household characteristics. Compared to existing two-stage approaches, a new method known as the joint approach produced 23 -27% less error in estimates of characteristics and 30 -38% less error in estimates of choice model parameters. Contrary to conventional wisdom, the joint approach, which simultaneously uses household choice and characteristic data, is shown to be superior even when one is interested in recovering only characteristic-based segments. D (I.S. Currim).
Retail Clients Latent Segments
Abstract. Latent Segments Models (LSM) are commonly used as an approach for market segmentation. When using LSM, several criteria are available to determine the number of segments. However, it is not established which criteria are more adequate when dealing with a specific application. Since most market segmentation problems involve the simultaneous use of categorical and continuous base variables, it is particularly useful to select the best criteria when dealing with LSM with mixed type base variables. We first present an empirical test, which provides the ranking of several information criteria for model selection based on ten mixed data sets. As a result, the ICL-BIC, BIC, CAIC and L criteria are selected as the best performing criteria in the estimation of mixed mixture models. We then present an application concerning a retail chain clients’ segmentation. The best information criteria yield two segments: Preferential Clients and Occasional Clients. Keywords: Clustering, Finite Mixture Models, Information Criteria, Marketing Research
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
Food Quality and Preference, 2001
A procedure of clustering of variables is discussed and applied for the purpose of segmenting a panel of consumers. The underlying principle of the method is to find K groups of variables (i.e. the consumers) and K latent components such that the consumers in each group are as much correlated as possible with the corresponding latent component. The procedure involves running, in a first step, a hierarchical clustering algorithm to determine the appropriate number of clusters and an initial partition of consumers. In a second step, a partitioning algorithm is carried out in order to improve the solution thus obtained. This clustering approach is illustrated using two real data sets. On these data sets, the procedure MD-PREF is also performed and it is shown how it can be complemented by the outcomes of the cluster analysis. In particular, indication about the number of clusters among consumers is given.