Latent class analysis for segmenting preferences of investment bonds

Market segmentation is a key component of conjoint analysis which addresses consumer preference heterogeneity. Members in a segment are assumed to be homogenous in their views and preferences when worthing an item but distinctly heterogenous to members of other segments. Latent class methodology is one of the several conjoint segmentation procedures that overcome the limitations of aggregate analysis and a-priori segmentation. The main benefit of Latent class models is that market segment membership and regression parameters of each derived segment are estimated simultaneously. The Latent class model presented in this paper uses mixtures of multivariate conditional normal distributions to analyze rating data, where the likelihood is maximized using the EM algorithm. The application focuses on customer preferences for investment bonds described by four attributes; currency, coupon rate, redemption term and price. A number of demographic variables are used to generate segments that are accessible and actionable.peer-reviewe

Segmenting preferences for investment bonds using latent variable mixture models

Market segmentation is a key component of conjoint analysis which addresses consumer preference heterogeneity. Members in a segment are assumed to be homogenous in their views and preferences when worthing an item but distinctly heterogenous to members of other segments. Latent class methodology is one of the several conjoint segmentation procedures that overcome the limitations of aggregate analysis and a-priori segmentation. The main benefit of Latent class models is that market segment membership and regression parameters of each derived segment are estimated simultaneously. The Latent class model presented in this paper uses mixtures of multivariate conditional normal distributions to analyze rating data, where the likelihood is maximized using the EM algorithm. The application focuses on customer preferences for investment bonds described by four attributes; currency, coupon rate, redemption term and price. A number of demographic variables are used to generate segments that are accessible and actionable.peer-reviewe

Modelling customer preference heterogeneity to iPad attributes using a finite mixture procedure

The identification of segments in strategic market planning has long been recognized as a powerful tool to understand consumer behaviour. An approach that has managerial appeal in addressing market heterogeneity is by assuming that customers can be grouped in a number of unobserved homogeneous segments where customers in each cluster have similar purchasing behaviours. This paper describes the different procedures in affecting market segmentation focusing more on the Finite Mixture approach, while the application addresses heterogeneity issues in customer preferences when purchasing iPads given demographic and product-related predictors.peer-reviewe

Clusterwise regression and market segmentation : developments and applications

The present work consists of two major parts. In the first part the literature on market segmentation is reviewed, in the second part a set of new methods for market segmentation are developed and applied.Part 1 starts with a discussion of the segmentation concept, and proceeds with a discussion on marketing strategies for segmented markets. A number of criteria for effective segmentation are summarized. Next, two major streams of segmentation research are identified on the basis of their theoretical foundation, which is either of a microeconomic or of a behavioral science nature. These two streams differ according to both the bases and the methods used for segmenting markets.After a discussion of the segmentation bases that have been put forward as the normative ideal but have been applied in practice very little, different bases are classified into four categories, according to their being observable or unobservable, and general or product- specific. The bases in each of the four categories are reviewed and discussed in terms of the criteria for effective segmentation. Product benefits are identified as one of the most effective bases by these criteria.Subsequently, the statistical methods available for segmentation are discussed, according to a classification into four categories, being either a priori or post hoc, and either descriptive or predictive. Post hoc (clustering) methods are appealing because they deal adequately with the complexity of markets, while the predictive methods within this class (AID, clusterwise regression) combine this advantage with prediction of purchase (predisposition).Within the two major segmentation streams, segmentation methods have been developed that are specifically tailored to the segmentation problems at hand. These are discussed. For the microeconomic school focus is upon recently developed latent class approaches that simultaneously estimate consumer segments and market characteristics (market shares, switching, elasticities) within these segments. For the behavioral science school focus is on benefit segmentation. Disadvantages of the traditional two-stage approach, in which consumers are clustered into segments on the basis of benefit importances estimated at the individual level, are revealed and procedures that have been addressed to one or more of these problems are reviewed.In Part 2, three new methods for benefit segmentation are developed: clusterwise regression, fuzzy clusterwise regression and generalized fuzzy clusterwise regression.The first method is a clustering method that simultaneously groups consumers in a number of nonoverlapping segments, and estimates the benefit importances within segments. The performance of the algorithm on synthetic data is investigated in a Monte Carlo study. Empirically, the method is shown to outperform the two-stage procedure. Special attention is paid to significance testing with Monte Carlo test procedures, and convergence to local optima. An application to segmentation of the meat-market in the Netherlands on the basis of data on elderly peoples preferences for meat products is given. Three segments are identified. The first segment weights sensory quality against exclusiveness (price), in the second segment quality is traded off against fatness. This segment, comprising predominantly of females, had the best knowledge of nutrition. In the third segment preference is based on quality only. Regional differences were identified among segments.Fuzzy clusterwise regression extends clusterwise regression in that it allows consumers to be a member of more than one segment. It simultaneously estimates the preference functions within segments, as well as the degree of membership of consumers in those segments. Using synthetic data, the performance of the method is evaluated. Empirical comparisons with two other methods are provided, and the cross-validity of the method with respect to classification and prediction is assessed. Attention is given in particular to the selection of the appropriate number of segments, the setting of the user defined fuzzy weight parameter, and Monte Carlo significance test procedures. An application to data on preferences for meatproducts used on bread in the Netherlands revealed three segments. In the first segment, taste and fitness for common use are important. In the second segment, taste overridingly determines preference, but products that are considered more exclusive and natural and less fat and salt are also preferred. In segment three the health related product benefits are even more important. The importance of taste decreases from segment one to three, while the importance of health-related aspects increases in that direction. The health oriented segments comprised more females, older people and people who attributed causality of their behavior more to themselves.The method was also applied to data on consumers image for stores that sell meat. Again three segments were revealed. The value shoppers, trade off quality and price.They come from smaller families and spend less on meat. In the largest segment store image is based upon product quality. Females have higher membership in this segment, that is more involved with the store where they buy meat. For service shoppers, both service and atmosphere are important. This segment tends to be more store-loyal.Next, a generalization of fuzzy clusterwise regression is proposed, which incorporates both benefit segmentation and market structuring within the framework of preference analysis. The method simultaneously estimates the preference functions within each of a number of clusters, and the parameters indicating the degree of membership of both subjects and products in these clusters. The performance of this method is assessed in a Monte Carlo study on synthetic data. The method is compared empirically with clusterwise regression and fuzzy clusterwise regression. The significance testing with Monte Carlo test procedures, and the selection of the fuzzy weight parameters is treated in detail. Two segments were revealed in an analysis of consumer preferences of butter and margarine brands. The segments differed mainly in the importance attached to exclusiveness and fitness for multiple purposes. The brands competing within these segments were revealed. Females and consumers with a higher socioeconomic status had higher memberships in the segments in which exclusiveness was important.Finally, the clusterwise regression methods developed in this work are compared with other recently developed procedures in terms of the assumptions involved. The substantive results obtained in the empirical studies concerning foods are summarized and their implications for future research are given. The implications and the contribution of the methods to the development of marketing strategies for segmented markets are discussed

Value Measurement for New Product Category: a Conjoint Approach to Eliciting Value Structure

Ability to measure value from the customer\u27s point of view is central to the determination of market offerings: Customers will only buy the equivalent of perceived value, and companies can only offer benefits that cost less to provide than customers are willing to pay. Conjoint analysis is the most popular individual-level value measurement method to determine relative impact of product or service attributes on preferences and other dependent variables. This research focuses on how value measurement can be made more accurate and more reliable by measuring the relative influence of selected methodological variations on performance in prediction and on stability of value structure, and by grouping customers with similar value structure into segments which respond to product stimuli in a similar manner. Influences of the type of attributes included in the conjoint task, of the factorial design used to construct the product profiles, of the type and form of model, of the time of measurement, and of the type of cluster-based segmentation method, are evaluated. Data was gathered with a questionnaire that controlled for methodological variations, and with a notebook computer as the measurement object. One repeated measurement was taken. The study was conducted in two phases. In Phase I, influences of methodological variations on accuracy in prediction and on respective value structure were examined. In Phase II, different cluster-based segmentation methods--hierarchical clustering (HIC), non-hierarchical clustering (NHC), and fuzzy c-means clustering (FUC)--and according conjoint models were evaluated for their performance in prediction and in comparison with individual-level conjoint models. Results show the best models for a variety of design parameters are traditional individual-level, main-effects-only conjoint models. Neither modeling of interactions, nor segment-level conjoint models were able to improve on prediction. Best segment-level conjoint models were obtained with a fuzzy clustering method, worst models were obtained with k-means and the most fuzzy clustering approach. In conclusion, conjoint analysis reveals itself as a reliable method to measure individual customer value. It seems more rewarding for improvement of accuracy in prediction to apply repeated measures, or gather additional data about the respondent, than to attempt improvement on methodological variations with a single measurement

Essays in international market segmentation

The primary objective of this thesis is to develop and validate new methodologies to improve the effectiveness of international segmentation strategies. The current status of international market segmentation research is reviewed in an introductory chapter, which provided a number of methodological and substantive issues that need further attention. These issues are critically assessed and methodologies are developed as potential solutions.In chapter 1, previous research in international segmentation is classified according to three dimensions depicted in Figure 1. In the figure, the first dimension relates to the segmentation basis, the second to segmentation objects, and the third to segmentation methodology. All three dimensions affect the effectiveness of international segmentation strategies. Two key research directions for improving the effectiveness of international segmentation were formulated along these dimensions.The first direction concerns the integration of targeted product and communication strategies by linking product-specific bases with general consumer-level bases. A new methodology is developed to identify cross-national market segments using means-end chain theory. Based on theory founded in consumer behavior, the means-end chain links values (a general consumer-level basis) with benefits and attributes (product-specific bases).Figure 1Three dimensions of international segmentationSuch an approach has the potential to combine product development and communication strategies at the international segment level and may serve as a guiding principle for international marketers to tailor products and advertising messages to the desires of global consumer segments. Chapter 4 provides a model-based methodology for identifying such segments. An international segmentation model was developed that estimates relations between product attributes, benefits of product use, and consumer values at the international segment level, and at the same time identifies those segments. The model builds upon methodological issues that were addressed in chapters 2 and 3 and rests on mixture methodology that, due to its capability of deriving segments based on models of consumer behavior, is particularly effective. In particular, it accounts for the international sampling design and the heterogeneity of response tendencies across countries and consumers.The segmentation model was applied to identify segments in the European yogurt market, using a large sample of European consumers. Four segments were identified, of which one was truly pan-European and the other segments were cross-national. The segments were found to represent distinctive means-end structures and the pattern of links between attributes, benefits, and values gave rise to strategic implications with respect to product development and communication. The segments were found to be related to socio-demographics, consumption patterns, media consumption, and personality data, which contributes to the identifiability and accessibility of the segments. The results suggest that the proposed model-based international segmentation methodology, combining product- and consumer-level bases, has the potential to identify segments of consumers in different countries that are actionable towards product development and advertising strategy.In chapter 5, a different direction is proposed that seeks to improve the effectiveness of international target market selection of expanding companies, by improving the geographic configuration of segments. Whereas consumer segments are more responsive, their typical geographic configuration does not make them accessible with cost efficient logistic operations. Especially if physical distribution represents a major component of total production and marketing costs, it is important that a geographic segment defines one particular area as opposed to dispersed segments that may arise in previous segmentation approaches. A flexible model-based segmentation approach is developed that identifies contiguous geographic segments based on consumer-level data. The model is based on multi-attribute theory of preference formation and accommodates a broad set of strategic restrictions on the segments. Moreover, the model accounts for heterogeneity that is likely to exist within geographic segments.The methodology is illustrated in the international retailing domain, where geographic expansion is an important strategy to attain growth. Based on the importance that consumers attach to different attributes of store image, five geographic segments were identified across regions in seven countries of the European Union. The segments were distinctive in terms of their patterns of image attribute importances, which provides opportunities for expanding retailers to delineate geographic areas to enter and to develop an appropriate image in such areas. The results also demonstrated the accessibility of the segments through advertising media and logistics. In addition, no significant differences were found between the original model and a nested model that does not take the contiguity into account. This means that the actionability of restricting segments to be contiguous does not substantially harm the responsiveness of these segments.Given the often limited rigor of statistical and measurement techniques applied in the area of international segmentation, special attention has been given to methodological issues. Several issues were addressed that may negatively affect international segmentation research findings and methods were developed to deal with these issues.The first issue concerns the segmentation method . International segmentation research demonstrates an excessive reliance on heuristic segmentation techniques, such as cluster analysis. These techniques provide limited flexibility for international segmentation and may not be very effective in recovering response-based segments. The international segmentation methodologies developed in this thesis are model based and rely on insights from state of the art statistical techniques such as mixture and hierarchical Bayes models. Three international segmentation models are described in chapters 2, 4, and 5, and are successfully applied to empirical data. Chapter 5 provided a Bayesian formulation of a new international segmentation model that accommodates within-segment heterogeneity and complex restrictions on the configuration of segments. In chapter 4 it is empirically shown that a new mixture model approach outperforms standard clustering approaches that are traditionally employed in international segmentation.A second methodological issue is related to the estimation of international segmentation models. The importance of international sampling designs had not been acknowledged in the literature on international segmentation and mixture modeling. Previous international segmentation studies did not account for the implicit stratified sampling designs encountered in cross-national data collection. In this thesis the effects of international sampling designs on maximum likelihood estimation of segmentation models are investigated and a framework for accommodating those effects is proposed. A pseudo maximum likelihood procedure is introduced that accommodates complex sample designs for maximum likelihood estimation of finite mixture models. In addition, modified or pseudo-information criteria are suggested for correct estimation of the number of international segments.The effects of not accounting for the sampling design were empirically assessed in an international value segmentation study. The pseudo-maximum likelihood approach was compared to standard maximum likelihood estimation that does not account for the sampling design. The results show that the estimates of segment sizes and segment-level parameters may be severely biased when not accounting for the design in standard maximum likelihood estimation. In addition, the empirical application demonstrated that the use of standard information criteria leads to incorrect inferences about the number of segments. This means that standard estimation methods in international segmentation research may lead to incorrect conclusions and erroneous managerial action.The international segmentation methodology in chapter 4 was based on MEC theory. The traditional measurement technique for means-end chains (laddering) is not suitable for international segmentation. A necessary condition for the validity of international segments is that the basis for segmentation is measured in a valid and reliable way. Measurement instruments should allow collecting large and representative samples and standardization across countries. In this thesis a MEC measurement technique is developed that meets those criteria. The technique is denoted as the association pattern technique (APT), and its validity is further assessed. Two key issues were investigated that may hamper the validity of APT. First, APT implicitly assumes that attribute-benefit and benefit-value links are independent because it measures these links in two separate tasks. The second issue is the convergent validity of APT as compared to the more traditional laddering interview. Consistent support for independence of attribute-benefit and benefit-value links was found across four product categories. Statistical tests of convergent validity of APT and laddering demonstrated that the basic structure revealed by both methods is similar. This suggests that APT is valid for measuring means-end chains and can be used for identifying international consumer segments. The APT method is successfully applied in an international segmentation study in 11 countries.The final methodological issue addressed in this thesis is related to response tendencies , which may hamper the identification of cross-national segments. The APT method may be prone to a respondent's propensity to choose any link. Therefore, the international segmentation model in chapter 4 accounted for differences in those tendencies that may exist between respondents. Based on item response theory, a response threshold approach was developed that allows testing those differences between countries, but also within countries. The results demonstrated that the differences in response tendencies were significant between countries, but also within countries. This means that it is important to account for response tendencies in international segmentation but in domestic segmentation as well.</p

Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats

A large proportion of information systems research is concerned with developing and testing models pertaining to complex cognition, behaviors, and outcomes of individuals, teams, organizations, and other social systems that are involved in the development, implementation, and utilization of information technology. Given the complexity of these social and behavioral phenomena, heterogeneity is likely to exist in the samples used in IS studies. While researchers now routinely address observed heterogeneity by introducing moderators, a priori groupings, and contextual factors in their research models, they have not examined how unobserved heterogeneity may affect their findings. We describe why unobserved heterogeneity threatens different types of validity and use simulations to demonstrate that unobserved heterogeneity biases parameter estimates, thereby leading to Type I and Type II errors. We also review different methods that can be used to uncover unobserved heterogeneity in structural equation models. While methods to uncover unobserved heterogeneity in covariance-based structural equation models (CB-SEM) are relatively advanced, the methods for partial least squares (PLS) path models are limited and have relied on an extension of mixture regression—finite mixture partial least squares (FIMIX-PLS) and distance measure-based methods—that have mismatches with some characteristics of PLS path modeling. We propose a new method—prediction-oriented segmentation (PLS-POS)—to overcome the limitations of FIMIX-PLS and other distance measure-based methods and conduct extensive simulations to evaluate the ability of PLS-POS and FIMIX-PLS to discover unobserved heterogeneity in both structural and measurement models. Our results show that both PLS-POS and FIMIX-PLS perform well in discovering unobserved heterogeneity in structural paths when the measures are reflective and that PLS-POS also performs well in discovering unobserved heterogeneity in formative measures. We propose an unobserved heterogeneity discovery (UHD) process that researchers can apply to (1) avert validity threats by uncovering unobserved heterogeneity and (2) elaborate on theory by turning unobserved heterogeneity into observed heterogeneity, thereby expanding theory through the integration of new moderator or contextual variables

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Segmentation and Dimension Reduction: Exploratory and Model-Based Approaches

Representing the information in a data set in a concise way is an important part of data analysis. A variety of multivariate statistical techniques have been developed for this purpose, such as k-means clustering and principal components analysis. These techniques are often based on the principles of segmentation (partitioning the observations into distinct groups) and dimension reduction (constructing a low-dimensional representation of a data set). However, such techniques typically make no statistical assumptions on the process that generates the data; as a result, the statistical significance of the results is often unknown. In this thesis, we incorporate the modeling principles of segmentation and dimension reduction into statistical models. We thus develop new models that can summarize and explain the information in a data set in a simple way. The focus is on dimension reduction using bilinear parameter structures and techniques for clustering both modes of a two-mode data matrix. To illustrate the usefulness of the techniques, the thesis includes a variety of empirical applications in marketing, psychometrics, and political science. An important application is modeling the response behavior in surveys with rating scales, which provides novel insight into what kinds of response styles exist, and how substantive opinions vary among respondents. We find that our modeling approaches yield new techniques for data analysis that can be useful in a variety of applied fields