Segmentation has been taken immense attention and has extensively been used in strategic marketing. Vast majority of the research in this area focuses on the usage or development of different techniques. By means of the internet and database technologies, huge amount of data about markets and customers has now become available to be exploited and this enables researchers and practitioners to make use of sophisticated data analysis techniques apart from the traditional multivariate statistical tools. These sophisticated techniques are a family of either data mining or machine learning research. Recent research shows a tendency towards the usage of them into different business and marketing problems, particularly in segmentation. Soft computing, as a family of data mining techniques, has been recently started to be exploited in the area of segmentation and it stands out as a potential area that may be able to shape the future of segmentation research. In this article, the current applications of soft computing techniques in segmentation problem are reviewed based on certain critical factors including the ones related to the segmentation effectiveness that every segmentation study should take into account. The critical analysis of 42 empirical studies reveals that the usage of soft computing in segmentation problem is still in its early stages and the ability of these studies to generate knowledge may not be sufficient. Given these findings, it can be suggested that there is more to dig for in order to obtain more managerially interpretable and acceptable results in further studies. Also, recommendations are made for other potentials of soft computing in segmentation research.

Review: Soft computing applications in customer segmentation: State-of-art review and critique

http://mason.gmu.edu/~hleeb/IM_14_Digital_Systems_Competitive_Responsiveness.pdf

Digital systems and competitive responsiveness: The dynamics of IT business value

Using a unique dataset on U.S. beer consumption, we investigate brand preferences of consumers across various social group and context related consumption scenarios (“scenarios”). As sufficient data are not available for each scenario, understanding these preferences requires us to share information across scenarios. Our proposed modeling framework has two main building blocks. The first is a standard continuous random coefficients logit model that the framework reduces to in the absence of information on social groups and consumption contexts. The second component captures variations in mean preferences across scenarios in a parsimonious fashion by decomposing the deviations in preferences from a base scenario into a low dimensional brand map in which the brand locations are fixed across scenarios but the importance weights vary by scenario. In addition to heterogeneity in brand preferences that is reflected in the random coefficients, heterogeneity in preferences across scenarios is accounted for by allowing the brand map itself to have a discrete heterogeneity distribution across consumers. Finally, heterogeneity in preferences within a scenario is accounted for by allowing the importance weights to vary across consumers. Together, these factors allow us to parsimoniously account for preference heterogeneity across brands, consumers and scenarios. We conduct a simulation study to reassure ourselves that using the kind of data that is available to us, our proposed estimator can recover the true model parameters from those data. We find that brand preferences vary considerably across the different social groups and consumption contexts as well as across different consumer segments. Despite the sparse data on specific brand-scenario combinations, our approach facilitates such an analysis and assessment of the relative strengths of brands in each of these scenarios. This could provide useful guidance to the brand managers of the smaller brands whose overall preference level might be low but which enjoy a customer franchise in a particular segment or in a particular context or a social group setting.

Investigating brand preferences across social groups and consumption contexts

This paper examined the practice of using LinkedIn as a tool for teaching students how to create a professional online presence. A descriptive analysis of student LinkedIn profiles revealed that students included some basic requirements, but many students still neglected to fully complete the profile, thereby leaving out some important information that would boost their credibility. Additional exploratory analyses were conducted. Results are discussed in relation to contemporary best practices for the platform. Suggestions for the improvement of the assignment given to students to teach professional online presence through LinkedIn are also given.

Assessing students’ use of LinkedIn in a business and professional communication course

Clustering algorithms partition a set of n objects into p groups (called clusters), such that objects assigned to the same groups are homogeneous according to some criteria. To derive these clusters, the data input required is often a single n × n dissimilarity matrix. Yet for many applications, more than one instance of the dissimilarity matrix is available and so to conform to model requirements, it is common practice to aggregate (e.g., sum up, average) the matrices. This aggregation practice results in clustering solutions that mask the true nature of the original data. In this paper we introduce a clustering model which, to handle the heterogeneity, uses all available dissimilarity matrices and identifies for groups of individuals clustering objects in a similar way. The model is a nonconvex problem and difficult to solve exactly, and we thus introduce a Variable Neighborhood Search heuristic to provide solutions efficiently. Computational experiments and an empirical application to perception of chocolate candy show that the heuristic algorithm is efficient and that the proposed model is suited for recovering heterogeneous data. Implications for clustering researchers are discussed.

A Model for Clustering Data from Heterogeneous Dissimilarities

Previous research in marketing and consumer research has shown that consumers/households often possess multiple ideal points in a given product/service category. In such cases, traditional segmentation and positioning models that estimate a single ideal point per individual/segment may render an inaccurate portrayal of the true underlying utility functions of such consumers/segments and the resulting market structure. We propose a new clusterwise multiple-ideal-point spatial methodology that estimates multiple ideal points at the market segment level while simultaneously determining the market segments' composition of consumers, as well as the corresponding joint space.

/pdf/estimating-multiple-consumer-segment-ideal-points-from-83gppnv0o6.pdf

Estimating multiple consumer segment ideal points from context-dependent survey data

Few, if any, marketing students are adequately prepared to conduct a thorough job search that will lead them to enjoyable, meaningful employment upon graduation. We present a method we have used in several classes that helps demystify the job search process for students. Using our approach, students have been able to discover their career passions and find work that they enjoy. Our method can be used in numerous courses and can be used by both undergraduate and graduate students.

Give Them a Tool Kit: Demystifying the Job Search Process for Marketing Students.

Estimating Multiple Consumer Segment Ideal Points from Context-Dependent Survey Data

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Estimating multiple consumer segment ideal points from context-dependent survey data

Give Them a Tool Kit: Demystifying the Job Search Process for Marketing Students.

Estimating Multiple Consumer Segment Ideal Points from Context-Dependent Survey Data