Designing customer segmentation model for analysing consumer data: Case: Consumer segmentation model for retail sales

Customer segmentation has a crucial impact on today’s highly competitive business environment, and it is especially affecting to organizations marketing processes. The main idea behind customer segmentation is to divide customers into homogeneous groups based on their various characteristics. This enables organizations to tailor their marketing strategies to the various needs of different customers, without creating distinct plan for each individual customer. Customer segmentation can also help organizations to understand their customers better and to find latent business opportunities. There are many different approaches to customer segmentation in the consumer markets, but the four major approaches are behavioural, demographic, geographic, and psychographic segmentation. To utilize these approaches, it is recommended to use machine calculation and various data scientific methods to be able to process bigger amounts of data. The objective of this research was to design customer segmentation model, that is appropriate for analysing quantitative sales data in retailer consumer market. The purpose of the model is to show how behavioural based consumer segmentation could be implemented and utilized in the client organization. Research includes the theory and the empirical case study section, which follows the design science research as a strategic framework. The study explores various consumer segmentation approaches and data scientific methods that can be utilized in the segmentation model. The model is developed in case study, in which the identified methods are practically applied by using the client organization’s consumer sales data. The study is also investigating data scientific frameworks that can be utilized in the iterative development process of segmentation model along with the design science. The main result of this research is the consumer behaviour-based segmentation model, that utilizes customer’s recency, frequency, and monetary based modelling as a base segmentation method, which is widely used in the behavioural segmentation. The segmentation model divides consumers into seven homogenous segments based on their buying behaviour during the last five years. The used method is easy to understand, and it enables arbitrary tailoring of the limit values and the labels of segments. Some additional geographic and product related attributes were also added to model as an explanatory features. Another segmentation method considered in this research is K-Means clustering. The study found that this unsupervised method would be a proper solution if more than three features were used as a dividing criterion in the segmentation. However, clustering is not completely excluded from the model, as it offers a good comparison for manually created segments and enables several further development opportunities

Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

Structural Equation Modeling and simultaneous clustering through the Partial Least Squares algorithm

The identification of different homogeneous groups of observations and their appropriate analysis in PLS-SEM has become a critical issue in many appli- cation fields. Usually, both SEM and PLS-SEM assume the homogeneity of all units on which the model is estimated, and approaches of segmentation present in literature, consist in estimating separate models for each segments of statistical units, which have been obtained either by assigning the units to segments a priori defined. However, these approaches are not fully accept- able because no causal structure among the variables is postulated. In other words, a modeling approach should be used, where the obtained clusters are homogeneous with respect to the structural causal relationships. In this paper, a new methodology for simultaneous non-hierarchical clus- tering and PLS-SEM is proposed. This methodology is motivated by the fact that the sequential approach of applying first SEM or PLS-SEM and second the clustering algorithm such as K-means on the latent scores of the SEM/PLS-SEM may fail to find the correct clustering structure existing in the data. A simulation study and an application on real data are included to evaluate the performance of the proposed methodology

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