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

A Systems Engineering Methodology for Wide Area Network Selection using an Analytical Hierarchy Process

In this paper, we apply a systems engineering methodology to select the most appropriate wide area network (WAN) media suite, according to organizational technical requirements, using an Analytic Hierarchy Process (AHP). AHP is a mathematical decision modeling tool that utilizes decomposition, determination, and synthesis to solve complex engineering decision problems. AHP can deal with the universal modeling of process engineering decision-making, which is difficult to describe quantitatively, by integrating quantitative and qualitative analysis. We formulate and apply AHP to a hypothetical case study in order to examine its feasibility for the WAN media selection problem. The results indicate that our model can improve the decision-making process by evaluating and comparing all alternative WANs. This shows that AHP can support and assist an organization in choosing the most effective solution according to its demands. AHP is an effective resource-saver from many perspectives—it gives high performance, economic, and high quality solutions. Keywords: Analytical Hierarchy Process, Wide Area Network, AHP Consistency, WAN alternatives

Market segment evaluation and selection based on application of fuzzy AHP and COPRAS-G methods

Market segment evaluation and selection is one of the critical marketing problems of all companies. This paper presents a novel approach which integrates fuzzy analytic hierarchy process (FAHP) and COPRAS-G method for market segment evaluation and selection. Fuzzy AHP is used to calculate the weight of each criterion, and COPRAS-G method is proposed to prioritize market segments from the best to the worst ones. The application of fuzzy set theory allows incorporating the vague and imprecise linguistic terms into the decision process. This study can be used as a pattern for market segment selection and future researches. A case study on a chair manufacturing company is put forward to illustrate the performance of the proposed methodology. First published online: 14 Sep 201

Analyzing Patients’ Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients’ values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients’ needs

Spiking neurons in 3D growing self-organising maps

In Kohonen’s Self-Organising Maps (SOM) learning, preserving the map topology to simulate the actual input features appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. Nonetheless, it is a challenging task as most of the real problems are composed of complex and insufficient data. Spiking Neural Network (SNN) is the third generation of Artificial Neural Network (ANN), in which information can be transferred from one neuron to another using spike, processed, and trigger response as output. This study, hence, embedded spiking neurons for SOM learning in order to enhance the learning process. The proposed method was divided into five main phases. Phase 1 investigated issues related to SOM learning algorithm, while in Phase 2; datasets were collected for analyses carried out in Phase 3, wherein neural coding scheme for data representation process was implemented in the classification task. Next, in Phase 4, the spiking SOM model was designed, developed, and evaluated using classification accuracy rate and quantisation error. The outcomes showed that the proposed model had successfully attained exceptional classification accuracy rate with low quantisation error to preserve the quality of the generated map based on original input data. Lastly, in the final phase, a Spiking 3D Growing SOM is proposed to address the surface reconstruction issue by enhancing the spiking SOM using 3D map structure in SOM algorithm with a growing grid mechanism. The application of spiking neurons to enhance the performance of SOM is relevant in this study due to its ability to spike and to send a reaction when special features are identified based on its learning of the presented datasets. The study outcomes contribute to the enhancement of SOM in learning the patterns of the datasets, as well as in proposing a better tool for data analysis

Visualising Business Data: A Survey

A rapidly increasing number of businesses rely on visualisation solutions for their data management challenges. This demand stems from an industry-wide shift towards data-driven approaches to decision making and problem-solving. However, there is an overwhelming mass of heterogeneous data collected as a result. The analysis of these data become a critical and challenging part of the business process. Employing visual analysis increases data comprehension thus enabling a wider range of users to interpret the underlying behaviour, as opposed to skilled but expensive data analysts. Widening the reach to an audience with a broader range of backgrounds creates new opportunities for decision making, problem-solving, trend identification, and creative thinking. In this survey, we identify trends in business visualisation and visual analytic literature where visualisation is used to address data challenges and identify areas in which industries use visual design to develop their understanding of the business environment. Our novel classification of literature includes the topics of businesses intelligence, business ecosystem, customer-centric. This survey provides a valuable overview and insight into the business visualisation literature with a novel classification that highlights both mature and less developed research directions

Customer Value Analysis Based on WRFM Model With the Combined Data Mining Method (Case Study of Hygienic and Cosmetic Products)

The accumulated volume of customer information due to the growth and development of information technology and the creation of databases has led companies that want to provide better services to their customers to benefit from new tools for customer relationship. One of these tools and methods is data mining techniques that can play an important and key role in customer relationship management. The purpose of this study is to analyze customer value with a combined data mining approach based on the WRFM model. So 64858 samples from customer database in the period 2019-2020 have been selected by available purposive sampling method. The weight of WRFM attributes has been determined by surveying 3 experts of the company using a hierarchical analysis process. Based on the initial variables of the research and the variables obtained from the attributes of the WRFM model, the purchase value of customers has been analyzed. SPSS Modeller and SPSS software were used to analyze the data.The results show that the K-Means clustering method has a better performance in customer segmentation than the TwoStep clustering and the Cohonen neural network methods. Finally, based on the criteria of purity percentage, repetition, error rate and Normalized Mutual Information (NMI (index, six clusters with NMI (0.631) were selected from different K-Means clustering.This study introduces the WRFM model for customer value analysis.The weight of the attributes of this model is based on a survey of experts and using a hierarchical analysis process based on the degree of incompatibility (0.052) obtained from the hierarchical analysis method (0.15), (0.29) and (0.56), respectively, have been determined that these values ​​indicate the greater importance of the monetary value index than the other two indices; Finally, these six clusters were divided into 4 general categories using naming market segments methods in research (Chang and Tsai 2004; Babaian and Sarfarazi 2019): key and special customers, golden potential customers, missing uncertain customers and new uncertain customers. According to the research model, the company should focus more on its specific and key customers, ie customers who are in the first, third and fifth clusters, ie loyal customers who have higher than average values in the two attributes of monetary value and frequency and recently they have had purchases with a high value of Rials that the company should consider effective marketing strategies for this group of customers due to its limited resources in order to lead to more profitability for the company while maintaining customer relationship management

Seismic attribute optimization with unsupervised machine learning techniques for deepwater seismic facies interpretation: users vs machines

Machine learning (ML) has many applications within the geosciences, from predicting seismic facies, to automatic fault detection. A variety of machine learning algorithms are commonly employed, among these principal component analysis (PCA) and self-organized maps (SOMs), which provide a fast organization of data into groups that aid in geological interpretation. It is, nevertheless, interesting to note that parametrization choices during algorithm initiation could create a range of reasonable output model responses. The goal of PCA is to reduce a multivariate space down to a computationally more manageable size of variables. But this method relies primarily on the mathematically calculated eigenvectors and does not consider the a priori knowledge of the interpreter. The main motivation of this research is to investigate the impact of a user-controlled selection of attributes to perform SOM for facies classification versus a machine- derived result. Looking at a reflection seismic data of deepwater channel systems in the Taranaki Basin, a variety of attribute classes are systematically examined, including geometric, instantaneous, and textural attributes, in mixed combinations with one another, to understand how input variability alters the resultant SOM classification for deepwater architectural elements and facies characterization. The findings reveal that an appropriate combination of attributes with a clear interpretation objective enhance the SOMs results and facilitates the interpreter understanding of the output classes especially if attributes are previously tested. On the other hand, PCA provides insightful information regarding the contribution of attributes that may not have been initially considered by the interpreter. This study reveals that while ML techniques are a powerful tool for geological interpretation, user control on initial input attributes and to validate output results remain necessary for an optimal interpretation, at least in unsupervised ML methods

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.