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

Segmentacijska analiza poslovnih klijenata banaka pomoću samo-organizirajućih mapa

Samo-organizirajuće mape (SOM) su dvoslojne umjetne neuronske mreže koje su inicijalno kreirane za rješavanje problema klaster analize, vizualizacije i apstrakcije podataka. Njihov najveći doprinos je u području vizualizacije više-dimenzionalnih podataka na dvo-dimenzionalnu mapu, koja odražava eventualne veze među ulaznim podacima. Cilj ovog rada je prezentirati teorijsku osnovu SOM-a i prikazati primjenu metode u svrhu segmentacije tržišta. U radu je objašnjen algoritam SOM-Ward koji je implementiran u softveru Viscovery SOMine. Tada je u istom softveru provedena klaster analiza prema anketi poslovnih klijenata banaka. Nakon toga su prikazani i interpretirani rezultati te analize kao tri segmenta. Segmenti se razlikuju s obzirom na atribute trgovinskog poslovanja s inozemstvom (uvoz/izvoz), godišnje prihod, podrijetlo kapitala, stavove o odabiru kredita, planove zapošljavanja itd. Tako kreirani segmenti mogu biti korišteni za daljnje odlučivanje o poduzimanju marketinških aktivnosti.samo-organizirajuće mape, SOM, neuronska mreža, klaster analiza, segmentacija tržišta, rudarenje podataka

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

Mathematical model for dynamic case-based planning

This paper presents a case-based planning and beliefs, desires, intentions (CBP–BDI) planning model which incorporates a novel artificial neural network. The CBP–BDI model, which is integrated within an agent, is the core of a multi-agent system that allows managing the security in industrial environments. The BDI model integrates within a CBP engine of reasoning that incorporates artificial neural network-based techniques, and in this way it is possible to adapt past experiences to generate new plans. The proposed model uses self-organized maps to calculate optimum routes for the security guards. Besides, some technologies of ambient intelligence such as radio-frequency identification and Wi-Fi are used to develop the intelligent environment that has been tested and analysed in this paper

Segmentacijska analiza poslovnih klijenata banaka pomoću samoorganizirajućih mapa

Samo-organizirajuće mape (SOM) su dvoslojne umjetne neuronske mreže koje su inicijalno kreirane za rješavanje problema klaster analize, vizualizacije i apstrakcije podataka. Njihov najveći doprinos je u području vizualizacije više-dimenzionalnih podataka na dvo-dimenzionalnu mapu, koja odražava eventualne veze među ulaznim podacima. Cilj ovog rada je prezentirati teorijsku osnovu SOM-a i prikazati primjenu metode u svrhu segmentacije tržišta. U radu je objašnjen algoritam SOM-Ward koji je implementiran u softveru Viscovery SOMine. Tada je u istom softveru provedena klaster analiza prema anketi poslovnih klijenata banaka. Nakon toga su prikazani i interpretirani rezultati te analize kao tri segmenta. Segmenti se razlikuju s obzirom na atribute trgovinskog poslovanja s inozemstvom (uvoz/izvoz), godišnje prihod, podrijetlo kapitala, stavove o odabiru kredita, planove zapošljavanja itd. Tako kreirani segmenti mogu biti korišteni za daljnje odlučivanje o poduzimanju marketinških aktivnosti

A gestão centrada no cliente: avaliação do nível de preparação organizacional e proposta de segmentação em ambiente business-to-business

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de InformaçãoEsta investigação situa-se no âmbito organizacional numa empresa multinacional cujo core business é a comercialização de lentes, mas que também opera no mercado de armações de óptica (e de sol). Os clientes da empresa são essencialmente lojas de óptica, designadas por ópticas ou oculistas. Este projecto incide no desenvolvimento de uma proposta de segmentação da carteira de clientes no mercado de armações com base em dados transaccionais e utilizando o conceito de Customer Lifetime Value. Em primeiro lugar, procurou-se avaliar se a empresa está verdadeiramente centrada no cliente e se está apta a desenvolver segmentações na sua carteira de clientes, avaliando a receptividade por parte da empresa em adoptar uma filosofia de gestão centrada no cliente. O presente estudo teve como objectivos seguintes mostrar em quantos segmentos puderam ser agrupados os clientes da empresa, estudar quais as características de cada segmento de clientes e avaliar a rentabilidade de cada segmento, identificando quais os grupos de clientes mais rentáveis. A partir do conhecimento extraído das etapas anteriores, pretendeu-se, adicionalmente, apresentar pistas de actuação para programas de marketing com a finalidade de desenvolver a rentabilidade de cada segmento e promover o valor da empresa, actuando na retenção e desenvolvimento dos seus clientes. Espera-se com este estudo, apoiar a tomada de decisão a nível comercial e a nível de marketing e comunicação, no contexto de Customer Relationship Management