19,394 research outputs found

    Applying cluster analysis to build a patient-centric healthcare service strategy for elderly patients

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    [[abstract]]Cluster analysis can be viewed as a cornerstone for customer-centred services since it contributes to classification and segmentation of customers. The proposed six-step approach is based on a Customer Relationship Management (CRM) perspective and hence enables both, patient segmentation by cluster analysis, and development of customised services. The six steps are selection, preprocessing, transformation, data mining, evaluation and integration. Therefore, the proposed approach is a procedure to support knowledge management for strategic decision making. In the empirical study, we show how to deploy the proposed approach to build a customised service strategy for elderly patients. This procedure can also be applied to other data sets

    Applying cluster analysis to build a patient-centric healthcare service strategy for elderly patients

    Get PDF
    [[abstract]]Cluster analysis can be viewed as a cornerstone for customer-centred services since it contributes to classification and segmentation of customers. The proposed six-step approach is based on a Customer Relationship Management (CRM) perspective and hence enables both, patient segmentation by cluster analysis, and development of customised services. The six steps are selection, preprocessing, transformation, data mining, evaluation and integration. Therefore, the proposed approach is a procedure to support knowledge management for strategic decision making. In the empirical study, we show how to deploy the proposed approach to build a customised service strategy for elderly patients. This procedure can also be applied to other data sets. Copyright © 2009 Inderscience Enterprises Ltd

    Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification

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    Maintaining healthy organization-customers relationship has positive influence on customers’ behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers’ characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers’ transaction dataset into 3 and 4 disjoint segments based on customers’ frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers’ relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems. Keywords: customer relationship, data mining, k-means, pattern recognition, self organizing ma

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

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    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

    Further Thoughts on CRM

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    Skepticism and disappointment have replaced the initialenthusiasm about CRM. The disappointing results ofCRM-projects are often related to difficulties thatmanagers encounter in embedding CRM in their strategyand organization structure. In this article we presenta classification scheme on how CRM can be strategicallyembedded in organizations using the value disciplinesof Treacy and Wiersema. We use the findings from threecase studies to illustrate our classification. Based onthese case studies and interviews with managers wedistinguish between strategic and tactical CRM, andderive important issues that managers should considerbefore successfully implementing CRM.customer relationship management;marketing strategy;marketing performance

    Customer Segmentation with RFM Model using Fuzzy C-Means and Genetic Programming

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    One of the strategies a company uses to retain its customers is Customer Relationship Management (CRM). CRM manages interactions and supports business strategies to build mutually beneficial relationships between companies and customers. The utilization of information technology, such as data mining used to manage the data, is critical in order to be able to find out patterns made by customers when processing transactions. Clustering techniques are possible in data mining to find out the patterns generated from customer transaction data. Fuzzy C-Means (FCM) is one of the best-known and most widely used fuzzy grouping methods. The iteration process is carried out to determine which data is in the right cluster based on the objective function. The local minimum is the condition where the resulting value is not the lowest value from the solution set. This research aims to solve the minimum local problem in the FCM algorithm using Genetic Programming (GP), which is one of the evolution-based algorithms to produce better data clusters. The result of the research is to compare the application of fuzzy c-means (FCM) and genetic programming fuzzy c-means (GP-FCM) for customer segmentation applied to the Cahaya Estetika clinic dataset. The test results of the GP-FCM yielded an objective function of 20.3091, while for the FCM algorithm, it was 32.44741. Furthermore, evaluating cluster validity using Partition Coefficient (PC), Classification Entropy (CE), and Silhouette Index proves that the results of cluster quality from gp-fcm are more optimal than fcm. The results of this study indicate that the application of genetic programming in the fuzzy c-means algorithm produces more optimal cluster quality than the fuzzy c-means algorithm

    A new model to support the personalised management of a quality e-commerce service

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    The paper presents an aiding model to support the management of a high quality e-commerce service. The approach focuses on the service quality aspects related to customer relationship management (CRM). Knowing the individual characteristics of a customer, it is possible to supply a personalised and high quality service. A segmentation model, based on the "relationship evolution" between users and Web site, is developed. The method permits the provision of a specific service management for each user segment. Finally, some preliminary experimental results for a sport-clothing industry application are described

    PREDICTING CROSS-GAMING PROPENSITY USING E-CHAID ANALYSIS

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    Cross-selling different types of games could provide an opportunity for casino operators to generate additional time and money spent on gaming from existing patrons. One way to identify the patrons who are likely to cross-play is mining individual players’ gaming data using predictive analytics. Hence, this study aims to predict casino patrons’ propensity to play both slots and table games, also known as cross-gaming, by applying a data-mining algorithm to patrons’ gaming data. The Exhaustive Chi-squared Automatic Interaction Detector (E-CHAID) method was employed to predict cross-gaming propensity. The E-CHAID models based on the gaming-related behavioral data produced actionable model accuracy rates for classifying cross-gamers and non-cross gamers along with the cross-gaming propensity scores for each patron. Using these scores, casino managers can accurately identify likely cross-gamers and develop a more targeted approach to market to them. Furthermore, the results of this study would enable casino managers to estimate incremental gaming revenues through cross-gaming. This, in turn, will assist them in spending marketing dollars more efficiently while maximizing gaming revenues
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