Integrated Clustering Modeling with Backpropagation Neural Network for Effcient Customer Relationship Management

2005 ◽  
pp. 355-373 ◽  
Author(s):  
Tijen Ertay ◽  
Bora Çekyay
Keyword(s):  
Neural Network ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Backpropagation Neural Network

scholarly journals Prediction of customer behaviour through datamining assets

2009 ◽  
Vol 57 (3) ◽  
pp. 43-54
Author(s):  
Naděžda Chalupová
Keyword(s):  
Neural Network ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Marketing Strategies ◽  
Customer Relationship ◽  
Business Managers ◽  
Correct Decision ◽  
Commercial Success ◽  
Customer Behaviour ◽  
Managerial Decisions

Business managers accounting for commercial success or non-success of the organization have to gain knowledge needful for correct decision acceptance. These knowledge represent sophisticated information hidden in enterprise data. One possibility, how to extract mentioned knowledge from data, is to use so-called datamining assets.The paper deals with an application of chosen basic methods of knowledge discovering in da­ta­ba­ses for area of customer-provider relation and it presents, how to avail acquired knowledge as basis of managerial decisions leading to improving of customer relationship management. It solves prediction, whose aim is, on the basis of some attributes of exploring objects, to predict future be­ha­viour of objects with these attributes. This way acquired knowledge, as the output of prediction, then can markedly help competent enterprise manager with planning of marketing strategies, for example so-called cross-selling and up-selling. The contribution describes a whole operation of available data processing: from its purifying, over its preparation for mining task, to self processing by the help of SAS Enterprise Miner tool. Regression analysis, neural network and decision tree, whose principles are briefly explained in this paper too, were used for knowledge mining. The estimation of customer behaviour was tested by two mining task varying in attribute using and in categories number of one of predicive attributes. The results of these two tasks are confronted by the help of prediction fruitfulness charts.

scholarly journals Development of Customer Relationship Management System in E-Commerce Based on BP Neural Network

2012 ◽  
Vol 6-7 ◽  
pp. 995-999
Author(s):  
Mei Ling Zhou ◽  
Jing Jing Hao
Keyword(s):  
Neural Network ◽  
Customer Relationship Management ◽  
Bp Neural Network ◽  
Management System ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Feed Forward Network ◽  
Neural Network Algorithm ◽  
Hidden Layer

BP neural network can learn and store a lot of input - output mode mapping, without prior reveal the mathematical equations describe the mapping. The model based on BP neural network algorithm is constituted by an input layer, output layer and one hidden layer, three-layer feed forward network. CRM is to acquire, maintain and increase the methods and processes of profitable customers. The core of CRM is the customer value management, customer value; it is divided into the de facto value, potential value and model value. The paper presents development of customer relationship management system in e-commerce based on BP neural network. The experiment shows BP is superior to RFCA in CRM.

scholarly journals Implementing a Data Mining Solution Approach to Identify the Valuable Customers for Facilitating Electronic Banking

2020 ◽  
Vol 14 (15) ◽  
pp. 157
Author(s):  
Mohammad Vahid Sebt ◽  
Elahe Komijani ◽  
Shiva S. Ghasemi
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Customer Relationship Management ◽  
Personal Information ◽  
Banking System ◽  
Relationship Management ◽  
Customer Relationship ◽  
Solution Approach ◽  
C4.5 Decision Tree

<p class="0abstract">Nowadays, the banking system is known as one of the inherent sectors of customer relationship management systems. Its main advantage is to redesign a more responsive organization to satisfy the customers. The banking system aims to improve the structure of organizations to provide a better customer service through a set of automated and integrated processes. The final goal is to collect and reprocess the personal information of customers. To handle this dilemma, a number of new techniques in data mining provide a powerful tool to explore customers’ information regarding a set of data and tools for customer relationship management. Accordingly, the customers’ classification and coordination of banking system are the main challenging issues of today's world. These reasons motivate the attempts of this study to apply a composition of neural network by considering the C4.5 decision tree and the k-closest neighbor method as a variant of core boosting methodology with maximal strategy. To validate the proposed solution approach, a case study of Ansar Bank in Iran is utilized. From the results, it is observed that the proposed method provides a competitive output with the rate of 95% for the customers’ classification. It also outperforms other existing methods with the rate of C4.5 decision tree, neural network, Naive Bayes and KNN with the rate of 1.04%. The main finding of this research is to propose an algorithm with the error rate of 1.9% and error squared of 0.72% as the best performance among other methods from the literature.<strong></strong></p>

scholarly journals CLASSIFICATION AND PREDICTION IN CUSTOMER RELATIONSHIP MANAGEMENT USING BACK PROPAGATION

2014 ◽  
pp. 173-178
Author(s):  
LEELA RANI KOMMA REDDY ◽  
G LOSHMA
Keyword(s):  
Neural Network ◽  
Business Process ◽  
Customer Relationship Management ◽  
Insurance Industry ◽  
Back Propagation ◽  
Relationship Management ◽  
Back Propagation Neural Network ◽  
Customer Relationship ◽  
Business Organization ◽  
Customer Classification

Customer Relationship Management provides a customer classification and prediction which is used for the optimization of business process. The classification and prediction which is used for the optimization of business process. This classification and prediction in CRM will help the company to study, analyze and forecast customers pattern of consumption, business transaction and purchasing CRM has become major activity in the enterprise based business organization using the CRM. CRM is an important activity in the enterprise business organization like banking industry, insurance industry, retail industry and manufacture industry. In the system we are using data mining techniques to implement customer classification in CRM as we need to analyze mass volume of data we are implementing an efficient and effective Neural Network based technique. Based on the existing system like Naïve Bayesian System, our proposed system implements Back propagation Neural Network techniques which would generate accurate results with less time complexity.

Implementasi Sistem Rekam Medis Pasien Menggunakan Pendekatan Customer Relationship Management (CRM)

2018 ◽  
Vol 7 (2) ◽  
pp. 180
Author(s):  
Wiyanto Wiyanto ◽  
Fajar Butsianto ◽  
Karsito Karsito
Keyword(s):  
Information Technology ◽  
Information System ◽  
Medical Record ◽  
Customer Relationship Management ◽  
Unified Modeling Language ◽  
Relationship Management ◽  
Customer Relationship ◽  
Design System ◽  
System Validation ◽  
Iso 9126

Information technology is rapidly developed in this century that impact to various aspects of the organization really need information technology to support the performance and everyday business processes. In health services, information technology is required to process and storage the patient medical records, so that the patient's medical record is well preserved, and competitive advantage can be obtained between patient and polyclinic. The application of Customer Relationship Management (CRM) approach can be developed by implementing information system of medical record history to get new patient and retain existing patient, improving relationship with patient and maintaining patient loyalty as well as supporting the company/organization to provide excellent service to customers in real time through the advantage of information technology. The aims of this research are to understand patient medical record by CRM approach and Unified Modeling Language (UML) for system design, system validation using Forum Group Discussion (FGD), and using software testing Model ISO 9126. The result of this research are Medical Record History Information System and the result of system validation with FGD is 100% accepted, the result of system test using Model ISO 9126 is good with success rate 82,86%, so it can give contribution to polyclinic.

Customer Relationship Management

2001 ◽  
Vol 30 (8) ◽  
pp. 417-422 ◽  
Author(s):  
Hajo Hippner ◽  
Stephan Martin ◽  
Klaus D. Wilde
Keyword(s):  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship
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