A Hybrid Data Mining Model for Effective Citizen Relationship Management: A Case Study on Tehran Municipality

2010 ◽  
Author(s):  
Ali Mohammad Ahmadvand ◽  
Behrooz Minaei Bidgoli ◽  
Elham Akhondzadeh
Keyword(s):  
Data Mining ◽  
Relationship Management ◽  
Hybrid Data ◽  
Mining Model

Decision Support System For A Customer Relationship Management Case Study

2014 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Özge Kart ◽  
Alp Kut ◽  
Vladimir Radevski
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Banking Sector ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Set ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Management Case Study

<span lang="EN-US">Data mining is a computational approach aiming to discover hidden and valuable information in large datasets. It has gained importance recently in the wide area of computational among which many in the domain of Business Informatics. This paper focuses on applications of data mining in Customer Relationship Management (CRM). The core of our application is a classifier based on the naive Bayesian classification. The accuracy rate of the model is determined by doing cross validation. The results demonstrated the applicability and effectiveness of the proposed model. Naive Bayesian classifier reported high accuracy. So the classification rules can be used to support decision making in CRM field. The aim of this study is to apply the data mining model to the banking sector as example case study. This work also contains an example data set related with customers to predict if the client will subscribe a term deposit. The results of the implementation are available on a mobile platform. </span>

scholarly journals Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

2020 ◽  
Vol 11 (2) ◽  
pp. 1-10
Author(s):  
Bashar Shahir Ahmed ◽  
Mohamed Larabi Ben Maâti ◽  
Mohammed Al-Sarem
Keyword(s):  
Data Mining ◽  
Access Control ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Mining Algorithms ◽  
Control Functions ◽  
Customer Segment ◽  
Predictive Data Mining ◽  
Mining Model

The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.

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