Decision Support System For A Customer Relationship Management Case Study

  title={Decision Support System For A Customer Relationship Management Case Study},
  author={{\"O}zge Kart and Alp Kut and Vladimir Radevski},
  journal={International Journal of Informatics and Communication Technology},
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… 

Figures and Tables from this paper


Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty
A new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies-Bouldin Index is proposed, which has been implemented for SAPCO Co. in Iran.
Data Mining Application in Customer Relationship Management of Credit Card Business
This paper employs data mining tools and effectively discover the current spending pattern of customers and trends of behavioral change, which allow management to detect in a large database potential changes of customer preference, and provide as early as possible products and services desired by the customers to expand the clientele base and prevent customer attrition.
Using data mining for bank direct marketing: an application of the CRISP-DM methodology
The increasingly vast number of marketing campaigns over time has reduced its effect on the general public. Furthermore, economical pressures and competition has led marketing managers to invest on
Application of data mining techniques in customer relationship management: A literature review and classification
Findings of this paper indicate that the research area of customer retention received most research attention and classification and association models are the two commonly used models for data mining in CRM.
Data Mining
  • K. Rihaczek
  • Computer Science, Medicine
    Datenschutz und Datensicherheit
  • 2000
This article focuses on data mining technology and its ability to uncover hidden and unexpected patterns in data for strategic decision-making in healthcare.
Characterising Data Mining software
A standard schema for the characterisation of Data Mining software tools is presented and the results of a recent survey of 41 popular Data Mining tools described within this schema are presented.
Linking innovative product development with customer knowledge: a data-mining approach
An E-CKM model with a methodology for precisely delineating the process of customer knowledge management for innovative product development is proposed and meets the evaluation criteria in a multiple-assessment scheme for showing a satisfactory result.
On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities
It is argued that the reformulation of eCRM problems within this new framework of analysis can result in more powerful analytical approaches.
Applications of data mining in retail business
  • S. Ahmed
  • Computer Science, Business
    International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004.
  • 2004
This work highlights the application of data mining in retail business, and its pros and cons on consumers.
Applying data mining to telecom churn management
This study compares various data mining techniques that can assign a ‘propensity-to-churn’ score periodically to each subscriber of a mobile operator and indicates that both decision tree and neural network techniques can deliver accurate churn prediction models.