API Standardization Efforts for Data Mining

2011 ◽  
pp. 39-43
Author(s):  
Jaroslav Zendulka
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Customer Relationship Management ◽  
Real World ◽  
Relationship Management ◽  
Scientific Data ◽  
Customer Relationship ◽  
Mining Technology ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Data mining technology just recently became actually usable in real-world scenarios. At present, the data mining models generated by commercial data mining and statistical applications are often used as components in other systems in such fields as customer relationship management, risk management or processing scientific data. Therefore, it seems to be natural that most data mining products concentrate on data mining technology rather than on the easy-to-use, scalability, or portability. It is evident that employing common standards greatly simplifies the integration, updating, and maintenance of applications and systems containing components provided by other producers (Grossman, Hornick, & Meyer, 2002). Data mining models generated by data mining algorithms are good examples of such components.

Applying data mining on customer relationship management system to discover forgotten effects

2020 ◽  
pp. 1-12
Author(s):  
Angélica Urrutia ◽  
Fabiola Rojo ◽  
Carolina Nicolas ◽  
Roberto Ahumada
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Causal Relationships ◽  
Customer Data ◽  
Customer Preferences ◽  
Data Mining Algorithms ◽  
Analysis Methodology ◽  
Mining Algorithms

Companies need to know customer preferences for decision-making. For this reason, the companies take into account the Customer Relationship Management (CRM). These information systems have the objective to give support and allow the management of customer data. Nevertheless, it is possible to forget causal relationships that are not always explicit, obvious, or observables. The aim of this study on new methodologies for finding causal relationships. This research used a data analysis methodology of a CRM. The traditional analysis method is the Theory of Forgotten Effects (TFE), which is considered in this work. The new approach proposed in this article is to use Data Mining Algorithms (DMA) like Association Rules (AR) to discover causal relationships. This study analyzed 5,000 users’ comments and opinions about a Chilean foods industry company. The results show that the DMA used in this work obtains the same values as the TFE. Consequently, DMA can be used to identify non-obvious comments about products and services.

The Research of Data Mining Technology in Customer Relationship Management

2014 ◽  
Vol 945-949 ◽  
pp. 3360-3363
Author(s):  
Tian Song ◽  
Guang Jian Chen ◽  
Yu Mei Luo
Keyword(s):  
Data Mining ◽  
Customer Satisfaction ◽  
Organizational Structure ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Technical Support ◽  
Customer Relationship ◽  
Mining Technology ◽  
Satisfaction Degree ◽  
Customer Satisfaction Degree

Customer relationship management (CRM) focuses on the customer and also aims to reestablish the organizational structure, optimize the business procedure as well as carry out the research upon the customer so as to enhance the customer satisfaction degree and improve the efficiency and profit of the enterprise. The technology of data mining has provided the powerful technical support for the CRM. This paper will make an analysis on the thinking of CRM, the procedure of data mining as well as the application of data mining to the CRM.

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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