Erfolgsorientiertes Customer Relationship Management (CRM) auf der Basis von Business Intelligence (BI)-Lösungen

2001 ◽  
pp. 233-255
Author(s):  
Thomas Schulze
Keyword(s):  
Customer Relationship Management ◽  
Business Intelligence ◽  
Relationship Management ◽  
Customer Relationship

Customer Relationship Management Based on Business Intelligence : Proposed Framework

2021 ◽  
Author(s):  
Alaa Alyamani ◽  
◽  
Mohammed Alsalem ◽  
Keyword(s):  
Customer Relationship Management ◽  
Business Intelligence ◽  
General Framework ◽  
Academic Research ◽  
Relationship Management ◽  
Customer Relationship ◽  
Main Role ◽  
Review Of Literature ◽  
High Importance ◽  
Major Interest

It is known currently that the processes of transaction and interaction in the various points and channels between the organization and their customers have resulted in huge amounts of data. These data have great importance if we analyzed properly. By analyzing these data can determine the appropriate ways of dealing with customers and identify their needs and preferences in order to enable the organizations to develop and maintain their relationships with them. Data storing and analytics technologies and applications such as business intelligence tools have been the major interest of researchers and practitioners due to the benefits that can provide for CRM applications. The purposes of this study are to discuss the usage of business intelligence in CRM in order to improve the activities of dealing with customers. As well as, this study proposed a general framework of CRM based on business intelligence technologies which can be applied in the clothing factory . An extensive review of literature has been conducted to identify trends and patterns of academic research in the context of usages business intelligence for CRM. The reviewed studies confirm the high importance of using business intelligence, and its main role in obtaining important insights and knowledge between large amounts of data.

Enhancing the Electronic Customer Relationship Management through Data Mining

2010 ◽  
pp. 119-139
Author(s):  
M. Vignesh
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Customer Relationship Management ◽  
Business Intelligence ◽  
Relationship Management ◽  
Daily Basis ◽  
Customer Relationship ◽  
Data Driven Approach ◽  
Second Tier

According to the Moore’s law, the number of transistors per microprocessor will double in every two years. In no doubt, this exponential increase in the processing speeds would be flanked by the increasing amount of data that corporates contend on a daily basis. Hence all corporates are literally drowning in data. But definitely there exists a hiatus between the data storage and the information retrieval. One can ask an enigmatic question, how effectively a stored data can be utilised for the decision making in the long-term perspective. The answer is not yet arrived out. Hence the “Organizations are data rich, but information poor!”. If capturing and storing the relevant data is a hectic task, then analyzing and translating this data into the actionable information is the other corner stone in any information systems of a concern. This gap can be bridged or overruled by the concept of business intelligence. Business Intelligence (BI) can be simply defined in terms of data –driven approach rather than information driven which includes methods as decision support systems, online analytical processing (OLAP), statistical analysis, query and reporting, forecasting which can be primarily done by data mining. BI along with customer relationship management (CRM) software forms the second tier of a firm’s IT infrastructure. This chapter holds a bird’s eye view of the usage of datawarehousing approaches for a systematic business intelligence approach and its varied applications in view of electronic customer relationship management.

Business Intelligence, Knowledge Management, and Customer Relationship Management Technological Support in Enterprise Competitive Competence

2015 ◽  
pp. 243-262 ◽  
Author(s):  
Ming-Chang Lee
Keyword(s):  
Knowledge Management ◽  
Customer Relationship Management ◽  
Business Intelligence ◽  
Business Models ◽  
Relationship Management ◽  
Customer Relationship ◽  
Knowledge Based ◽  
Customer Information ◽  
Modern Enterprise ◽  
Customer Lifetime

The approach of knowledge management, business intelligence, and customer relationship management was used as theoretical technologies in order to build an intelligence enterprise framework. Since the business intelligence process can create additional customer value through knowledge creation with the customer, business intelligence can provide users with reliable, accurate information and help them make decisions. Customer relationship management focuses on the integration of customer information and knowledge for finding and keeping customers to grow customer lifetime value. Therefore, integrating intelligence enterprise is needed in order to respond to the challenges the modern enterprise has to deal with and represents not only a new trend in IT but also a necessary step in the emerging knowledge-based economy. In intelligent enterprise operations, KM contains business models, processes, competence, implementation, performance assessment, and enterprise in information, organization, and e-commence processes.

Improving Customer Relationship Management Through Business Intelligence

2014 ◽  
Vol 13 (02) ◽  
pp. 1450015 ◽  
Author(s):  
Meliha Handzic ◽  
Kursad Ozlen ◽  
Nermina Durmic
Keyword(s):  
Customer Relationship Management ◽  
Business Intelligence ◽  
Business Strategy ◽  
Relevant Literature ◽  
Relationship Management ◽  
Customer Relationship ◽  
Current Investigation ◽  
East Europe ◽  
Competitive Actions

This paper examines empirically the role of business intelligence (BI) in customer relationship management (CRM). Drawing on relevant literature on BI and CRM, the research model for the current investigation proposes that BI approaches of an organisation and its competition influence organisational business strategy which in turn impacts its customer strategy. The model is tested empirically using survey data of 165 respondents from 73 different private and state owned businesses operating in a transitional economy of East Europe. Empirical evidence confirms a key role of BI in CRM through its impact on organisational business and customer strategies development. Such findings make two important contributions. For research, they provide an improved understanding of the factors and processes involved in realising benefits from BI. For practice, they show managers how BI can be leveraged to achieve performance gains through competitive actions. Further research is recommended to confirm and extend the current investigation.

Business Intelligence, Knowledge Management, and Customer Relationship Management Technological Support in Enterprise Competitive Competence

2016 ◽  
pp. 216-233 ◽  
Author(s):  
Ming-Chang Lee
Keyword(s):  
Knowledge Management ◽  
Customer Relationship Management ◽  
Business Intelligence ◽  
Business Models ◽  
Relationship Management ◽  
Customer Relationship ◽  
Knowledge Based ◽  
Customer Information ◽  
Modern Enterprise ◽  
Customer Lifetime

The approach of knowledge management, business intelligence, and customer relationship management was used as theoretical technologies in order to build an intelligence enterprise framework. Since the business intelligence process can create additional customer value through knowledge creation with the customer, business intelligence can provide users with reliable, accurate information and help them make decisions. Customer relationship management focuses on the integration of customer information and knowledge for finding and keeping customers to grow customer lifetime value. Therefore, integrating intelligence enterprise is needed in order to respond to the challenges the modern enterprise has to deal with and represents not only a new trend in IT but also a necessary step in the emerging knowledge-based economy. In intelligent enterprise operations, KM contains business models, processes, competence, implementation, performance assessment, and enterprise in information, organization, and e-commence processes.

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