Customer Relationship Management as an Imperative for Academic Libraries

Academic Libraries ◽

Linked Data ◽

Trust Management ◽

Relationship Management ◽

Customer Relationship ◽

Customer Data ◽

Customer Group

This chapter proposes a conceptual model, the 121 e-Agent Framework, for Customer Relationship Management (CRM) in academic libraries. Linked data and Semantic Web are the core components of this model. The implementation of the Framework will enable the participating U.S. academic libraries to reach out to their user communities through systematic customer group identification, differentiation, and interaction. The main contributions of the chapter are 1) applying Semantic Web technologies for CRM in academic libraries using the 121 e-Agent Framework, 2) defining the relevance challenges of CRM for academic libraries, 3) adding trust management to the linked data layer with a touch of tagging, categorizing, query log analysis, and social ranking as part of the underlying structure for distributed customer data filtering on the Web in CRM applications, and 4) making the approach extensible to address the challenges of CRM in other fields.

Customer Relationship Management as an Imperative for Academic Libraries

Mobile Computing and Wireless Networks ◽

10.4018/978-1-4666-8751-6.ch018 ◽

2016 ◽

pp. 395-428

Author(s):

Amanda Xu ◽

Sharon Q. Yang

Keyword(s):

Semantic Web ◽

Academic Libraries ◽

Linked Data ◽

Trust Management ◽

Relationship Management ◽

Customer Relationship ◽

Customer Data ◽

Customer Group

This chapter proposes a conceptual model, the 121 e-Agent Framework, for Customer Relationship Management (CRM) in academic libraries. Linked data and Semantic Web are the core components of this model. The implementation of the Framework will enable the participating U.S. academic libraries to reach out to their user communities through systematic customer group identification, differentiation, and interaction. The main contributions of the chapter are 1) applying Semantic Web technologies for CRM in academic libraries using the 121 e-Agent Framework, 2) defining the relevance challenges of CRM for academic libraries, 3) adding trust management to the linked data layer with a touch of tagging, categorizing, query log analysis, and social ranking as part of the underlying structure for distributed customer data filtering on the Web in CRM applications, and 4) making the approach extensible to address the challenges of CRM in other fields.

Customer Relationship Management and the Social and Semantic Web - Advances in Electronic Commerce ◽

Applying Semantic Web Technologies to Meet the Relevant Challenge of Customer Relationship Management for the U.S. Academic Libraries in the 21st Century Using 121 e-Agent Framework

10.4018/978-1-61350-044-6.ch017 ◽

2011 ◽

pp. 284-311

Author(s):

Sharon Q. Yang ◽

Amanda Xu

Keyword(s):

Semantic Web ◽

21St Century ◽

Academic Libraries ◽

Trust Management ◽

Customer Relationship ◽

Customer Data ◽

Web Technologies ◽

Social Semantic Web ◽

The Web

The main contributions of the chapter are 1) defining relevance challenge of CRM for U.S. academic libraries in the 21st century and applying social Semantic Web technologies to address the relevance challenge of CRM using 121 e-Agent framework in the Web as an infrastructure; 2) binding OLTP, OLAP, and Online Ontological Processing to social Semantic Web applications in CRM; 3) adding trust management to the linked data layer with a touch of tagging, categorizing, query log analysis, and social ranking as part of the underlying structure for distributed customer data filtering on the Web in CRM applications; 4) making the approach extensible to address relevance challenge of CRM in other fields.

Applying Semantic Web Technologies to Meet the Relevant Challenge of Customer Relationship Management for the U.S. Academic Libraries in the 21st Century Using 121 e-Agent Framework

Enterprise Resource Planning ◽

10.4018/978-1-4666-4153-2.ch044 ◽

2013 ◽

pp. 737-764

Author(s):

Sharon Q. Yang ◽

Amanda Xu

Keyword(s):

Semantic Web ◽

21St Century ◽

Academic Libraries ◽

Trust Management ◽

Customer Relationship ◽

Customer Data ◽

Web Technologies ◽

Social Semantic Web ◽

The Web

The main contributions of the chapter are 1) defining relevance challenge of CRM for U.S. academic libraries in the 21st century and applying social Semantic Web technologies to address the relevance challenge of CRM using 121 e-Agent framework in the Web as an infrastructure; 2) binding OLTP, OLAP, and Online Ontological Processing to social Semantic Web applications in CRM; 3) adding trust management to the linked data layer with a touch of tagging, categorizing, query log analysis, and social ranking as part of the underlying structure for distributed customer data filtering on the Web in CRM applications; 4) making the approach extensible to address relevance challenge of CRM in other fields.

Customer Relationship Management in Social and Semantic Web Environments

International Journal of Customer Relationship Marketing and Management ◽

10.4018/jcrmm.2010040101 ◽

2010 ◽

Vol 1 (2) ◽

pp. 1-10 ◽

Cited By ~ 19

Author(s):

Ángel García-Crespo ◽

Ricardo Colomo-Palacios ◽

Juan Miguel Gómez-Berbís ◽

Fernando Paniagua Martín

Keyword(s):

Semantic Web ◽

Paradigm Shift ◽

Dynamic Environment ◽

Relationship Management ◽

Customer Relationship ◽

Web 3.0 ◽

Web Technologies ◽

The Social

The growing influence of the Internet in current 21st-century everyday life has implied a paradigm shift in terms of relationships between customers and companies. New interaction means in the Web 1.0 have undergone a dramatic change in quantity and quality with the advent of the so-called Web 2.0, the Social Web. The upcoming Web 3.0, the Semantic Web will also impact tremendously in how companies understand Customer Relationship Management (CRM). In this dynamic environment, the present work presents a combination of both Social and Semantic Web Technologies and their application in the particular field of CRM. Tool and technology analysis both prove the challenging opportunities for these cutting-edge innovation trends in the CRM domain.

Customer Relationship Management in Social and Semantic Web Environments

Successful Customer Relationship Management Programs and Technologies ◽

10.4018/978-1-4666-0288-5.ch006 ◽

2012 ◽

pp. 83-92

Author(s):

Ángel García-Crespo ◽

Ricardo Colomo-Palacios ◽

Juan Miguel Gómez-Berbís ◽

Fernando Paniagua Martín

Keyword(s):

Semantic Web ◽

Dynamic Environment ◽

Relationship Management ◽

Customer Relationship ◽

Web 3.0 ◽

Web Technologies ◽

The Social ◽

The Web

The growing influence of the Internet in current 21st-century everyday life has implied a paradigm shift in terms of relationships between customers and companies. New interaction means in the Web 1.0 have undergone a dramatic change in quantity and quality with the advent of the so-called Web 2.0, the Social Web. The upcoming Web 3.0, the Semantic Web will also impact tremendously in how companies understand Customer Relationship Management (CRM). In this dynamic environment, the present work presents a combination of both Social and Semantic Web Technologies and their application in the particular field of CRM. Tool and technology analysis both prove the challenging opportunities for these cutting-edge innovation trends in the CRM domain.

The Implementation of Customer Relationship Management (CRM) Supported by SMS Gateway Technology

Jurnal Pendidikan Teknologi dan Kejuruan ◽

10.23887/jptk-undiksha.v16i1.16580 ◽

2019 ◽

Vol 16 (1) ◽

pp. 45

Author(s):

Komang Redy Winatha

Keyword(s):

Service Management ◽

Environmental Changes ◽

Relationship Management ◽

Customer Relationships ◽

Customer Relationship ◽

Web Based ◽

Customer Data ◽

Gateway Technology ◽

Web Based System

Responding to the higher restaurant industry competition, the Mailaku Roemah Nongkrong restaurant was not too flexible in facing an environmental changes. It was still using manual technology while there was an advancing technological developments. It was still applying the internal resources for business development. One way to overcome this problem is by utilizing technology and the concept of customer relationship management (CRM). CRM is a marketing strategy to create and maintain customer relationships and reduce the possibility of customers moving to other competitors. This study presented the development and implementation of CRM in a web-based system that was supported by sms gateway technology. The research methodology that will be used in this study consists of some steps, such as library study, observation, interviews, and system development which was divided into analysis, design, coding, and testing. The result was a web-based system was able to manage customer data, product promotion, and customer service management to create good relationships with customers. This system can be as an alternative for restaurants and customers in establishing practical business communication.

Customer Relationship Management and Knowledge Discovery in Database

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch144 ◽

2010 ◽

pp. 902-907

Author(s):

Jounghae Bang ◽

Nikhilesh Dholakiam ◽

Lutz Hamel ◽

Seung-Kyoon Shin

Keyword(s):

Knowledge Discovery ◽

Relationship Marketing ◽

Relationship Management ◽

Customer Relationships ◽

Customer Relationship ◽

Knowledge Discovery In Databases ◽

Data Warehouses ◽

Customer Data ◽

Purchase Patterns

Customer relationships are increasingly central to business success (Kotler, 1997; Reichheld & Sasser, 1990). Acquiring new customers is five to seven times costlier than retaining existing customers (Kotler, 1997). Simply by reducing customer defections by 5%, a company can improve profits by 25% to 85% (Reichheld & Sasser, 1990). Relationship marketing—getting to know customers intimately by understanding their preferences—has emerged as a key business strategy for customer retention (Dyche, 2002). Internet and related technologies offer amazing possibilities for creating and sustaining ideal customer relationships (Goodhue, Wixom, & Watson, 2002; Ives, 1990; Moorman, Zaltman, & Deshpande, 1992). Internet is not only an important and convenient new channel for promotion, transactions, and business process coordination; it is also a source of customer data (Shaw, Subramaniam, Tan, & Welge, 2001). Huge customer data warehouses are being created using advanced database technologies (Fayyad, Piatetsky- Shapiro, & Smyth, 1996). Customer data warehouses by themselves offer no competitive advantages: insightful customer knowledge must be extracted from such data (Kim, Kim, & Lee, 2002). Valuable marketing insights about customer characteristics and their purchase patterns, however, are often hidden and untapped (Shaw et al., 2001). Data mining and knowledge discovery in databases (KDD) facilitate extraction of valuable knowledge from rapidly growing volumes of data (Mackinnon, 1999; Fayyad et al., 1996). This article provides a brief review of customer relationship issues. The article focuses on: (1) customer relationship management (CRM) technologies, (2) KDD techniques, and (3) Key CRM-KDD linkages in terms of relationship marketing. The article concludes with the observations about the state-of-the-art and future directions.

Sistem Informasi Penjualan Dengan Fitur Customer Relationship Management (CRM) Pada Tria Salon Berbasis Web

Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) ◽

10.33173/jsikti.31 ◽

2019 ◽

Vol 1 (3) ◽

pp. 165-174

Author(s):

Welda Welda ◽

Bagus Baskara Sukarma ◽

Emmy Febriani Thalib

Keyword(s):

Customer Loyalty ◽

Relationship Management ◽

Customer Relationship ◽

Web Based ◽

Customer Data ◽

Transaction Data ◽

To Come ◽

Service Data ◽

Incorrect Data

Sales is a transaction carried out by two or more parties between one person to another with a valid payment that generates an income. One of the factors of success of sales is to maintain good relationships with customers, such as giving special promotions to Pelaggan so that customers will continue to come to buy products or services offered by a company.In this study found problems such as the difficulty of determining customer loyalty, the difficulty of determining the product that must be stocked a lot, the difficulty in recording transactions because of the possibility of missing or incorrect data due to Tria salon still recording data in the ledger. In addition, other problems that arise are the delivery of promotions that take more time due to having to send the same message one by one to the customer.The solution to solving this problem is to build a sales information system with web-based customer relationship management (CRM) features. Which later can help admin performance in managing customer data, managing product and service data and managing transaction data. In addition, it can help salon owners in managing customer reports and transaction reports and can provide promotions to all customers.From the results of blackbox testing that has been done, the results obtained are that the test is in accordance with the process that is run and produces an output that is in accordance with the system functions that have been defined.

Trends and Innovations in Marketing Information Systems - Advances in Marketing, Customer Relationship Management, and E-Services ◽

Data-Driven Customer Centricity

10.4018/978-1-4666-8459-1.ch008 ◽

2015 ◽

pp. 157-174

Author(s):

Othman Boujena ◽

Kristof Coussement ◽

Koen W. de Bock

Keyword(s):

Competitive Advantage ◽

Predictive Analytics ◽

Relationship Management ◽

Customer Behavior ◽

Customer Relationship ◽

Data Driven ◽

Customer Data ◽

Business Environments ◽

Customer Centricity

Customer relationship management (CRM) is becoming a very hot topic nowadays in academia and business environments. Indeed, companies are constantly searching for new innovative ways to create or maintain their competitive advantage. Due to the recent advances in Internet and technology, CRM predictive analytics is becoming an important tool in the toolset of the marketer. It is the practice of using the huge volumes of historical customer data to predict future customer behavior. This chapter introduces the reader to the shift towards a data-driven customer centricity approach, where marketers act upon what they know, rather than upon what they think.