scholarly journals Customer relationship management: Are software applications aligned with business objectives?

2006 ◽  
Vol 37 (2) ◽  
pp. 17-32 ◽  
Author(s):  
M. L. Hart
Keyword(s):  
South African ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Strategy Formulation ◽  
Customer Data ◽  
Business Objectives ◽  
Software Applications ◽  
Customer Data Integration

Customer relationship management (CRM) can be thought of as IT-enabled relationship marketing. It has numerous definitions and perspectives, and success of implementation has been limited to date. This paper examines recent literature on the subject, drawing attention to the importance of a balance between strategy formulation, IT and organisational alignment when adopting and implementing CRM. It then discusses results of an exploratory study carried out amongst 34 South African organisations on their business objectives for CRM, the CRM applications that they have invested in or are considering, and the extent of integration of their customer data. The most important objectives and most widely used CRM applications are determined, and associations between applications and objectives are analysed. It is not apparent that CRM applications are selected in line with business objectives, and expected associations with objectives often do not exist for the objectives rated most important. Only certain aspects of customer data integration are significantly linked to objectives or CRM applications. The study suggests the need for fuller determination of strategy and objectives when involved with CRM investigation and adoption, and co-ordination at all levels of implementation between Marketing and IT.

scholarly journals The influence of bank employees on bank customer relationship management

2007 ◽  
Vol 7 (1) ◽  
Author(s):  
C. Rootman ◽  
M. Tait ◽  
J Bosch
Keyword(s):  
South Africa ◽  
South African ◽  
Customer Service ◽  
Customer Relationship Management ◽  
Service Providers ◽  
Relationship Management ◽  
Primary Objective ◽  
Customer Relationship ◽  
Positive Attitudes ◽  
Bank Employees

Purpose: Despite extensive research in services marketing, much is still unknown to specific service providers on the influence of their employees on their services. This paper attempts to address this limitation and investigates the influence of employees on the customer relationship management (CRM) of banks. The primary objective of this paper is to investigate the influence of selected independent variables, namely attitude and knowledgeability, on the CRM of banks.Design/Methodology/Approach: An empirical investigation was conducted with a structured questionnaire with items that related to banks' CRM in terms of attitude and knowledgeability. The sample consisted of 290 banking clients in the Nelson Mandela Metropolitan area and the response rate was 91.03%. Findings: Significant positive relationships exist between both the knowledgeability, and attitude of bank employees and a bank's CRM. These relationships imply that more extensive knowledgeability and more positive attitudes of bank employees lead to improved, maintained relationships between a bank and its clients. Employees play an important role in banks’ client relationships. Implications: Banks should focus on increasing their employees' knowledgeability and improving their attitude to ensure higher levels of CRM. This paper provides strategies for banks and could create greater awareness among South African banks of the advantages of CRM, how their employees influence their CRM, and ways to adapt to these influences. Originality/Value: No study has focused exclusively on CRM within banks in South Africa. Prior research focused on customer service and service quality; both possible results of superior CRM. However, this research differs, as it identifies the variables influencing CRM in banks in South Africa. It is proposed that this paper will be beneficial for South African banks, as the recommendations may be used to ensure higher levels of CRM in banks.

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

2019 ◽  
Vol 16 (1) ◽  
pp. 45
Author(s):  
Komang Redy Winatha
Keyword(s):  
Customer Relationship Management ◽  
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 as an Imperative for Academic Libraries

2015 ◽  
pp. 44-77
Author(s):  
Amanda Xu ◽  
Sharon Q. Yang
Keyword(s):  
Semantic Web ◽  
Customer Relationship Management ◽  
Academic Libraries ◽  
Linked Data ◽  
Trust Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Semantic Web Technologies ◽  
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 Knowledge Discovery in Database

2010 ◽  
pp. 902-907
Author(s):  
Jounghae Bang ◽  
Nikhilesh Dholakiam ◽  
Lutz Hamel ◽  
Seung-Kyoon Shin
Keyword(s):  
Knowledge Discovery ◽  
Relationship Marketing ◽  
Customer Relationship Management ◽  
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.

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

2019 ◽  
Vol 1 (3) ◽  
pp. 165-174
Author(s):  
Welda Welda ◽  
Bagus Baskara Sukarma ◽  
Emmy Febriani Thalib
Keyword(s):  
Customer Relationship Management ◽  
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.

Customer Relationship Management as an Imperative for Academic Libraries

2016 ◽  
pp. 1641-1674
Author(s):  
Amanda Xu ◽  
Sharon Q. Yang
Keyword(s):  
Semantic Web ◽  
Customer Relationship Management ◽  
Academic Libraries ◽  
Linked Data ◽  
Trust Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Semantic Web Technologies ◽  
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.

Data-Driven Customer Centricity

2015 ◽  
pp. 157-174
Author(s):  
Othman Boujena ◽  
Kristof Coussement ◽  
Koen W. de Bock
Keyword(s):  
Competitive Advantage ◽  
Customer Relationship Management ◽  
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.

Customer Relationship Management as an Imperative for Academic Libraries

2016 ◽  
pp. 395-428
Author(s):  
Amanda Xu ◽  
Sharon Q. Yang
Keyword(s):  
Semantic Web ◽  
Customer Relationship Management ◽  
Academic Libraries ◽  
Linked Data ◽  
Trust Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Semantic Web Technologies ◽  
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.

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