A strategic customer relationship management system: a hybrid OLAP-neural approach

K. C.M. Kwok, King Lun Tommy Choy, H. C.W. Lau, S. K. Kwok

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


Competitiveness is a key word for business success. Manufacturers face the challenge of offering customers high quality products and shorter delivery times while remaining cost-effective. In order to stay ahead of competitors, many enterprises are implementing strategic CRM programs in order to respond quickly to customer demands. Thus, modern enterprises are tending to move from a traditional CRM to an approach integrating other enterprise operational systems. This enables enterprises to improve business intelligence, make better decisions and enhance customer relations. In this paper, the design of a Strategic Customer Relationship Management System (SCRMS) is described which collects, integrates and diagnoses various customer-related data from different operation systems in departments within an enterprise. The proposed system aims to establish a cost-effective strategic CRM solution for achieving total customer satisfaction. It integrates the data warehouse concept with two emerging technologies, Online Analytical Processing (OLAP) and Artificial Neural Networks (ANNs), to support in the customer relation strategy. The system was applied in Ka Shui Manufactory Company Limited to support their customer relationship planning. The result shows that significant improvements were made in customer service efficiency and cost reduction.
Original languageEnglish
Pages (from-to)350-371
Number of pages22
JournalInternational Journal of Enterprise Network Management
Issue number4
Publication statusPublished - 1 Jan 2007


  • ANNs
  • Artificial Neural Networks
  • CRM
  • Customer Relationship Management
  • data warehouse
  • Enterprise Resource Planning
  • ERP
  • OLAP
  • Online Analytical Processing

ASJC Scopus subject areas

  • Business and International Management
  • Management Science and Operations Research
  • Management of Technology and Innovation


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