In this article, the authors develop a conceptual framework for customer relationship management (CRM) that helps broaden the understanding of CRM and its role in enhancing customer value and, as a result, shareholder value. The authors explore definitional aspects of CRM, and they identify three alternative perspectives of CRM. The authors emphasize the need for a cross-functional, process-oriented approach that positions CRM at a strategic level. They identify five key cross-functional CRM processes: a strategy development process, a value creation process, a multichannel integration process, an information management process, and a performance assessment process. They develop a new conceptual framework based on these processes and explore the role and function of each element in the framework. The synthesis of the diverse concepts within the literature on CRM and relationship marketing into a single, process-based framework should provide deeper insight into achieving success with CRM strategy...

https://www.jstor.org/stable/pdfplus/30166559.pdf

A Strategic Framework for Customer Relationship Management

This article examines Customer Relationship Management, or CRM, from the perspective of strategy formulation and implementation. We commence by reviewing the origins and role of CRM and highlighting the importance of adopting a cross-functional approach to CRM strategy formulation. We examine alternative approaches to CRM strategy development and, using an 'interaction research' approach, propose a model that addresses both CRM strategy and implementation. We identify four critical implementation components of a successful CRM programme and examine these in the context of five key cross-functional CRM processes. The model structure is used to help identify a research agenda

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Customer Relationship Management: from Strategy to Implementation

A Strategic Framework for CRM The Strategy Development Process The Value Creation Process The Multi-Channel Integration Process The Information Management Process The CRM Performance Assessment Process Organising for CRM Implementation Chapter references CRM reading list

Handbook of CRM : archieving excellence in customer management

Purpose – Managing enterprise performance is an important, yet a difficult process due to its complexity. The process involves monitoring the strategic focus of an enterprise, whose performance is measured from the analysis of data generated from a wide range of interrelated business activities performed at different levels within the enterprise. This study aims to investigate management data systems technologies in terms of how they are used and the issues that are related to their effective management within the broader context of enterprise performance management (EPM).Design/methodology/approach – A range of recently published research literature on data warehousing, online analytic processing and EPM is reviewed to explore their current state, issues and challenges learned from their practice.Findings – The findings of the study are reported in two parts. The first part discusses the current business practices of these technologies, and the second part identifies and discusses the issues and challeng...

Understanding management data systems for enterprise performance management

Improved method of and apparatus for aggregating data elements in multidimensional databases (MDDB). In the preferred embodiment, the apparatus is realized in the form of a high-performance stand-alone (i.e. external) aggregation server which can be plugged-into conventional MOLAP systems to achieve significant improvements in system performance. In accordance with the principles of the present invention, the stand-alone aggregation server contains a scalable MDDB and a high-performance aggregation engine that are integrated into the modular architecture of the aggregation server. The stand-alone aggregation server of the present invention can uniformly distribute data elements among a plurality of processors, for balanced loading and processing, and therefore is highly scalable.

Data aggregation module supporting dynamic query responsive aggregation during the servicing of database query requests provided by one or more client machines

I. FUNDAMENTAL COMMITMENTS. 1. Basic Data Warehousing Distinctions. An Architecture, Not A Product. The One Fundamental Question. The One Question-The Thousand and One Answers.... The First Distinction: Transaction and Decision Support System. Data Warehouse Sources of Data. Dimensions. The Data Warehouse Fact. The Data Warehouse Model of the Business: Alignment. The Data Cube. Aggregation. Data Warehouse Professional Roles. The Data Warehouse Process Model. Summary. 2. A Short History of Data. In the Beginning.... Fast Forward to Modern Times. The Very Idea of Decision Support. From Mainframes To PCs. The Promise of the Relational Database. Data Every Which Way. From Client-Server to Thin Client Computing. Why Will Things Be Different This Time? The More Things Change, the More They Stay the Same. Model of Technology Dynamics. Summary. 3. Justifying Data Warehousing. Competition for Limited Resources. An Integrated Business and Technology Solution. Economic Value, Not Business Benefits. Selling the Data Warehouse. The Reporting Data Warehouse: Running Fewer Errands. The Supply Chain Warehouse. The Cross-Selling Warehouse. The Total Quality Management Data Warehouse. The Profitability Warehouse. Data Warehousing Case Vignettes in the Press. Summary. 4. Data Warehousing Project Management. Simulating a Rational Design Process. Managing Project Requirements. Managing the Development of Architecture. Managing Project Schedule. Managing Project Quality. Managing Project Risks. Managing Project Documentation. Managing the Project Development Team. Managing Project Management. Summary. II. DESIGN AND CONSTRUCTION. 5. Business Design: The Unified Representations of The Customer and Product. The Critical Path: Alignment. A Unified Representation of the Customer. Data Scrubbing. The Cross-Functional Team. Hierarchical Structure. Customer Demographics. A Unified Representation of the Product. Data Marts: Between Prototype and Retrotype. Summary. 6. Total Data Warehouse Quality. The Information Product. Data Quality as Data Integrity. Intrinsic Qualities. Ambiguity. Timeliness and Consistency in Time. Security. Secondary Qualities. Credibility. Quality Data, Quality Reports. Information Quality, System Quality. Performance. Availability. Scalability. Functionality. Maintainability. Reinterpreting the Past. Summary. 7. Data Warehousing Technical Design. Use case Scenarios. Abstract Data Types and Concrete Data Dimensions. Data Normalization: Relevance and Limitations. Dimensions and Facts. Primary and Foreign Keys. Design for Performance: Technical Interlude. Summary. 8. Data Warehouse Construction Technologies: SQL. The Relational Database: A Dominant Design. Twelve Principles. Thinking in Sets: Declarative and Procedural Approaches. Data Definition Language. Indexing: B-Tree. Indexing: Hashing. Indexing: Bitmap. Indexing Rules of Thumb. Data Manipulation Language. Data Control Language. Stored Procedures. User-Defined Functions. Summary. 9. Data Warehouse Construction Technologies: Transaction Management. The Case For Transaction Management: The ACID Test. The Logical Unit of Work. Two-tier and Three-tier Architectures. Distributed Architecture. Middleware: Remote Procedure Call Model. Middleware: Message-Oriented Middleware. The Long Transaction. Summary. III. OPERATIONS AND TRANSFORMATIONS. 10. Data Warehouse Operation Technologies: Data Management. Database Administration. Backing Up the Data (in the Ever-Narrowing Backup Window). Recovering the Database: Crash Recovery. Recovering the Database: Version (Point-in-Time) Recovery. Recovering the Database: Roll-Forward Recovery. Managing Lots of Data: Acres of Disk. Managing Lots of Data: System-Controlled Storage. Managing Lots of Data: Automated Tape Robots. RAID Configurations. Summary. 11. Data Warehousing Performance. Performance Parameters. Denormalization for Performance. Aggregation For Performance. Buffering For Performance. Partitioning For Performance. Parallel Processing: Shared Memory. Parallel Processing: Shared Disk. Parallel Processing: Shared Nothing. Data Placement: Colocated Join. Summary. 12. Data Warehousing Operations: The Information Supply Chain. A Process, Not an Application. The Great Chain of Data. Partitioning: Divide and Conquer. Determining Temporal Granularity. Aggregate Up To the Data Warehouse. Aggregates in the Data Warehouse. The Debate about the Data Warehouse Data Model. The Presentation Layer. Integrated Decision Support Processes. Summary. 13. Metadata and Metaphor. Metaphors Alter Our Perceptions. A New Technology, a New Metaphor. Metadata are Metaphorical. Semantics. Forms of Data Normalization and Denormalization. Metadata Architecture. Metadata Repository. Models and Metamodels. Metadata Interchange Specification (MDIS). Metadata: A Computing Grand Challenge. Summary. 14. Aggregation. On-line Aggregation, Real-Time Aggravation. The Manager's Rule of Thumb. A Management Challenge. Aggregate Navigation. Information Density. Canonical Aggregates. Summary. IV. APPLICATIONS AND SPECULATIONS. 15. OLAP Technologies. OLAP Architecture. Cubes, Hypercubes, and Multicubes. OLAP Features. The Strengths of OLAP. Limitations. Summary. 16. Data Warehousing and the Web. The Business Case. The Web as a Delivery System. Key Internet Technologies. Web Harvesting: The Web as the Ultimate Data Store. The Business Intelligence Portal. Summary. 17. Data Mining. Data Mining and Data Warehousing. Data Mining Enabling Technologies. Data Mining Methods. Data Mining: Management Perspective. Summary. 18. Breakdowns: What Can Go Wrong. The Short List. The Leaning Cube of Data. The Data Warehouse Garage Sale. Will the Future be Like the Past? Model Becomes Obsolete. Missing Variables. Obsessive Washing. Combinatorial Explosion. Technology and Business Misalignment. Becoming a Commodity. Summary. 19. Future Prospects. Enterprise Server Skills to be in High Demand. The Cross-Fictional, Oops, -Functional Team. Governance. The Operational Data Warehouse. Request for Update. The Web Opportunity: Agent Technology. The Future of Data Warehousing. Summary. Glossary. References. Index.

Lou Agosta

Papers

The Essential Guide to Data Warehousing