(Week 10) A04. Information System for CRM. Electronic Commerce Marketing

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1 (Week 10) A04. Information System for CRM Electronic Commerce Marketing Course Code: Course Name: Electronic Commerce Marketing Period: Autumn 2015 Lecturer: Prof. Dr. Sync Sangwon Lee Department: Information and Electronic Commerce University: WONKWANG WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p. 1 Contents 4.1. Outlines and Structure of CRM System 4.2. Analytical CRM System 4.3. Operational CRM System 4.4. Collaborative CRM System 4.5. Implementation of CRM System WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p. 2 1

2 4.1. Outlines and Structure of CRM System Functional Requirements of CRM System 1 Acquiring interactive data 2 Storing in data warehouse (DW) 3 Extracting useful information by analysis 4 Planning CRM activities 5 Developing CRM activities 6 Monitoring and analyzing performance WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p Outlines and Structure of CRM System Architecture of CRM System Concepts of system architecture Analytical CRM Factors to accumulate, manage, and analyze customer data Operational CRM Factors to plan and develop customer strategies Collaborative CRM Factors to support efficient and effective customer interactions Roles of system architecture Supporting systematic implementation guide for CRM strategies WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p. 4 2

3 Data Warehouse (DW) Characteristics of DW Subject orientation Subject-oriented data management in DW Relations among limited objects(e.g. table) Cf. data management in traditional applications Function/goods-oriented data management Relations among various objects Data integration Time variance Non-volatility WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p Analytical CRM System Data Warehouse (DW) (cont d) Data warehousing = Procedure to implement and manage DW Components of data warehousing Operational DB ETT(Extraction, Transformation, Transportation) DW Data application Data mart ODS(Operational Data Store) OLAP(On-Line Analytical Processing) Types of data warehousing Bottom-up Top-down WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p. 6 3

4 Data Mart Outlines of data mart Small department-unit subset of DW Comparisons of data mart vs. DW Viewpoint of system implementation Viewpoint of operation WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p Analytical CRM System Operational Data Store (ODS) Outlines of ODS DB that are implemented and managed for CRM Characteristics of ODS Extracting operational data from ODS Storing analytical data of CRM into ODS Considerations of ODS Technological compatibility of various software Work-relatedness of various works WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p. 8 4

5 On-Line Analytical Processing (OLAP) (cont d) Outlines of OLAP Multi-dimensional information analysis Comparisons of OLAP vs. OLTP(On-Line Transaction Processing OLAP Multi-dimensional information analysis OLTP Single-dimensional data handling(insert, update, delete, retrieve) Characteristics of OLAP Multi-dimensional information structure Direct access to data Information analysis through dialog query Effective decision-making supporting WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p Analytical CRM System On-Line Analytical Processing (OLAP) Data analysis methods of OLAP Multi-dimensional data analysis Heuristic data analysis Functions of OLAP Pivoting Filtering Reporting Slice & Dice Drilling (e.g. drill-up, drill-down, drill-across, drill-through) Types of OLAP MOLAP(Multi-dimensional OLAP) MDB(Multi-dimensional DB) with cube ROLAP(Relational OLAP) RDB(Relational DB) with star schema or snow-flake schema HOLAP(Hybrid OLAP) = MOLAP + ROLAP DOLAP(Desktop OLAP) WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p. 10 5

6 Data Mining Outline of data mining Procedure to extract practicable knowledge from data for decision-making Roles of data mining Association rule Sequential pattern Classification rule Clustering rule Prediction and Generalization rule Considerations of data mining Using high reliable data Analyzing suitable amounts of data WKU / Electronic Commerce Marketing / WKU-ECM-A04.pptx / Prof. Dr. SSL - IDEA+STEM+RF+FP+S+C+ LDV / p. 11 6

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