Data Fusion, Data Mining, and Decision Data Fusion, Data Mining, and Decision Support System: Support System: Bank Marketing in the 21st Century Bank Marketing in the 21st Century Prof. Chan Chi Fai, Department of Marketing Prof. Lai Siu King, Department of Decision Science and Economics Prof. Lau Kin Nam, Department of Marketing Prof. Leung Kwong Sak, Department of Computer Science and Engineering Prof. Leung Pui Lam, Department of Statistics Prof. Leung Yee, Department of Geography The Chinese University of Hong Kong 11 June 2001
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Data Fusion, Data Mining, and Decision Support System: Data Fusion, Data Mining, and Decision Support System:
Bank Marketing in the 21st CenturyBank Marketing in the 21st Century
Prof. Chan Chi Fai, Department of MarketingProf. Lai Siu King, Department of Decision Science and Economics
Prof. Lau Kin Nam, Department of MarketingProf. Leung Kwong Sak, Department of Computer Science and Engineering
Prof. Leung Pui Lam, Department of StatisticsProf. Leung Yee, Department of Geography
The Chinese University of Hong Kong
11 June 2001
The Chinese University of Hong Kong
The Introductionby Prof. Chan Chi Fai
The Chinese University of Hong Kong
Introduction: Introduction:
• CUHK research project supported by :
• 0.7M Strategic Research Fund, CUHK
• 3.5M Innovation and Technology Fund from Industry Department of SAR
• Hong Kong’s first prominent academic/business cooperation on
design and implementation of Customer Relationship
Management system for financial institutions
• A major Bank in Hong Kong participated as industry partner to
provide data for pilot system implementation since Jan 99
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The Studyby Prof. Lau Kin Nam
The Chinese University of Hong Kong
Contents:Contents:
• Bank Marketing Objectives• Marketing Technology in the Information Era• CRM fundamentals• Major types of Selling• CRM Roadmaps
– Phase 1 : Data Capturing– Phase 2 : Data Cleansing– Phase 3 : Data Mining Applications
• CRM System• Future CRM Directions
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Bank Marketing ObjectivesBank Marketing Objectives
• New customer acquisition
• Cross-selling / up-selling
• Increase utilization
• Customer retention
• Win-back
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Technology Description
Data Farming Design data capturing sytem
Data Warehouse Enhance data retrieval
Data Cleansing and FusionConvert data into meaningfulinformation
Data MiningRecover hidden knowledge fromthe database
Database MarketingApply data mining results toimprove sales/efficiency
Sales Automation e.g. Siebel
Marketing Technology in Marketing Technology in Information EraInformation Era
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CRM FundamentalsCRM Fundamentals
• Customer Focus
• Speed
• Technology
– Selling by Information
– Selling by Relationship
– Selling by Automation
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Major Types of SellingMajor Types of Selling
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Passive Selling• Customer Based Selling
– By Branch– By Phone– By Internet
Active Selling• Event Triggered Selling
– Mortgage, Personal Loan
• Product Based Selling– Campaign Management
CRM RoadmapCRM Roadmap
Bank’sRaw Database
PurchasesPurchases
Employers
Retail Customers
Bank’s Internet Mall
Merchants
Bank’sRaw Database
Salary
Autopay
Demographics, banking transaction
Phase 2
Browsing data
Card data
Phase 2
Phase 1:Internal Data Capturing Process
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In-house dataIn-house data
A. Types:– Product Usage Data– Demographics– Socio-economics– Transactional Data
• Credit Card• EPS• PPS• Autopay/payroll• MPF
– Channel Data
B. Problems:• Outdated• Incomplete• Isolated
BACKBACK
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Phase 1
Solving missing value problems
EnrichedDatabase
Standardization of data and format
Identification of household relationship
Various classification schemes to convert data to useful information