Bank Marketing Objectives:

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Data Fusion, Data Mining, and Decision Support
System 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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The Introduction
by Prof. Chan Chi Fai
The Chinese University of Hong Kong
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Introduction

• CUHK research project supported by
• 0.7M Strategic Research Fund, CUHK
• 3.5M Innovation and Technology Fund
• from Industry Department of SAR
• Hong Kongs 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

The Chinese University of Hong Kong
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The Study
by Prof. Lau Kin Nam
The Chinese University of Hong Kong
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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 Objectives

• New customer acquisition
• Cross-selling / up-selling
• Increase utilization
• Customer retention
• Win-back

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Marketing Technology in Information Era
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CRM Fundamentals

• Customer Focus
• Speed
• Technology
• Selling by Information
• Selling by Relationship
• Selling by Automation

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Major Types of Selling

• Active Selling
• Event Triggered Selling
• Mortgage, Personal Loan
• Product Based Selling
• Campaign Management

• Passive Selling
• Customer Based Selling
• By Branch
• By Phone
• By Internet

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Phase 1 Internal Data Capturing Process
CRM Roadmap
Employers
Autopay
Salary
Purchases
Purchases
Banks Internet Mall
Merchants
Retail Customers
Demographics, banking transaction
Browsing data
Card data
Banks Raw Database
Banks Raw Database
Phase 2
Phase 2
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In-house data
BACK

• Types
• Product Usage Data
• Demographics
• Socio-economics
• Transactional Data
• Credit Card
• EPS
• PPS
• Autopay/payroll
• MPF
• Channel Data

• Problems
• Outdated
• Incomplete
• Isolated

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Phase 2 Data Cleansing
Standardization of data and format
Identification of household relationship
Various classification schemes to convert data
to useful information
Enriched Database
External databases
fusion
update
Customer Survey
Solving missing value problems
Analytical and statistical models
Validation
Phase 3
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Example

• Address standardization
• Unformatted 4/F., K.K. Leung Bldg., ShaTin, N.T.
  
• Formatted Room no.
• Floor
• Building
• Street
• District

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Example
BACK

• Name standardization
• Unformatted Andrew C.F. Chan
• Formatted
• Last Name First Name Christian
  Name

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Example
BACK

• Mr. Chan Tai Man
• Sex M
• Age 32
• Tel. [phone]
• Address 25, 5/F., CRM building, Sha Tin,
• NT.

• Miss Lee Mei Lai
• Sex F
• Age 28
• Tel. [phone]
• Address 25, 5/F., CRM building, Sha Tin,
• NT.

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Example
BACK

• Job Classification
• Address Classification
• Life-stage Classification
• Credit Card Merchant Classification
• SME Classification

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Example
BACK

• Census
• Property transaction database
• CRE (Central Registration Establishment)
• TDC (Trade Development Council)

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Survey
BACK

• Contact information
• Phone, Business address, Email, Website
• Updated demographics
• Marital Status, Number of dependants, Spouses
  information
• Socio-economics
• Job, Income, Property ownership, Car ownership
• Product Interests
• UT
• Insurance
• Deposit

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Enrichment Examples
BACK
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Example
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Integrated Database
BACK
Category
Variables
Contact Information
e.g. Address, phone (Business/home), E-mail,
website
Demographics
e.g. Age, sex, marital status, Life stage
Socio-economics
e.g. Income, job, education, property ownership,
car ownership, social class
Household Information
e.g. Household Income, Numbers/Age of
dependants, Spouse information
Relationship Variables
e.g. Overall tenure, product tenure, past
profitability, No. of product
Product Ownership/ Usage
e.g. RFM (card), Deposit, Loan, UT, Insurance
Channel
e.g. Branch, ATM, Phone, Internet
Behavioral variables
e.g. Gambling, Travel, Degree of Luxury,
Life-style, Risk attitude
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Next part
Phase 3 Data Mining
OLAP (query)
Customer and product segmentation
New customers analysis
Attrition Analysis
Integrated Database ready for mining
Marketing campaigns
Prediction model
Customer based selling 1. Cross selling
opportunity 2. Channel 3. CLV and ROI
SWOT on each customer segment and product
1.Acquisition criterion 2.Increase
utilization
Attrition pattern and signals
Cross selling by branch, phone, internet
Customer retention
Extract internal and external signals
Targeting, positions, pricing, bundling
Event driven selling
Strategic Marketing Plan
Cross Selling Plan
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2. Prizm scheme Lifestage ? Address class
Segmentations
1. By occupation
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3. by shareholders variables
Segmentations
BACK
Profitability
Tenure
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Name Liu Wai ChuenAge 24Sex MaleEdu
College
Current Basket
Future Basket

• Credit Card
• Deposit

Mortgage UT
Current and Past Value
Future Value
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BACK
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Campaign Management
BACK
Potential Customers in the Database
Selected
Selected
Past Campaign Results
Adopters
Non-Adopters
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CRM System
Data Capturing
Data Retrieval
Data Analysis
Data Application
Sales Execution and Automation
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A Comparison

• Before CRM
• Selling a good product by
• Advertising
• Personal Selling
• Product Based Selling

• After CRM
• Selling a good product by
• Information
• Relationship
• Automation
• Customer Based Selling

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Future CRM Direction 1. Customer Driven
Organization
Customer
Segment 1
Segment 2
Segment 3
Customer manager
Retention Officer (Loyalty Program, Building
Relationship)
Brand Equity Officer (Brand Awareness)
Value Equity Officer (Price, Convenience, Quality)
Modified from Driving Customer Equity, (Rust
Zeithaml Lemon), 2001
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Future CRM Direction 2. Product and Service
Diversification
Information Service Middleman
Non-Banking Products
Banking Products
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End of Presentation
There would be a Q A session after the coffee
break.
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Coffee Break
Venue 1/F Foyer Time 1045 a.m. 1115 a.m.
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