Presentation in public workshop 1 on 11 June,2001

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 Introductionby Prof. Chan Chi Fai


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

Page I1Page I1

The Studyby Prof. Lau Kin Nam


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

Page S1Page S1The Chinese University of Hong Kong

Bank Marketing ObjectivesBank Marketing Objectives

• New customer acquisition

• Cross-selling / up-selling

• Increase utilization

• Customer retention

• Win-back


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


CRM FundamentalsCRM Fundamentals

• Customer Focus

• Speed

• Technology

– Selling by Information

– Selling by Relationship

– Selling by Automation


Major Types of SellingMajor Types of Selling


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


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


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

Customer Survey

fusionupdate

Phase 3

Validation

Externaldatabases

Analytical and statistical models

Phase 2:Data Cleansing

Page S8Page S8

Example:Example:

• Address standardization:Unformatted: 4/F., K.K. Leung Bldg., ShaTin, N.T.

Formatted: Room no.

Floor

Building

Street

District


Example:Example:

• Name standardization:Unformatted: Andrew C.F. Chan

Formatted: Last Name First Name Christian

Name

BACKBACK


Example:Example:

• Mr. Chan Tai ManSex : M

Age : 32

Tel. : 26096000

Address : 25, 5/F., CRM building, Sha Tin, NT.

• Miss Lee Mei LaiSex : F

Age : 28

Tel. : 26096000

Address : 25, 5/F., CRM building, Sha Tin, NT.

CoupleCouple

BACKBACK


Example:Example:

• Job Classification

• Address Classification

• Life-stage Classification

• Credit Card Merchant Classification

• SME Classification

BACKBACK


Example:Example:

• Census

• Property transaction database

• CRE (Central Registration Establishment)

• TDC (Trade Development Council)

BACKBACK


Survey:Survey:

• Contact information– Phone, Business address, Email, Website

• Updated demographics– Marital Status, Number of dependants, Spouse’s information

• Socio-economics– Job, Income, Property ownership, Car ownership

• Product Interests– UT– Insurance– Deposit

Yes No

Yes No

Yes No

BACKBACK


Enrichment Examples:Enrichment Examples:

Signal Enrichment variables:

ATM withdrawal fromrace-course

Gambling

ISP payment from creditcard

Internet user

School payment Age and number of kids

Change of address Buy/sell property

BACKBACK


Age : 29MS : MarriedEdu level : CollegeIndustry : BankingAddress : 6B, Blk. 2,

Island Harborview

Income(?) : $35,000 ± 2000

Example:Example:

Age : 30MS : MarriedEdu level : CollegeIndustry : BankingAddress : 3A, Blk. 5,

Island Harborview

Income : $35,000Page S16Page S16

Integrated DatabaseIntegrated Database

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

Channel e.g. Branch, ATM, Phone, Internet

Household Information

e.g. Household Income, Numbers/Age of dependants, Spouse information

RelationshipVariables

e.g. Overall tenure, product tenure, past profitability, No. of product

Product Ownership/Usage e.g. RFM (card), Deposit, Loan, UT, Insurance

Behavioral variables e.g. Gambling, Travel, Degree of Luxury, Life-style, Risk attitude

BACKBACK

Page S17Page S17

Phase 2

Integrated Databaseready for mining

Marketing campaigns

Customerand productsegmentation

Newcustomersanalysis

AttritionAnalysis

SWOTon each customersegment and product

1.Acquisition criterion2.Increase utilization

Attritionpattern and signals

Targeting,positions,pricing,bundling

Customer based selling1. Cross selling opportunity2. Channel3. CLV and ROI

Cross selling by branch, phone, internet

Customerretention

Extractinternaland external signals

Eventdrivenselling

Cross Selling PlanStrategicMarketing Plan

OLAP (query)

Predictionmodel

Next partPhase 3:Data Mining

Page S18Page S18

Segmentations:Segmentations:

2. Prizm scheme = Lifestage Address class

Single MarriedMarriedwith kids

Retired

Public

Private

Luxuryhousing

1. By occupation


Segmentations:Segmentations:

3. by shareholders variables

Low Medium High

New

1 – 3

3+

Tenure

Profitability

BACKBACK


Credit CardDeposit

Current and Past Value

Name : Liu Wai ChuenAge : 24Sex : MaleEdu : College

MortgageUT

Future Value

Current Basket

Future Basket

Page S21Page S21

BACKBACK

Past Value

Future Value

High

Low

HighLow

Let go/stay Retention / Loyalty Program

De-marketingAggressive

selling


Campaign Management:Campaign Management: BACKBACK

Potential Customers in the Database

Adopters Non-Adopters

SelectedSelectedPast Campaign Results

Page S23Page S23

Data Capturing

Data Retrieval

Data Analysis

Data Application

Sales Execution and Automation

CRM System:CRM System:


A Comparison: 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


Future CRM Direction Future CRM Direction ————

1. Customer Driven Organization1. Customer Driven Organization

Customer

Segment 1 Segment 2 Segment 3

Customer manager

Value Equity Officer

(Price, Convenience,

Quality)

Retention Officer

(Loyalty Program, Building

Relationship)

Brand Equity Officer

(Brand Awareness)

Page S26Page S26Modified from: Driving Customer Equity, (Rust Zeithaml Lemon), 2001

Future CRM Direction Future CRM Direction ————

2. Product and Service Diversification2. Product and Service Diversification

Banking Products


End of PresentationEnd of Presentation

*There would be a Q & A session after the coffee break.


Venue : 1/F Foyer

Time : 10:45 a.m.– 11:15 a.m.

Coffee BreakCoffee Break


Presentation in public workshop 1 on 11 June,2001

Documents

Transcript of Presentation in public workshop 1 on 11 June,2001