elisabete silva sogrupo - sascommunity · 1 elisabete silva, henrique nicola sogrupo - si cgd group...

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Elisabete Silva, Henrique NicolaSOGRUPO - SICGD GROUP

[email protected]@cgd.pt

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Agenda

The requirement...The requirement...

Data Mining as part of Business IntelligenceData Mining as part of Business Intelligence

Data Mining projects at CGD BankData Mining projects at CGD Bank

Next / current projects…Next / current projects…

ConclusionsConclusions

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Don Tapscott & Art Caston, em “Paradigm Shift”

“A pressing reality of the new global environment is theemergence of a new era of competition.

Competition is arising not only from traditional adversariesin traditional markets, or from new entrants to a specific

industry or economic sector, but also from disintegration of barriers to previously insulated and protected markets.”

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Some of the main goals for CGD are:

to be “Customer Oriented”predict Customer needssolve Customer problems... not just “answer their questions”

To satisfy these goals it’s necessary:

to improve Knowledge about Customers (using Data Mining)to invest in Data Quality Projects (to have correct information)

The highest priorities for CGD Group are:

Increase Profitability (Customers, Transactions, Products,...)Decrease Risk (Customers, Credit products,...)

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Business Needs: Improving Information Business Needs: Improving Information Quality of CGD CustomersQuality of CGD Customers

One of the objectives of DMK credit cards area is to increase the number of its customers. The objective was to identify the customer segment at which marketing and sales actions were to be particularly targeted.

An analysis of the profile of credit card customers may help to refine the definition of customers at whom a campaign should be targeted and the most suitable approach to be made – reducing campaign costs and increasing its efficiency.

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Communications platform

DB2

LANWindows 95

NT Server

TCP/IPSNA

Data Marts

OLAP

Data Mining

Ad-Hoc Queries,Reporting

• Temporary Data• WEB Server• Data Mining Server• DB2 Connect Server• ...

Data Mining as a part of Business IntelligenceData Mining as a part of Business Intelligence

Information System

DB2

Data Marts

External Data

DW

OS390 Servers

Operational Systems

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«Mastering Data Mining»«The art and science of customer relationship management»

Michael J. A. BerryGordon S. Linoff

“Data Mining is the process of exploration and analysis, by automatic or semiautomatic means, of large quantities ofdata in order to discover meaningful patterns and rules.”

Data Mining Data Mining -- What is it?What is it?

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Data Mining is the process used to select, exploit and model large volumes of data with a view to identifying previously unknown consistent patterns or systematic relationships between variables, with a view to generating major value to an organisation’s business oractivities.

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Data Mining Data Mining -- What is it?What is it?Name Age Scoring Own

Credit CardJohnMaryJuliaPamelaMichaelTomGeorgeCindy

6586455535826542

1227293632474349

YNNYYNNY

Y

Y

YYN

N N

N

10 20 30 40 50Scoring

20

40

60

80

Age

Scoring10 20 30 40 50

20

40

60

80

Age

Y

Y

Y

Y

N

N

N

N

Scoring10 20 30 40 50

20

40

60

80

Y

Y

Y

Y

N

N

N

N

Age

10


10 20 30 40 5020

40

60

80

Y

Y

Y

Y

N

N

N

N

Scoring

Age

Scoring >=20

Age < 60 Age >= 60

Scoring<30

Scoring >=30

Scoring <20

YN Y N

Scoring10 20 30 40 50

20

40

60

80

Y

Y

Y

Y

N

N

N

N

Age

10 20 30 40 5020

40

60

80

Y

Y

Y

Y

N

N

N

N

Age

Scoring

Decision Tree

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Initial Work Initial Work -- Extraction and Analysis of Data Extraction and Analysis of Data QualityQuality

Definition of data set to be analysed;

Extraction of data required for the defined analyses from legacy systems (operational systems);

Loading of extracted data in SAS®;

Definition of rules for the preparation of data and new files;

Analysis and validation of data.

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Work Performed Using SAS Enterprise Work Performed Using SAS Enterprise MinerMinerTMTM

Training on use of tool;

Installation of tool on NT server and team PC workstations;

Study of profile of CGD customers: differentiating characteristics between credit card and non credit card customers

Creation of a credit card acquisition propensity model (standard model);

Creation of a “classic” (i.e. standard) credit card acquisition propensity model (Classic model);

Creation of a gold credit card acquisition propensity model (Gold model);

Creation of a gold credit card acquisition propensity model for classic credit card customers (upselling model);

Creation of customers segments.

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Several Tool OutputsSeveral Tool Outputs

AGE on 30 June 1999

Credit Card Indicator

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Cluster 5Cluster 5

Cluster 1Cluster 1

Cluster 0Cluster 0

Cluster 3Cluster 3

Cluster 6Cluster 6Cluster 2Cluster 2Cluster 4Cluster 4

InactiveInactive Traditional SaversTraditional Savers

Active InvestorsActive Investors

TopTop--toptopHigh SpendersHigh Spenders

BorrowersBorrowersDebit Card UsersDebit Card Users

Cluster 0 – Inactive (medium %)

low asset levels - 57% of customers have less than PTE 100,000 in investments;

only 4% have residential mortgage loans and only 2% have taken out personal loans;

customers with weak ties to CGD, having little more than a current account.

Cluster 1 – Traditional savers (medium %)

39% of customers are retired and 18% are middle age (50-65);

few of these customers (3% and 2% respectively) use debit and credit cards;

99% of these customers have more than PTE 200,000 in Deposits and 37% have more than PTE 2,000,000;

they do not use Credit products;they prefer term deposits – more traditional customers

– 76% have more than one term deposit account;4% are classified as preferred customers.

Cluster 2 – Active Investors (low %)

around 66% of customers are over 50;98% have deposits amounts more than PTE

2,000,000;these customers do not use credit but have

savings accounts, some financial products ranging from the most traditional to the most sophisticated.

Cluster 3 – Top-top (very low%)

45% of customers are middle aged (50-65);94% have deposits amounts more than PTE

2,000,000;83% of these customers have more than one current

account and 22% have overdrafts;these customers are investors - 68% have

securities portfolio, 61% have one or more term deposit accounts and 56% have savings accounts;

86% of these customers have taken out a residential mortgage loan;

18% are classified as preferred customers;these are sophisticated, investing customers who

make use of a wide range of the bank’s products.

Cluster 4 – Debit Cards Users (low%)82% of these customers have investments of less

than PTE 200,000 although they have savings accounts;

they are transactional customers – 11% areCaixaDirecta subscribers, 79% have debit cards and 34% have credit cards.

Cluster 5 – High Spenders (low %)most of these customers are aged between 25-65 and

belong to the working population (82%);these customers have high credit levels and do not

make investments - 6% have recorded payment defaults;

these are customers with a high level of credit product propensity – 91% have taken out residential mortgage loans, 25% have taken out personal loans and 24% have overdrafts.

Cluster 6 – Borrowers (low %)these are customers with loan product propensity -

76% have taken out residential mortgage loans, 31% have taken out personal loans and 26% have overdrafts;

these customers are moderate in their use of credit- the payment default rate is not particularly high.

Results by SegmentResults by Segment

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Cluster 5Cluster 5

Cluster 1Cluster 1

Cluster 0Cluster 0

Cluster 1Cluster 1

Cluster 0Cluster 0

Cluster 3Cluster 3

Cluster 6Cluster 6Cluster 2Cluster 2Cluster 4Cluster 4

InactiveInactive Traditional SaversTraditional Savers

Active InvestorsActive Investors

TopTop--toptopHigh SpendersHigh Spenders

BorrowersBorrowersDebit Card UsersDebit Card Users


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Marketing Campaign


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Extraction from DW corporate customers’ data

Add external data to improve information about these customers

Apply Data Mining techniques to discover and understand customers’ profile: “What’s the profile of a customer that has purchase a corporate product?”

Creation of a Global Industry Offer acquisition propensity model

Credit customers propensity model

Deposits customers propensity model

Potential customers propensity model

Work done in a Corporate Customers’ Project

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SAS Enterprise Miner screen shot:


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Have improved knowledge about corporate customers;

Understand the profile of some corporate customers; the characteristics of customers that have bought the Industry Offer Global product;

Understand the characteristics of customer segments according to the products that they have: current accounts, credits, insurance, ...

Now, CGD Group:


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SAS modules at CGD

SAS ACCESS to DB2

SAS BASE SAS EIS

SAS Enterprise MinerSAS Enterprise Miner

SAS CONNECTSAS CONNECT

NT ServerLANs

Windows 95 e NT

TCP/IPSNA

DB2

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Conclusions

The quality of information obtained on CGD customers was highly relevant to CGD’s Marketing Department;

It was shown that the use of a data mining tool - SAS Enterprise MinerTM - made it possible to obtain fresh information of vital business interest;

Much of the project’s success was, in part, based on the multi-disciplinary (IT and business) composition of the project team.

The rightinformationto the rightpersonat the right

Therightdecisionat the

time right time

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Data Mining with the Electronic Channels Department;

The 4th iteration of Data Warehouse is about Risk and Channel Analysis...

The 5th iteration of Data Warehouse is about Profitability...

Goal: a consistent approach to customer servicing, independent of channel utilised.

Next / current Projects...Next / current Projects...

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Click ThroughClick From

Clickstream Analysis

www.cgd.pt

caixadirecta.cgd.ptwww.fidelidade.pt

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Customer Churn (Attrition)

Customer Retention

Customer Acquisition

Customer Profitability

Targeted MarketingCross Selling

Market-basket analysis

Upgrading or Upselling

Fraud Detection

Channel Management

Risk AnalysisAttributing of Credit Limits

Web Mining

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Data Marts

elisabete silva sogrupo - sascommunity · 1 elisabete silva, henrique nicola sogrupo - si cgd group...

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