citibank

Succesvol, efficiënt en operationeel

management van de customer life-cycle

Une gestion efficace et performante du cycle

clients

Greet Maris

Head of CRM Group

Thierry Van de Merckt

Project Manager

24.03.09 IMAGIBRAINE

Agenda

( Citi in Belgium

( Vadis Consulting sa

( How to capture Client Life Cycle from prospect to mature

client?

( Methodology & Tools

( Making things operational

( Business Added Value

Citi in the world

( World-wide financial services company organized into :

� Institutional Client Group & Corporate

� Consumer Banking

� Global Wealth Management – Private Banking

� Global Cards Group

( Present in + 100 countries

( Servicing 200 million customer accounts

( By over 300.000 employees

Citi in Belgium

Customers : 560.000 Employees : 1500

Points of sales : 212

Consumer Banking with specific focus on consumer credit

Citi in Belgium

BIG 4

+ 85% market share

Challengers

Consumer Banking in Belgium

Who is VADIS

( VADIS Consulting sa/nv

� Founded Jan 2004, as the preferred analytic partner

of WDMLocated in WDM building, Brussels

� Focuses on the implementation of Analytical

Solutions

� Software development & innovation (45% of turnover

in R&D)

� 17 high level consultants and developers in this field

� Very active in B2B analytical world

� Consulting & Integration activity as well

Our objective is to leverage internal and external data for our

clients in order for them to gain a significant competitive

advantage, in terms of market expansion, enterprise

profitability and global risk reduction.

Agenda

( Citi in Belgium

( Vadis Consulting sa

( How to capture Client Life Cycle from prospect to mature

client?

Managing Customer Life Cycle

Anticipate

&

Manage

Understand

( From prospect …

How to keep a growing process, where more and more information can be captured,

where a lot different interactions and events will influence the life cycle,

still to be efficient and operational?

How to keep a growing process, where more and more information can be captured,

where a lot different interactions and events will influence the life cycle,

still to be efficient and operational?

to client … to your best client

Capture Consumer Life Cycle

InteractionsEvents

Events

Events

Events

Defining elements describing life cycle is joint effort from external data provider,

marketeer, product manager and sales person, so that there is a fertilization

cross interactions and cross product lines.

Defining elements describing life cycle is joint effort from external data provider,

marketeer, product manager and sales person, so that there is a fertilization

cross interactions and cross product lines.

Market Situation

Product Usage

Portfolio

Family / DemographicA

cqu

isit

ion

Capture Consumer Life Cycle

( Think big

( Variables are based on RFM+

( Data driven approach

� Data dictionary – clear definitions

� Data audit

� Updates and historisation

In order to allow industrialization of full process, based on this data driven approach,

important to have the data updated and historized.

In order to allow industrialization of full process, based on this data driven approach,

important to have the data updated and historized.

Agenda

( Citi in Belgium

( Vadis Consulting sa

( How to capture Client Life Cycle from prospect to mature

client?

( Methodology & Tools

Methodology & critical success factors

1.500 computed variables account for the life-cycle stage of each client.

It’s a generic container for all predictive models, analysis and contact strategy design.

1.500 computed variables account for the life-cycle stage of each client.

It’s a generic container for all predictive models, analysis and contact strategy design.

Client

360°

Data

RFM

Events

Dynamics

Contact/Channel

Family/Co-Holder

Socio-demo

Business

Knowledge

Bank Products

Bank Processes

Contact Strategy

Client Life cycle

Analytical data

Events are combined within models (not outside)

( Event X: Age in months of the measured event since last run

of the modelCARD_Cash_LastAge

63,598

3,091 2,701 2,819 4,536

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

-1 0 1+2 3+4+5 6+7+8+9+10+11

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

Frequency Fraction_of_Target

No event

Chance for a client

to be interested by

our offer

Number of clients

having the computed

characteristic

Events are combined within models (not outside)

( Event X: Age in months of the measured event since last run

of the model

CARD_Cash_LastAge

63,598

3,091 2,701 2,819 4,536

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

-1 0 1+2 3+4+5 6+7+8+9+10+11

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

Frequency Fraction_of_Target

4.8 more chance to sell Y if proposed within the month of event X

Events are captured from operational systems, transformed in the analytical datamart,

and used with 10 other variables to get a lift of 7.9 on the top 5% cases.

Events are captured from operational systems, transformed in the analytical datamart,

and used with 10 other variables to get a lift of 7.9 on the top 5% cases.

Lift of 4.8 on 4% of clients

No event

Contact Strategy is part of the picture…

( Nbre of Past Contacts: Not always what we expect…

Learning loop is not only for Reporting. Models can use those “feedback” measures

on contacts and channels as well. Depending on the target, it might give highly different

results.

Learning loop is not only for Reporting. Models can use those “feedback” measures

on contacts and channels as well. Depending on the target, it might give highly different

results.

W e a lt h _ N b _ c a m p a ig n

6 8 8 0 8 3

2 2 3 8 7 5 5 3 6 1 0 7 80

1 0 0 ,0 0 0

2 0 0 ,0 0 0

3 0 0 ,0 0 0

4 0 0 ,0 0 0

5 0 0 ,0 0 0

6 0 0 ,0 0 0

7 0 0 ,0 0 0

8 0 0 ,0 0 0

0 1 2 3 + 4 + 5 + 6

0 .0 %

1 .0 %

2 .0 %

3 .0 %

4 .0 %

5 .0 %

6 .0 %

7 .0 %

F r e q u e n c y F r a c tio n _ o f_ Ta rg e t

C re d it_N b _c am p aign

484599

16205 15410 1840850354

27843 24533 33843 17213 15459 2506 1981 3159 55710

100,000

200,000

300,000

400,000

500,000

600,000

0 1 2 3 4 5 6 7 8 9 10 11 12 13+ 14+ 15

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

1.8%

2.0%

F requenc y F rac tion_of_Targe t

( Socio-demo: Type of family

Socio_profession

0

10,000

20,000

30,000

40,000

50,000

60,000

WOR Other Values PEN EMP SEL NOP

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

Frequency Fraction_of_Target

Socio-Demo combines usage and family

3.5 more chance to sell Y to Class 3 than to major Class

Socio-demo provided by WDM allows to include personal & family factors in the picture.

It also creates a smooth transition from Prospects acquisition to Clients fertilization.

Socio-demo provided by WDM allows to include personal & family factors in the picture.

It also creates a smooth transition from Prospects acquisition to Clients fertilization.

Lift of 3.5 on 9% of clients

Class 3 Class 1 Class 2 Class 4 Class 6 Class 5

Methodology & critical success factors

People People

Risk Mitigation

Data Model &

Method

Client

360°

Data

RFM

Events

Dynamics

Contact/Channel

Family/Co-Holder

Socio-demo

Business

Knowledge

Bank Products

Bank Processes

Contact Strategy

Client Life cycle

Analytical data

Robust

Scalable

Modelling

Good design

No deploy crash

No black boxes

Good validation

Good recoding

Methodology & critical success factors

People People

Risk Mitigation

Data Model &

Method

The Scoring task

( Task:

� Based on past experience, find a number of typical green profiles allowing to build a reliable proximity measure for computing probability of interest…

( Problem:

� Profile depends a lot of the variables used: how to find the best ones among many?

� What makes a real (in a statistical sense) difference?

High probability Low probability Medium probability

Performance

Model

selection

Random

selection

Percentage of selected population

Percentage of target

identified

?????

Methodology & critical success factors

Client

360°

Data

RFM

Events

Dynamics

Contact/Channel

Family/Co-Holder

Socio-demo

Robust

Scalable

Modelling

Good design

No deploy crash

No black boxes

Good validation

Good recoding

Business

Validation

Processes

Biases

Scoring

Joined Offers

Business

Knowledge

Bank Products

Bank Processes

Contact Strategy

Client Life cycle

Analytical data

People People People People

Risk Mitigation

Data Model &

Method

Tool &

Method

( Dynamics: elapse time in months when customer acquired

product X

Card_Nb_Active_months_HP

2,671 2,750 1,696 1,202 1,872 1,502 2,013 1,399 1,406 1,448

203,138

0

50,000

100,000

150,000

200,000

250,000

1 2 3 4 5 6 7+8 9 10 11 12

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

Frequency Fraction_of_Target Poly. (Fraction_of_Target)

Business MUST be there!

Something happens here.

Business process: after 9 months stopped to be included in campaigns…

Business interpretation MUST be done to eliminate business bias…Business interpretation MUST be done to eliminate business bias…

Methodology & critical success factors

Client

360°

Data

RFM

Events

Dynamics

Contact/Channel

Family/Co-Holder

Socio-demo

Robust

Scalable

Modelling

Good design

No deploy crash

No black boxes

Good validation

Good recoding

Business

Validation

Processes

Biases

Scoring

Joined Offers

Business

Knowledge

Bank Products

Bank Processes

Contact Strategy

Client Life cycle

Analytical data

People People People People

Risk Mitigation

Data Model &

Method

Tool &

Method

Methodology & critical success factors

Client

360°

Data

RFM

Events

Dynamics

Contact/Channel

Family/Co-Holder

Socio-demo

Robust

Scalable

Modelling

Good design

No deploy crash

No black boxes

Good validation

Good recoding

Business

Validation

Processes

Biases

Scoring

Joined Offers

Industria-lization

Fast deploy

Reliable scores

Alarm attention

Watch Oldness

“All inclusive”

Business

Knowledge

Bank Products

Bank Processes

Contact Strategy

Client Life cycle

Analytical data

People People People People

Risk Mitigation

Data Model &

Method

Tool &

Method

Tool &

Method

Agenda

( Citi in Belgium

( Vadis Consulting sa

( How to capture Client Life Cycle from prospect to mature

client?

( Methodology & Tools

( Making things operational

Full Process

ExclusionExclusion

AnalyticalDatamart

Trusted_Repository_current

Exclusion_History

DirectMarketing

Campaign & response

Operational

External data

Propositions_current

PropositionsHistory

OptimizationOptimization P of C

Results_current

Results_History

Model 1

Model 2

Model X

…

Data Drift Score Drift

Control Process

Industrialization

Results_current

Model 1

Propositions_current

ExclusionExclusion

Trusted_Repository_current

OptimizationOptimizationModel 2

Model X

…

AnalyticalDatamart

DirectMarketing

P of C

Model Z

When the process needs to be scaled up, important that as much as possible is

parameterized. An automated control process for the correctness of the models needs

to be in place with only manual intervention when required.

When the process needs to be scaled up, important that as much as possible is

parameterized. An automated control process for the correctness of the models needs

to be in place with only manual intervention when required.

Agenda

( Citi in Belgium

( Vadis Consulting sa

( How to capture Client Life Cycle from prospect to mature

client?

( Methodology & Tools

( Making things operational

( Business Added Value

Business Added Value

Product C

X-sell

1

1

1Product A

Product B

Business Added Value

Retention

Product C

X-sell

1

2

2Product A

Product B

Upsell

Business Added Value

1 1

1

Upsell Retention

Product C

X-sell

1

3

3Product A

Product B

Business Added Value

1 1

1

Upsell Retention

Product C

X-sell

1

4

4Product A

Product B

Business Added Value

1 1

1

Upsell Retention

Product C

X-sell

1

4

4Product A

Product B

1) Direct Marketing strategy (product driven) is adapted according to propensity

to buy scores, by doing the right offer, increasing your response and

this at the right time, maintaining your contact capital.

1) Direct Marketing strategy (product driven) is adapted according to propensity

to buy scores, by doing the right offer, increasing your response and

this at the right time, maintaining your contact capital.

?????

Conclusion

Manage life cycle

of your customer

Better targetting

Optimize contact strategy

and

Improve profitability