1. Greet Maris Thierry Van de Merckt
Head of CRM Group Project Manager
Succesvol, efficiënt en operationeel
management van de customer life-cycle
Une gestion efficace et performante du cycle
clients
24.03
.09 IM
A GIBR
AINE

2. 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

3. 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

4. Citi in Belgium
Consumer Banking with specific focus on
consumer credit
Customers : 560.000 Employees : 1500
Points of sales : 212

5. Citi in Belgium

6. Consumer Banking in Belgium
BIG 4
+ 85% market share
Challengers

7. Who is VADIS
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.
( 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

8. Agenda
( Citi in Belgium
( Vadis Consulting sa
( How to capture Client Life Cycle from prospect to mature
client?

9. Managing Customer Life Cycle
( From prospect … to client … to your best client
Anticipate
Understand &
Manage
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?

10. Capture Consumer Life Cycle
Family / Demographic Market Situation
Acquisition Events
Events
Events
Events
Interactions Product Usage
Portfolio
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.

11. 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.

12. Agenda
( Citi in Belgium
( Vadis Consulting sa
( How to capture Client Life Cycle from prospect to mature
client?
( Methodology & Tools

13. Methodology & critical success factors
Client
Business
360°
Knowledge
Data
Bank Products RFM
Bank Processes Events
Contact Strategy Dynamics
Client Life cycle Contact/Channel
Analytical data Family/Co-Holder
Socio-demo
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.

14. 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 Frequency Fraction_of_Target
70,000 2.5%
63,598
60,000
Chance for a client 2.0%
50,000
to be interested by
1.5%
40,000 our offer
30,000
Number of clients 1.0%
20,000
having the computed
0.5%
10,000 characteristic
3,091 2,701 2,819 4,536
0 0.0%
-1
No event 0 1+2 3+4+5 6+7+8+9+10+11

15. 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 Frequency Fraction_of_Target
70,000 2.5%
63,598
60,000
2.0%
50,000
40,000 Lift of 4.8 on 4% of clients 1.5%
30,000
1.0%
20,000
0.5%
10,000
3,091 2,701 2,819 4,536
0 0.0%
-1
No event 0 1+2 3+4+5 6+7+8+9+10+11
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.

16. Contact Strategy is part of the picture…
( Nbre of Past Contacts: Not always what we expect…
W e a lt h _ N b _ c a m p a ig n Frequenc y F r a c tio n _ o f _ T a r g e t
80 0 ,0 00 7 .0 %
688083
70 0 ,0 00 6 .0 %
60 0 ,0 00
5 .0 %
50 0 ,0 00
4 .0 %
40 0 ,0 00
3 .0 %
30 0 ,0 00
2 .0 %
20 0 ,0 00
10 0 ,0 00 1 .0 %
22387 5536 1078
0 0 .0 %
0 1 2 3+ 4+ 5+ 6
C r e d it _ N b _ c a m p a ig n F requenc y F r a c tio n _ o f_ Ta r g e t
600,000 2.0%
1.8%
500,000 484599
1.6%
1.4%
400,000
1.2%
300,000 1.0%
0.8%
200,000
0.6%
0.4%
100,000
50354
18408 27843 24533 33843 0.2%
16205 15410 17213 15459 3159 5571
2506 1981
0 0.0%
0 1 2 3 4 5 6 7 8 9 10 11 12 13+ 14+ 15
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.

17. Socio-Demo combines usage and family
( Socio-demo: Type of family
Socio_profession Frequency Fraction_of_Target
60,000 1.6%
1.4%
50,000
1.2%
40,000
Lift of 3.5 on 9% of clients 1.0%
30,000 0.8%
0.6%
20,000
0.4%
10,000
0.2%
0 0.0%
WOR
Class 3 Other Values
Class 1 PEN
Class 2 EMP
Class 4 SEL
Class 6 NOP
Class 5
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.

18. Methodology & critical success factors
Client
Business
360°
Knowledge
Data
Bank Products RFM
Bank Processes Events
Contact Strategy Dynamics
Client Life cycle Contact/Channel
Analytical data Family/Co-Holder
Socio-demo
People People
Data Model &
Method
Risk Mitigation

19. Methodology & critical success factors
Robust
Scalable
Modelling
Good design
No deploy crash
No black boxes
Good validation
Good recoding
People People
Data Model &
Method
Risk Mitigation

20. The Scoring task
High probability Low probability Medium probability
( 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?

21. Performance
Percentage of target
identified
Model
selection ?????
Random
selection
Percentage of selected population

22. Methodology & critical success factors
Client Robust
Business Business
360° Scalable
Knowledge Validation
Data Modelling
Bank Products RFM Good design
Processes
Bank Processes Events No deploy crash
Biases
Contact Strategy Dynamics No black boxes
Scoring
Client Life cycle Contact/Channel Good validation
Joined Offers
Analytical data Family/Co-Holder Good recoding
Socio-demo
People People People People
Data Model & Tool &
Method Method
Risk Mitigation

23. Business MUST be there!
( Dynamics: elapse time in months when customer acquired
product X
Card_Nb_Active_months_HP Frequency Fraction_of_Target Poly. (Fraction_of_Target)
250,000 8.0%
203,138 7.0%
200,000
6.0%
5.0%
150,000
4.0%
100,000
3.0%
2.0%
50,000
1.0%
2,671 2,750 1,696 1,202 1,872 1,502 2,013 1,399 1,406 1,448
0 0.0%
1 2 3 4 5 6 7+8 9 10 11 12
Something happens here.
Business process: after 9 months stopped to be included in campaigns…
Business interpretation MUST be done to eliminate business bias…

24. Methodology & critical success factors
Client Robust
Business Business
360° Scalable
Knowledge Validation
Data Modelling
Bank Products RFM Good design
Processes
Bank Processes Events No deploy crash
Biases
Contact Strategy Dynamics No black boxes
Scoring
Client Life cycle Contact/Channel Good validation
Joined Offers
Analytical data Family/Co-Holder Good recoding
Socio-demo
People People People People
Data Model & Tool &
Method Method
Risk Mitigation

25. Methodology & critical success factors
Client Robust
Business Business
360° Scalable Industria-lization
Knowledge Validation
Data Modelling
Bank Products RFM Good design
Processes Fast deploy
Bank Processes Events No deploy crash
Biases Reliable scores
Contact Strategy Dynamics No black boxes
Scoring Alarm attention
Client Life cycle Contact/Channel Good validation
Joined Offers Watch Oldness
Analytical data Family/Co-Holder Good recoding
“All inclusive”
Socio-demo
People People People People
Data Model & Tool & Tool &
Method Method Method
Risk Mitigation

26. Agenda
( Citi in Belgium
( Vadis Consulting sa
( How to capture Client Life Cycle from prospect to mature
client?
( Methodology & Tools
( Making things operational

27. Full Process
External data
Model 1
Analytical
Datamart
Model 2 Exclusion
Exclusion Optimization
Optimization P of C
Results_ Trusted_ Propositions_
…
current Repository_ current
current
Model X
Direct
Marketing
Operational Campaign
& response Results Exclusion Propositions
_History _History History

28. Industrialization
Model 1
Analytical
Datamart
Model 2 Exclusion
Exclusion Optimization
Optimization P of C
Propositions_
…
Results_ Trusted_
current Repository_ current
Model X current
Model Z Direct
Control Process Marketing
Data Drift Score Drift
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.

29. 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

30. Business Added Value
Product C 1
Product B 1
Product A 1
X-sell

31. Business Added Value
Product C 1
Product B 2
Product A 2
X-sell Upsell Retention

32. Business Added Value
Product C 1 1
Product B 3
Product A 3 1 1
X-sell Upsell Retention

33. Business Added Value
Product C 1 1
Product B 4
Product A 4 1 1
X-sell Upsell Retention

34. Business Added Value
Product C 1 1
Product B 4
Product A 4 1 1
X-sell Upsell Retention
?????
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.

35. Conclusion
Manage life cycle Better targetting
of your customer Optimize contact strategy
and
Improve profitability