1. Marketing leads Optimization at Fortis RBB
Evolution of an analytical CRM strategy : from product-oriented approach to customer-centric approach

2. Agenda
Fortis introduction  Retail Banking Belgium  Analytical & Predictive Marketing
Building blocks necessary for optimization
Required analytical skills
Industrialize response rate calculation
Translate the marketing plan & strategy into an optimization algorithm
The optimization process
Some results
Benefits & drawbacks
Questions

3. Fortis  RBB  Analytical & Predictive marketing
Fortis is an international provider of banking and insurance services to personal, business and institutional customers. We deliver a total package of financial products and services through our own high-performance channels and via intermediaries and other partners…
Analytical & Predictive marketing is a team dedicated to transform marketing needs into reality by using data mining techniques and state-of-the-art solutions

4. Building blocks necessary for optimization
Build an analytical team with people having the required skills
Industrialize your process to compute response rate automatically for each customer-product pair
Understand the business issues and convince management to solve it in a scientific way
Translate the marketing plan & strategy into an optimization algorithm
Integrate the solution in our marketing environment
Analytical dream team
Automate response rate calculation
Convince Management
OR Translation
Integration

5. Business Oriented & Computing & Data mining & Operational Research
Required analytical skills
Business Oriented & Computing & Data mining
+/- : Product/need driven solution
Feedback by product/need
Optimization
Generated business value by aCRM
Profiling Model
Predictive Model
Business Oriented & Computing & Data mining & Operational Research
+/- : Customer centric solution Marketing plan solution
Automatic feedback loop
Business Oriented & Computing
+/- : Subjective approach
No feedback loop
Queries
OLAP
Complexity

6. Industrialize response rate calculation : the process
Model Normalisation
Model construction
Business definition
Metadata Model transfer
Monitoring Results
DMI Admin
Customer Data mart
Score 1
Score database
Industrialisation
Score 2
Score 3

7. Industrialize response rate calculation : The score database
Done automatically every month

8. Translate the marketing plan & strategy into an optimization algorithm
The business context :
A marketing plan focused on sales objectives and customers’ satisfaction
A lot of customers with different needs and different service usage
A lot of marketing campaigns foreseen
A limited budget, resources availability and time to act
The translation :
Generate the best leads (offers) maximizing our expected sales revenues respecting the product mix strategy and contact strategy
Appetite scoring
Integrate every contact in only one optimization
Respect Constraints
maximizing
Constraints
Maximizing + Constraints problems
Operational Research solutions

9. Translate the marketing plan & strategy into an optimization algorithm
The operational algorithm at hand :
The “natural” solution
 Linear programming with SAS OR : The SAS LP procedure is used to optimize a linear function subject to linear and integer constraints. Specifically, the LP procedure solves the general mixed-integer program of the form :
Max c’x
Subject to :
A1x ≥ b1 and A2x = b2 and A3x ≤ b3
l ≤ x ≤ u
The difficulties :
 decision variables (xijc : propose the product j to the customer i by the channel c) are binary and there are plenty of them : # customers * # product proposed * # channel  the number of possible combination where to search the best solution was about :
± 2 ( ) : not reachable with standard computer
The retained solution
 A mixed integer programming approach (Linear + Binary Integer Programming) + a lot of SAS macro permitting us to industrialize the all process.

10. Translate the marketing plan & strategy into an optimization algorithm
A function to maximize :
of leads value = S Hit RatioLead * DLTVLead
= S S S [xijc*P (Productj=1|customeri contacted by channelc) * DLTVij]
Constraints : # leads allowed for our contact manager, maximum # leads per customer, minimum and maximum # leads per product, contact strategy
Customer
Sample for a small customer base
Product’s Lead value
S of leads value =
MAXIMUM
While respecting all constraints
5 leads in total composed by :
2 red, 1 black, 1 yellow, 1 dark grey
+ Max 1 lead per customer

11. Optimization Process : Leads generation and self learning
Offer Life time Value
Marketing Plan
Sales capacity
Max leads customer
Lead generators
Leads value
Optimization
Optimized Leads
Feedback loop to align optimization to reality
Contact & Sales
Hit ratio

12. The score band 19 generates three times more sales than a 14
Some results
The score band 19 generates three times more sales than a 14

13. Benefits and drawbacks
The leads distributed follow a general strategy and no isolated campaigns anymore, take care of our customers and take into account max profitability for the bank.
An efficient algorithm was quickly developed with SAS OR software
All the constraints and creative ideas of the marketers have been implemented “easily”
The true hit ratio of campaign is directly entered into the optimization process
Boosting the consciousness of the importance of propensity score and linking better predictive modeling with marketing campaigns
Low cost development
Drawbacks
Maintenance is time consuming
Not integrated in one package with nice reporting capabilities (as it is in SAS MO, …)

14. Thank you