marketing leads optimization at fortis rbb

SAS Belux Forum 2008 | Manuel Piette | 22 April 2023 | 1

Getting you there.

Marketing leads Optimization at Fortis RBB

Evolution of an analytical CRM strategy : from product-oriented approach to customer-centric approach

SAS Belux Forum 2008 | Manuel Piette | 22 April 2023 | 2 Estimate Automate InnovateEvaluate

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


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

Fortis

Marketing Intelligence

…

Analytical & Predictive marketing

…

Marketing

…Retail Banking

Belgium

…


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 Convince

Management Integration

OR Translation

Automate response

rate calculation


Complexity

Gen

erat

ed b

usin

ess

valu

e by

aC

RM

Queries

OLAP

ProfilingModel

PredictiveModel

Optimization

Business Oriented & Computing +/- : Subjective approach

No feedback loop

Business Oriented & Computing & Data mining +/- : Product/need driven solution

Feedback by product/need

Business Oriented & Computing & Data mining & Operational Research

+/- : Customer centric solutionMarketing plan solution

Automatic feedback loop

Required analytical skills


Industrialize response rate calculation : the process

Business definitionModel constructionModel Normalisation

Metadata Model transfer

Industrialisation

Score databaseDMI Admin

Customer Data mart

Score 1

Score 2

Score 3

Monitoring Results


Industrialize response rate calculation : The score database

Id customer id score DateScore Score T (Score)1 Model 1 31/12/2003 10% High1 Model 2 14/10/2004 2% low

…1 Model p 14/10/2004 15% Medium2 Model 2 14/10/2004 1% low

…2 Model p 14/10/2004 3% low

…1000001 Model 1 31/12/2003 6% Meduim1000001 Model 2 14/10/2004 1% low

…1000001 Model p 14/10/2004 5% low

…

Id customer T(Score 1) T(Score 2) … T (Score p)1 High low Medium2 low low3 High Medium High4 low low low

…1000001 Medium low low

…

Done automaticallyevery month

Score database


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

Operational Research solutionsMaximizing + Constraints problems



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 (12 000 000) : 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.


5 leads in total composed by :

2 red, 1 black, 1 yellow, 1 dark grey

+ Max 1 lead per customer


– A function to maximize : of leads value = Hit RatioLead * LTVLead

= [xijc*P (Productj=1|customeri contacted by channelc) * LTVij]

– Constraints : # leads allowed for our contact manager, maximum # leads per customer, minimum and maximum # leads per product, contact strategy

Customer

Product’s Lead value

of leads value =

MAXIMUM

While respecting all constraints

Sample for a small customer base


Optimization Process : Leads generation and self learning

Lead generators Optimization

Marketing PlanSales capacity

Max leads customer

Optimized Leads

Contact & SalesHit ratio

Leads value

Feedback loop to align optimization to reality

OfferLife time Value


Acquisition : Product X

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

14,0%

12 13 14 15 16 17 18 19

Scorebands : 19 = top5%, (18+19) = top 10%, …. , 0 = worst 5%

Hit

Rat

io (

pro

du

ctio

n b

etw

een

: b

egin

dat

e to

en

d d

ate

+ 1

mo

nth

)

Treated

Some results

The score band 19 generates three times more sales than a 14


Benefits and drawbacks

– Benefits

– 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, …)

SAS Belux Forum 2008 | Manuel Piette | 22 April 2023 | 14

Getting you there.

Thank you

marketing leads optimization at fortis rbb

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