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

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

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

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

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

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

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

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 (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.

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

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

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

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, …)

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