This document discusses ING Belgium's use of predictive analytics and their customer intelligence department. It provides an overview of ING Belgium's strategy and approach to predictive analytics from 2005 to present day, including expanding their use of predictive models from 10 in 2008 to 38 currently. It also discusses increasing usage of model scores from 5% to 70% of commercial contacts over 5 years. Finally, it outlines ING Belgium's goals of expanding their analytics capabilities through a new data science lab to explore new techniques using big data and advanced analytics.

1. customerintelligence
Large-scale Predictive
Analytics in practice
Jonathan Burez, Head of Business analysts, ING Belgium
Meric Potier, Project Manager, ING Belgium
Geert Verstraeten, Managing Partner, Python Predictions
IÉSEG School of Management – 2 October 2015

2. customerintelligence
Large-scale Predictive Analytics in practice
Where we are
Where we are going
ING Belgium - Customer Intelligence
Where we come from

3. customerintelligence
Belgium
Universal direct bank
Serving almost 2.4 mio active retail customers
730 branches and an expanded digital network
Extremely digital, extremely personal

4. customerintelligence
Large-scale Predictive Analytics in practice
ING Belgium - Customer Intelligence
Where we come from
Where we are
Where we are going

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Elements for a mature environment (2005)
Strategy Sponsors
Analysts
SoftwareData

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Strategy (2006)
Predictive
Interpretable
Proactive
Customer-
Centric
Actionable Proactive
Targeting
Framework
Quality as high
as possible
Predictive quality
irrelevant if
result is not
interpretable
A toolbox of predictive
models a priori
available
Client ‘pull’ logic
preferred to product
‘push’ logic
Small, high-quality
target groups adapted
towards channel
capacity

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Skills of Analysts (2006)
« L’analyse est
un métier " Analytics
ITBusiness
Comm-
unication
+ Project
Management

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Confirmation (2008)
Comparison of campaign results
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00%
Business Selections Predictive Models
+ 67% on average

9. customerintelligence
Large-scale Predictive Analytics in practice
ING Belgium - Customer Intelligence
Where we are
Where we come from
Where we are going

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• From ~10 models in 2008 to an
extensive battery of models:
- 38 propensity & potential
models,
- 5 segmentations
- 2 similarity models
• 600 million scores per year
generated
• Around 4,4 million of those
scores are effectively used as
leads, which represents 60% of all
our commercial outbound contacts
in 2014.
0%
10%
20%
30%
40%
50%
60%
70%
80%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
09/1 09/2 10/1 10/2 11/1 11/2 12/1 12/2 13/1 13/2 14/1 14/2 15/1
Million
Increase in model usage for commercial contacts
from 5% to 70% in 5 years
# Commercial contacts based on business rules & triggers
# Commercial contacts based on models
% of commercial contacts based on models
We increased the number and usage of models into our
marketing campaigns…
* Data of June not yet included
*

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… Allowing us to be more relevant for the clients
11
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
2007 2008 2009 2010 2011 2012 2013 2014
Evolution # unique commercial
campaigns
0
1000
2000
3000
4000
5000
6000
7000
2007 2008 2009 2010 2011 2012 2013 2014
Target size evolution of
commercial campaigns
Sending the right message to the right customer via the right channel implies to send
more differentiated messages to smaller groups
* 1 message for 1 target group via 1 channel at a specific moment of time

12. customerintelligence
Large-scale Predictive Analytics in practice
ING Belgium - Customer Intelligence
Where we are
Where we come from
Where we are going

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The move towards a broader scope in analytics
 Data-driven Business Intelligence
 Aim for robust models and recipes
 Deliver Proof of Concepts
(tools, algos, new data, new viz, …)
 Fail fast, fail often… and learn
 Business analysis
 Modelling (all, standard methods)
 Industrialisation
 Modelling (explore new techniques)
 Feature extraction from text, graphs, etc.
 Exploration of Big Data analytics tools
Predictive Analytics (in production) Data Science Lab
(Predictive)
Analytics
Data
Advanced
(Predictive)
Analytics
PresentPast
(Predictive)
Analytics
Data

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What is the difference between Business Analysts
and Data Scientists?
Data ScientistsBusiness Analysts
Business understanding
In-house data knowledge
Visualisation
Coding skills
Visualisation

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Different approach with regards to tooling
Data Science LabPredictive Analytics (in production)
BI server
Operational Data Store

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Key success factors of Data Science Lab:
Methodology
• Flexibility is key
• Learn fast, fail fast
• Regular status updates
and review sessions
• Exploration and R&D
with a certain pace
• Committing to a certain
scope for each sprint

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Key success factors of Data Science Lab:
Multi-disciplinary teams
• Involvement of
Business Owner
• Data Scientists
• Hadoop Developers
• Scrum Master /
Functional Team
Manager

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Key success factors of Data Science Lab:
Skills & compententies within the team
+ Soft Skills!
System Admin
Development Testing &
Deployment
DEVOPS
Domain Expertise
Mathematics Computer
Science
DATA
SCIENTIST
Machine
Learning
Data
Processing
Statistical
Research
+ Planning & organisation
skills!
Scrum Master as facilitator,
organiser and coach

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Exploratory projects so far…
Text Mining
Personal
Banking
client
Personal
Banker
Bla bla bla
Notes
 Very high noise level
 Small dataset
 Clear business goals
 Time to explore
 Team effort
Graph analytics
Find patterns in transactions between businesses
for predictive analytics for knowledge discovery

20. customerintelligence 9/24/11 20Name ING department or name