Predictive analytics and data science at ING Belgium
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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
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Transcript of Predictive analytics and data science at ING Belgium
customerintelligence
Large-scale Predictive Analytics in practiceJonathan Burez, Head of Business analysts, ING BelgiumMeric Potier, Project Manager, ING BelgiumGeert Verstraeten, Managing Partner, Python PredictionsIÉSEG School of Management – 2 October 2015
customerintelligence
Large-scale Predictive Analytics in practice
Where we are
Where we are going
ING Belgium - Customer Intelligence
Where we come from
customerintelligence
BelgiumUniversal direct bankServing almost 2.4 mio active retail customers 730 branches and an expanded digital networkExtremely digital, extremely personal
customerintelligence
Large-scale Predictive Analytics in practice
ING Belgium - Customer Intelligence
Where we come from
Where we are
Where we are going
customerintelligence
Elements for a mature environment (2005)
Strategy Sponsors
AnalystsSoftwareData
customerintelligence
Strategy (2006)
Predictive
Interpretable
ProactiveCustomer
-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
customerintelligence
Skills of Analysts (2006)
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« L’analyse est un métier " Analytics
ITBusiness
Comm-unication
+ Project Management
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Confirmation (2008)
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Comparison of campaign results
Business Selections Predictive Models0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00% + 67% on average
customerintelligence
Large-scale Predictive Analytics in practice
ING Belgium - Customer Intelligence
Where we are
Where we come from
Where we are going
customerintelligence10
•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.
09/1 09/2 10/1 10/2 11/1 11/2 12/1 12/2 13/1 13/2 14/1 14/2 15/10.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0%
10%
20%
30%
40%
50%
60%
70%
80%Increase in model usage for commercial contacts
from 5% to 70% in 5 years
# Commercial contacts based on models# Commercial contacts based on business rules & triggers
Mill
ion
We increased the number and usage of models into our marketing campaigns…
* Data of June not yet included
*
customerintelligence
… Allowing us to be more relevant for the clients
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2007 2008 2009 2010 2011 2012 2013 20140
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Evolution # unique commercial campaigns
2007 2008 2009 2010 2011 2012 2013 20140
1000
2000
3000
4000
5000
6000
7000Target size evolution of com-
mercial 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
customerintelligence
Large-scale Predictive Analytics in practice
ING Belgium - Customer Intelligence
Where we are
Where we come from
Where we are going
customerintelligence13
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 knowledgeVisualisation
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
PersonalBanking
client
PersonalBanker
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
customerintelligence 9/24/11
201) Name ING department or name presentation
