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Predictive analytics and data science at ING Belgium

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Presentation for the conference 'Etes-vous prêts à accueillir le Big Data?" at IESEG School of Management, Paris, Oct 2nd 2015. In this presentation, we covered ING Belgium's current developments and usage of Predictive Analytics. About ten years ago, ING Belgium started its first activities in Predictive Analytics for Marketing at ING Belgium. Since then, the bank has succeeded in transforming the first sporadic successes to a successful business activity in production. As a result, predictive models are the main engine of automated campaigns towards a large variety of target audiences and channels. In this presentation, we discuss the evolution from initial developments over industrialisation. We conclude by emphasizing how modern technologies and the Big Data hype has changed this domain for the bank.

Publié dans : Business
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Predictive analytics and data science at ING Belgium

  1. 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. 2. customerintelligence Large-scale Predictive Analytics in practice Where we are Where we are going ING Belgium - Customer Intelligence Where we come from
  3. 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. 4. customerintelligence Large-scale Predictive Analytics in practice ING Belgium - Customer Intelligence Where we come from Where we are Where we are going
  5. 5. customerintelligence Elements for a mature environment (2005) Strategy Sponsors Analysts SoftwareData
  6. 6. customerintelligence 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
  7. 7. customerintelligence Skills of Analysts (2006) « L’analyse est un métier " Analytics ITBusiness Comm- unication + Project Management
  8. 8. customerintelligence 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. 9. customerintelligence Large-scale Predictive Analytics in practice ING Belgium - Customer Intelligence Where we are Where we come from Where we are going
  10. 10. 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. 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 *
  11. 11. customerintelligence … 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. 12. customerintelligence Large-scale Predictive Analytics in practice ING Belgium - Customer Intelligence Where we are Where we come from Where we are going
  13. 13. 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
  14. 14. customerintelligence14 What is the difference between Business Analysts and Data Scientists? Data ScientistsBusiness Analysts Business understanding In-house data knowledge Visualisation Coding skills Visualisation
  15. 15. customerintelligence15 Different approach with regards to tooling Data Science LabPredictive Analytics (in production) BI server Operational Data Store
  16. 16. customerintelligence16 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
  17. 17. customerintelligence17 Key success factors of Data Science Lab: Multi-disciplinary teams • Involvement of Business Owner • Data Scientists • Hadoop Developers • Scrum Master / Functional Team Manager
  18. 18. customerintelligence18 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
  19. 19. customerintelligence19 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. 20. customerintelligence 9/24/11 20Name ING department or name

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