Case Study: Supporting the branch network using predictive analytics: Predicting First Investment behavior
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by Matthias Meul, Customer Intelligence Analyst, Deutsche Bank AG, Brussels Branch, Belgium
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Case Study: Supporting the branch network using predictive analytics: Predicting First Investment behavior
- 1. Deutsche Bank Matthias Meul Supporting the branch network using predictive analytics Predicting first investment behaviour 12Dec2013
- 2. Introduction Model Results Questions First Investment Predictive analytics in Investments The next 40 minutes — — — — — Introduction to Deutsche Bank & casestudy The First Investment model Commercial implementation & results Lessons learned Q&A Deutsche Bank 12/18/2013 11:56:11 AM Matthias Meul First Investment model 2010 DB Blue template Introduction Model Results Questions 1
- 3. Introduction Model Results Questions First Investment Predictive analytics in Investments Deutsche Bank in Belgium — — — — — — — > 315 000 clients +-715 employees (of which +-300 with client contact roles) 33 Financial Centers 3 Contact Centers Over 21 billion € AUM ―Open architecture‖, mandates as of 100€ Retail – DbPersonal – Private Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 2
- 4. Introduction Model Results Questions First Investment Predictive analytics in Investments Casestudy overview — Clients with investments are treated very well (maturities, downgrades,…) — The objective is to convert retail savers-only to investors A significant number of clients doesn’t have and never had investments! Deutsche Bank 12/18/2013 11:56:11 AM Matthias Meul First Investment model 2010 DB Blue template 3
- 5. Introduction Model Results Questions First Investment Predictive analytics in Investments Dependent variable — Will the client make a first investment in a strategic product in the year to come? Yes/No — Timeframe? T3: Start of the period in which the behavior will take place Historical data taken into account to make the prediction T2: The moment when we want to make the prediction T4: End of the period in which the behavior will take place Period to execute commercial initiatives based on predictions Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 4
- 6. Introduction Model Results Questions First Investment Predictive analytics in Investments Modeling technique — Linear regression — Dependent variable between -∞ and +∞ — Easy to explain: — However, the problem of identifying First Investors is binary — Made a FI = 1 — Did not make a FI = 0 — The desired outcome of the model are probabilities (between 0% and 100%) We have to perform a transformation — Logistic regression — Model the log odds of the event (=dependent variable), which will be transformed into probabilities Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 5
- 7. Introduction Model Results Questions First Investment Predictive analytics in Investments Variables retained (high level) using stepwise regression — — — — — — Current AUM & variations AUM in the past Education level Current level of loyalty towards Deutsche Bank (~advanced algorithm) Length of relation … Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 6
- 8. Introduction Model Results Questions First Investment Predictive analytics in Investments Evaluation — Check model performance… Wait until time goes by…? T3: Start of the period in which the behavior will take place Historical data taken into account to make the prediction T2: The moment when we want to make the prediction 30jun2012 T4: End of the period in which the behavior will take place Period to execute commercial initiatives based on predictions 31jul2012 Use the model to score, based on 28feb2013 Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 31jan2013 Monitor model performance 31mar2013 30sep2013 7
- 9. Introduction Model Results Questions First Investment Predictive analytics in Investments Technical performance measures — Lift — AUC Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 8
- 10. Introduction Model Results Questions First Investment Predictive analytics in Investments Technical performance measures: Lift — Lift Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 9
- 11. Introduction Model Results Questions First Investment Predictive analytics in Investments Technical performance measures: AUC — Area Under the receiver operating Curve — The probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one A random ―not‖ first investor (score = 0.005%) Customers Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template A random first investor (score = 10%) When I compare the scores of the FI and the non-FI, the FI has 70% chance (AUC = 0.70) of having a higher predicted probability of churning than the non-FI 10
- 12. Introduction Model Results Questions First Investment Predictive analytics in Investments Operational implementation of the First Investment model — Recurrent Tasks, monthly refresh — Event-driven (e.g. AUM evolution)No ice-cold calling — always at least 1 of 3 potential commercial triggers in Task comment Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 11
- 13. Introduction Model Results Questions First Investment Predictive analytics in Investments Results of the first roll-out +-20% of treated tasks results in a meeting! Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 12
- 14. Introduction Model Results Questions First Investment Predictive analytics in Investments ―Educate‖ the branch network Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 13
- 15. Introduction Model Results Questions First Investment Predictive analytics in Investments Results of the first roll-out (purchases until end of september) Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 14
- 16. Introduction Model Results Questions First Investment Predictive analytics in Investments Results of the first roll-out (purchases one month after task injection) Sales until October 19th taken into account because of *tasks being treated as of September 19th *time between ordering and booking an investment product Deutsche Bank 12/18/2013 11:56:12 AM Matthias Meul First Investment model 2010 DB Blue template 15
- 17. Introduction Model Results Questions First Investment Predictive analytics in Investments Is it necessary to contact these clients? Won’t they invest ―by themselves‖? Do they really need the extra trigger?... Same characteristics as targeted clients & compare! Deutsche Bank 12/18/2013 11:56:13 AM Matthias Meul First Investment model 2010 DB Blue template 16
- 18. Introduction Model Results Questions First Investment Predictive analytics in Investments Results of the First Investment model First Investors 2012 vs 2013 (31nov) Investors_per_month_2012 Investors_per_month_2013 YTDInvestors_2012 Total2012 YTDInvestors_2013 Jan Feb Deutsche Bank 12/18/2013 11:56:11 AM Mar Apr May Jun Matthias Meul First Investment model 2010 DB Blue template Jul Aug Sep Oct Nov Dec 17
- 19. Introduction Model Results Questions First Investment Predictive analytics in Investments Lessons Learned — Do not limit yourself to the existing KPIs. Do they match the business problem you’re trying to solve? — Assure the commercial implementation of the model, put the scores to use & add tangible customer leads. — Assure the followup of the model & recurrently report results. — Keep it simple & understandable! Deutsche Bank 12/18/2013 11:56:13 AM Matthias Meul First Investment model 2010 DB Blue template 18
- 20. Introduction Model Results Questions First Investment Predictive analytics in Investments Thank you! Deutsche Bank 12/18/2013 11:56:13 AM Matthias Meul First Investment model 2010 DB Blue template 19
Notes de l'éditeur
- I‘mgoingto talkabouthowwebackedupthecontactstrategy @DB withanalytics & predictivemodelingThe mainfocus in thispresentation will be on the First Investment model.I‘mgoingtobeveryhands on, talk aboutissueswefacedandhowwe dealt withthem. Pleasekeep in mindthattheorganizationdoes not havemuchexperiencewithpredictivemodels, so theaimistokeeptheanalyticspractice simple andunderstandableto all stakeholders!Commercial implementation & results: thisisgoingtobeabouthowweputanalyticstowork, andtheresultswegetfromthem.I‘ll do a smallrecap on thelessonslearnednearthe end ofmy talk, andthere will beroomforsome Q&A.
- Open architecture: Billboard showingnamesoffundmanagersthatsays „youdon‘thavethischoicewithyourotherbank“ „Not real bankers“This „capacitymanagement“ isonepartoftheIntroductionthatwealwayshavetokeep in mind…Mission of CFU team (Costumerfollowup)+ NO predictivemodelingnoradvancedanalytics 3 yearsago
- Add: “Analyse behavior 6 monthsafterfirstinvestmentfor “naturallyconvertedcustomers” in the past: higheraum, closer link with the bank, higherup-sellpotential, more investments,lowerchurn,…“Andthewaywewanttosolvethisisusing „Analytics“~predictivemodeling
- The firstquestionyou must askwhenyou’redeveloping a predictive model is: what is itthat I want to predict?Don’ttakethis topic of defining the timeframe lightly! It’s important that the customerbehavioryou’retrying to predictallignswellwith the intended commercialuse of youranalytical model. Likesomany models, we startedoffwith the idea of wanting to predict the behavior over the nextyear, butafterthoroughanalysis we sawthat the events in a 6 month timeframe had analmostidentical incident rate.+ Leavesome time for commercial actions to anticipate the forecastedbehaviourMODEL (“probablyonlyonewith a beautiful lady in hispresentation”)+ Carefulnot to miss commercial opportunities (~commercial implementation: useactualmaturities)
- The goalistosolvetheequationto „p“, becausethisistheprobabilityofmaking a firstinvestment
- I am not goingtospendtomuch time on themethodsused, splittingintotrainingandvalidation sample, ifyouareinterested in thisyoucan talk tome after thispresentation.But, after modeling, wecameupwiththislistofpredictor variables toinfluence First Investment behavior. Now, how well doesour model perform?
- Once we have the model, we can check its performance onrealbehavior (apart of coursefrom the performance on the validationor test set) …Waituntil time goesby? No, of coursenot, youcanalreadyuse the model to score clients in the past and model their performance. Just becarfeulnot to use the same timeframe as the oneyouused to build the model! Ifyou do thiscorrectly, youcanalreadyperform and out of sample out of time test
- I’mnotgoing to focus toomuchon “technical performance measureshere, butI’mgoing to talk a bit onhow we put these scores to use!I’mgoing to go over these 2 veryquickly.Also, this is notrocketscience, this was the first model developed @db, sointerpretability was prioritisedaboveachieving the highestpossibletechnical performance. Just soeveryone is on the same page, we now have predictions per customerthatindicate the probability of thatclientmaking a firststrategicinvestment.
- Whatdid we do before? (excel, high potentiallists per branch)Recurrenttasks (~explainwhat a task is!), monthlyrefresh”Keep the water fresh”, nolonger “the taskshaven’t been treatedyet, nonewones!”
- proactivelycontacting & helpingclientswiththeirfirstinvestmentisnice!On theimportanceofcorrectlylabelingandtreatingtheirtasks!
- proactivelycontacting & helpingclientswiththeirfirstinvestmentisnice!On theimportanceofcorrectlylabelingandtreatingtheirtasks!
- Task vsNotask: weare not speakingaboutthe same clients, hencethenextslideisneededtobereallyabletojudgeperformanceofthetrigger!
- proactivelycontacting & helpingclientswiththeirfirstinvestmentisnice!
- Youcan present AUC, lift curves,… all you want, butit’s a slidelikethisthatreallycapturesmanegement’sattention, and truly shows the addedvalue of implementinganalytics in your business processes.1) End of 2012: objectiveachieved, curve levelsoff (~Incentivecaused bias?)2) Results of anentireyear was alreadyachievedmid-september3) February: No tasks
- 1. THINK OUT OF THE BOX!Thosearethekeylessonswetrytokeep in mind, andthankstothisWe‘renolongerconsidered a costcenter, we‘renow a profitcenter!Thankyouforyourattention!