Case Study: Supporting the branch network using predictive analytics: Predicting First Investment...

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Matthias Meul Deutsche Bank Supporting the branch network using predictive analytics Predicting first investment behaviour 12Dec2013

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by Matthias Meul, Customer Intelligence Analyst, Deutsche Bank AG, Brussels Branch, Belgium

Transcript of Case Study: Supporting the branch network using predictive analytics: Predicting First Investment...

Page 1: Case Study: Supporting the branch network using predictive analytics: Predicting First Investment behavior

Matthias MeulDeutsche Bank

Supporting the branch network using predictive analyticsPredicting first investment behaviour12Dec2013

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Deutsche Bank Matthias MeulFirst Investment model

First InvestmentPredictive analytics in Investments

The next 40 minutes

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— Introduction to Deutsche Bank & casestudy

— The First Investment model

— Commercial implementation & results

— Lessons learned

— Q&A

Introduction Model Results Questions

Introduction

Model

Results

Questions

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Deutsche Bank in Belgium

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— > 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

ClientsCapacity

DBP

Private

Retail

300 employees with client contact

33 FCs

First InvestmentPredictive analytics in Investments

Introduction Model Results Questions

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Casestudy overview

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— 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 in-vestments!

INDICATION

First InvestmentPredictive analytics in Investments

Introduction Model Results Questions

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Dependent variable

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First InvestmentPredictive analytics in Investments

— Will the client make a first investment in a strategic product in the year to come? Yes/No

— Timeframe?

Introduction Model Results Questions

T2: The moment when we want to make the prediction

T3: Start of the period in which the behavior will take place

T4: End of the period in which the behavior will take place

Historical data taken into account to make the prediction

Period to execute commercial initiatives based on predictions

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Modeling technique

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— 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%)

Þ We have to perform a transformation

— Logistic regression

— Model the log odds of the event (=dependent variable), which will be transformed into probabilities

First InvestmentPredictive analytics in Investments

Introduction Model Results Questions

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Variables retained (high level) using stepwise regression

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— Current AUM & variations

— AUM in the past

— Education level

— Current level of loyalty towards Deutsche Bank (~advanced algorithm)

— Length of relation

— …

First InvestmentPredictive analytics in Investments

Introduction Model Results Questions

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Evaluation

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First InvestmentPredictive analytics in Investments

— Check model performance… Wait until time goes by…?

T2: The moment when we want to make the prediction

T3: Start of the period in which the behavior will take placeHistorical data taken into account to make the prediction

Period to execute commercial initiatives based on predictions

T4: End of the period in which the behavior will take place30jun2012

31jul2012 31jan2013

Use the model to score, based on 28feb2013

31mar2013 30sep2013Monitor model performance

Introduction Model Results Questions

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Technical performance measures

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— Lift

— AUC

Introduction Model Results Questions

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Technical performance measures: Lift

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— Lift

Introduction Model Results Questions

OVERFITTING?

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Customers

— 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

Technical performance measures: AUC

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A random first investor(score = 10%)

A random “not” first investor (score = 0.005%)

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 probabilityof churning than the non-FI

First InvestmentPredictive analytics in Investments

Introduction Model Results Questions

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Operational implementation of the First Investment model

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First InvestmentPredictive analytics in Investments

— 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

Introduction Model Results Questions

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Results of the first roll-out

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Introduction Model Results Questions

First InvestmentPredictive analytics in Investments

+-20% of treated tasks results in a meeting!

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“Educate” the branch network

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Introduction Model Results Questions

First InvestmentPredictive analytics in Investments

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Results of the first roll-out (purchases until end of september)

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Introduction Model Results Questions

First InvestmentPredictive analytics in Investments

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Results of the first roll-out (purchases one month after task injection)

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Introduction Model Results Questions

Sales until October 19th taken into account because of

*tasks being treated as of September 19th

*time between ordering and booking an investment product

First InvestmentPredictive analytics in Investments

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Is it necessary to contact these clients?

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Introduction Model Results Questions

Won’t they invest “by themselves”?

Do they really need the extra trigger?...Þ Same characteristics as targeted clients & compare!

First InvestmentPredictive analytics in Investments

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Results of the First Investment model

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First InvestmentPredictive analytics in Investments

Introduction Model Results Questions

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First Investors 2012 vs 2013 (31nov)

Investors_per_month_2012

Investors_per_month_2013

YTDInvestors_2012

Total2012

YTDInvestors_2013

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Lessons Learned

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— 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!

Introduction Model Results Questions

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Thank you!

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First InvestmentPredictive analytics in Investments

Introduction Model Results Questions