Big Data Monetization in Banking - SAP · Using SAP Predictive Analytics, mBank was able to respond to ... §Events for Real – Time Marketingare only triggers. If an event corresponds

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Nicolas PlocharskiSenior Director, Global Center of ExcellenceSAP Leonardo & Analytics, Predictive Analytics Expert

Tashkent, 19th of June, 2018

Big Data Monetizationin Banking

2PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Digital economy – we know everything about everybody


Woman29 years oldPhDOur customer for 3 yearsRelocated to another city lastyearLast month:Bought 36 bottles of yogurt of 7different tastes in organicfood stores

Woman32 years oldBachelorOften visits stores with goods forchildren. And spends there 200 EURmonthly.Man

35 years oldHigh schoolOur customer for one yearLast month:Was in ParisBought a car insurance (for Nissan,BTW)Average check in a food store 50 EUR

Woman20 years oldStudentOur customer for 6 monthsLast month:Got 3 A-gradesWas in 12 different city districtsBuys food for ~20 EUR on her way home.

Man48 years old.Master degreeOur customer for 5 yearsLast month:Bought a bicycleVisit a doctor 2 timesUse 7 different ATMs

Digital economy – we know everything about everybody


What can we do with this data? What should we do with the customer?


To develop or to retain?

GoalID Gen Age Tarif … atr n Response

1 M 32 WiFi+ 600 NO

2 F 25 Master Pirma 450 YES

BuildModel

ApplyID Gen Age Tarif … atr n Response Proba

1102 M 42 GSM Pulse 1 200 ? 0,5

1103 F 21 WiFi– 250 ? 0,2

Customer profile to build a modelCustomer profile to build a model

Reference date to learn

Predicted churn

«Today» – reference for prediction

Customer profile to apply a model

Known churnKnown churn

üBy historical data find a patterns in customers’ profilethat separates churners from non-churners

üBy new data make a prediction for each customerfor a new period

6© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀPublic

What can we do with this data? What should we do with the customer?

What we are talking about?


Big DataThe Big Data defined:

• Volume (how big is big?10GB? 10 TB? 10 PB?)• Variety (xls files? database tables? Emails? Twitter feed? Call center logs? Images? Data Streams?)• Velocity (how quickly the data is growing? 100 MB per day? 1 TB per hour?)• Veracity (How much can you trust the data? 60%? 99%?)

• And what about the 5th V – like VALUE?

Some stunning facts

• 80% of data is made by consumers like you and me• 20% is owned by us openly while Facebook, Google own the rest

• Data volume double every 2 years• It means that in June 2016 we had quite half of data that we have today.


(Big) Data MonetizationData Monetization is the organizational ability to turn data into:• cost savings• revenues in existing lines of business• new revenue streams

Getting the value from data requires investing in an analytics platform

The market for data monetization is expected to expand to reachUS$ 708.86 Bn by 2025

Internal versus external data monetization

• Internal data monetization strategy focuses on using information to enhance customer experience anddrive cross-selling as well as loyalty and costs optimization

• External data monetization strategy leverages data to create new revenue streams with third parties.


Five Data Monetization Strategies across Financial Services & Insurance…

Source: The Tao of Data Monetization in Banking and Insurance & Strategies to Achieve the Same…

https://i2.wp.com/www.vamsitalkstech.com/wp-content/uploads/2017/08/Data-Monetization.png


Five Data Monetization StrategiesMonetization Strategy #1 - Understand and Improve Your ProcessesLeverage Data that you collected from business operations and that you to Ensure Higher Efficiency inBusiness Operations…(branch design & optimization, customer service improvements etc)

Monetization Strategy #2 – Understand and Delight The CustomerLeverage Data to Improve Customer Service and Satisfaction…(cross sell and up-sell of products to existing customers)

Monetization Strategy #3 - InnovateUse Data to Enter New Markets…(product recommendations and real time offers)

Monetization Strategy #4 - ShareEstablish a Data Exchange…(fill in holes in their existing data about customers)

Monetization Strategy #5 - DialogueOffer Free Products to Gather Customer Data…(create products that can drive longer & continuous online interactions)

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Predictive Analytics and Machine Learning PlatformEnables Big Data Monetization


SAP In-Database Predictive Analytics Platform

Hadoop, Hive and Spark

Third-Party: DB2, Oracle,Teradata, Greenplum and more

Text and BinaryFiles, XML,Excel, CSV &More

Patterns

BusinessIntelligence -Dashboard

Forecast KPI AnalysisPredictive Applications:

Like Marketing Automation

Near Real-Time

Real-Time

Predictive Applications:Like CRM, Call Center, Web

DataWarehouse

SAP PredictiveAnalytics


Accurate results in days, not weeksAccelerate your work with automated techniques

Machine learning at scaleMaintain peak performance for thousands of models

Insights where people interactEmbed predictive in business processes and applications

SAP PredictiveAnalytics is aGame Changer.

Automation and Integration are keys.

Nowadays everybody talks about automation.

But what they do? “Pack and prey approach”:Just stack several algorithms and hope for thebest.


Some examples

Increased response rate of Marketing campaigns400%

7x increase of responserate compared to controlgroup

Lloyds Bank reducedmodelling effort fromdays and weeks to hours

Increase of response rateby 160% and purchasesby 35%

Bank of Montreal

http://de.wikipedia.org/w/index.php?title=Datei:Bank_Austria-logo.svg&filetimestamp=20101230104916&

http://de.wikipedia.org/w/index.php?title=Datei:Lloyds_TSB_Logo.svg&filetimestamp=20080322145310&


Using SAP Predictive Analytics, mBank was able to respond tocustomer needs in real time, deliver insights that supported theircustomer first banking experience philosophy, and optimize theirentire discount offer program with dramatic results andtransformative response rates.

Customer Success Details : mBank.

Delivering personalized banking experiencesfor 4.5 million people.

Higher hit rate fornon-mortgage loans

Larger hit rate forsavings products

Increased hit rate forinsurance products

400%

250%

200%

Results.

17© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀPublicSource: mBank

Transactionalindicators

Applicationindicators

ADSProducts

Customers

Marketing

Depositindicators

Creditindicators

5 000 variables

Big Data, Relational database, reports, etc. Analitical Data Set

The major concern of Big Data MonetizationCleaning and improving data to produce an ADS


Customize communications in channels!

TRANSACTIONAL SYSTEM CONTACT CENTER/VIRTUAL BRANCH

BRANCH NETWORK

CRM

MOBILE APPLICATION

Optimal Use of Omnichannels Strategy


Search for places frequentlyvisited by the customers!

Digital Dialog with The Customer - Personalize Placeholders


Next Best Action in CRM

§ Using scoring from models could help to optimize choosing the best campaign forcustomer in CRM system.

§ Events for Real – Time Marketing are only triggers. If an event corresponds to more than onecampaign CRM should decide which campaign will be presented to the customer.

§ Saving and NML Products § Discount platform – mDeals (mOkazje)

§ Predicting sensibility of customersfor savings and NML products

§ Optimizing value of interest ratemanagement for saving and NMLproducts.

§ Managing amount of discounts byfinding out vulnerability of customersto make card transactions in specificmerchants

§ Personalizing amount of discountsfor every customer

§ Calculating propensity for eachmerchant to buy an ad space

Based on presentation - mBank Digital Story - Bartosz Witorzeńć - Moscow, 13th April 2017

Smart Data Monetization @ mBank


Acquisition of newclients / Increasingin-store or online

traffic

Managing customerlifetime value

(CLV)

Market andconsumer knowledge

Partner’s goals

Tailor-made offersbased on purchasing

history

Simple redemptionmodel: no

vouchers/codes/coupons

Client’s benefitsPrecise targeting based ontransactional and demographic

data

1

2

Attractive discountoffers

mDeals - Precise and Effective Approach to Targeting Customers


Precise selection of a target group

STEP 1

ACQUISITION

LOYALISATION

Mechanics of a campaign Step 1 - Selecting the target groupThe integration of transactional data and customer demographics

X-SELLING

WHAT ARE WEANALYSING?

Transactional data

Spending value

Localisation ofpurchases

Account inflows

Frequency ofpurchases

Demographic data

Place of residence

Sex

Marital status

Age

WHAT DO WEOFFER?

PRECISESEGMENTATION

RTM CAMPAIGNS-REAL TIME MARKETING ADVANCED REPORTS

1

2

Communication of an offer tocustomers in selected channels

STEP 2

Activating an offer by payment by acredit/debit card or mTransfer

STEP 3

Scores



STEP 2

Mechanics of a campaign Step 2 - Communication of an offerMulti-channel and context of a purchase

CHANNEL

Internet Banking Facebook App Mobile App SMS Email


STEP 1


STEP 3



STEP 3

Mechanics of campaign Step 3 – Activation of an offerNo influence on sales process


STEP 2


STEP 1

NO NEED FOR EDUCATINGEMPLOYEES

1

NO NEED FOR ITSYSTEMS

DEVELOPMENT

2

NOVOUCHERS/CODES/COUPONS

3Sales people do not needto know about currentcampaigns

Fully automated processbased on POS-mBankintegration

Client activates an offerby credit/debit cardpayment or mTransfer


Predictive Analytics Delivers Business Value

before

2XINSURANCE - Success Rate

after before

2,5XSAVINGS - Success Rate

after

before

4XNon Mortgage Loan (NML) – Hit Rate

after

Based on presentation - mBank Digital Story - Bartosz Witorzeńć - Moscow, 13th April 2017


Customer behavior is not the only thing you can predict.Optimize resources and improving margins

FRAUD + RISK FINANCE + HR

• Fraud and AbuseDetection

• Claim Analysis• Collection and

Delinquency• Credit Scoring• Operational Risk

Modeling• Crime Threat• Revenue and Loss

Analysis

• Cash Flow and Forecasting• Budgeting Simulation• Profitability and Margin

Analysis• Financial Risk Modeling• Employee Retention

Modeling• Succession Planning

OTHER SECTORS

• Life Sciences• Health Care• Media• High Education• Public Sector/Social

Sciences• Construction and Mining• Travel and Hospitality• Big Data and IoT

SALES + MARKETING OPERATIONS

• Churn Reduction• Customer Acquisition• Lead Scoring• Product

Recommendation• Campaign

Optimization• Customer

Segmentation• Next Best

Offer/Action

• Predictive Maintenance• Load Forecasting• Inventory/Demand

Optimization• Product

Recommendation• Price Optimization• Manufacturing Process

Opt.• Quality Management• Yield Management


Let’s innovate the Future of Banking – Together!

Do you want to know more?Contact information:

Nicolas PlocharskiSenior Director, Global Center of ExcellenceSAP Leonardo & Analytics, Predictive Analytics [email protected]+33 6 6344 0891

mailto:[email protected]

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Big Data Monetization in Banking - SAP · Using SAP Predictive Analytics, mBank was able to respond to ... §Events for Real – Time Marketingare only triggers. If an event corresponds