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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
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Digital economy – we know everything about everybody
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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
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What can we do with this data? What should we do with the customer?
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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
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What can we do with this data? What should we do with the customer?
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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.
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(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.
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Five Data Monetization Strategies across Financial Services & Insurance…
Source: The Tao of Data Monetization in Banking and Insurance & Strategies to Achieve the Same…
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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
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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
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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.
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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
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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
18© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀPublicSource: mBank
Customize communications in channels!
TRANSACTIONAL SYSTEM CONTACT CENTER/VIRTUAL BRANCH
BRANCH NETWORK
CRM
MOBILE APPLICATION
Optimal Use of Omnichannels Strategy
19© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀPublicSource: mBank
Search for places frequentlyvisited by the customers!
Digital Dialog with The Customer - Personalize Placeholders
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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
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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
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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
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Communication of an offer tocustomers in selected channels
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
Precise selection of a target group
STEP 1
Activating an offer by payment by acredit/debit card or mTransfer
STEP 3
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Activating an offer by payment by acredit/debit card or mTransfer
STEP 3
Mechanics of campaign Step 3 – Activation of an offerNo influence on sales process
Communication of an offer tocustomers in selected channels
STEP 2
Precise selection of a target group
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
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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
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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
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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