Digital Banks powered by Big Data How to gather and successfully utilize customer data? Sopot 23rd of June 2015 Edwin Van der Ouderaa
From Big Data to All Data the new paradigm Big Data is not very Big at all but fast and cheap: $800/TB/yr at AWS 4,000,000+ searches on Google 347,000+ tweets 38,000+ photos shared 100+ hours of video uploaded IN SECONDS 3,100,000 likes on Facebook 277,000+ snapchats received 3,500+ pins $275,000+ transactions (USD) 2.4 billion people connected to the internet interact Copyright 2015 Accenture All rights reserved. 2
Which role for the banks in between non-bank payment origination, digital brokers and merchant credit? Invisible utility or part of the everyday conversation? Will the banks be left with the expensive payment backbone and simply be balance sheet risk carriers? Fire Copyright 2014 Amazon Inc. PayPal Copyright 2014 PayPal Inc Apple Pay Copyright 2014 Apple Inc. Copyright 2015 Accenture All rights reserved. 3
The New Mental Model of Marketing in the Digital Age Who will buy what, where and when? Personal life Stimulus Key Drivers ZMOT Pre-shopping/ in-store/at-home First Moment of Truth Key Drivers Second Moment of Truth Experience Customers always connected Mail from a bank 40% Ad on TV 36% Newspaper 30% Search engine 61% Talked with friends/family 59% Bank website 58% Talked with a FS person Brochure in FS location Customer service (phone) 51% 43% 39% Professional life Source: Google/Shopper Sciences, Zero Moment of Truth Study Banking Copyright 2015 Accenture All rights reserved. 4
Finding the People like You
Create predictive power through people like you Add Big Data by asking the right questions Campaign yield +300-1000% vs control, addressable churn -25-50% Pricing power 50-100bp or up to 5-10% of fees Copyright 2015 Accenture All rights reserved. 6
Link Bank and Insurance Behaviour through the mapping of People like You Bank Customers Insurance Customers Average Age Copyright 2015 Accenture All rights reserved. SANITIZED CLIENT DATA 7
Combine social data with internal to find digital segments: Who are they, where do they live, how do they behave digitally and who are the influencers Vkontakte 2,5m profiles Demographics (name, date of birth) Facebook 124k profiles 1.7m clients 1.2m contacts Foursquare 19k profiles Contacts (mobile, email) Social network analysis to find opinion leaders (13.000 Alpha s) Digital micro-segments Where do they live and work? Copyright 2015 Accenture All rights reserved. 8
From a real-time view of all your markets, drill down to every street and house Micro-segment 1 Micro-segment 2 Micro-segment 3 Micro-segment 4 Micro-segment 5 Micro-segment 6 Micro-segment 7 Micro-segment 8 No Yes SANITIZED CLIENT DATA Copyright 2015 Accenture All rights reserved. 9
Go where your clients are The Branch of the Future is a Sofa or a Pop-up Copyright 2015 Accenture All rights reserved. 10
Internet of Things Devices like Cars, Homes and Wearables will Redefine User Experience, Channels, Services and provide Trillions of Contextual Data Points Copyright 2014 Accenture All rights reserved. 11
Smart Cities will be Distribution Channels 4 Visit seletected pavilions 3 Visit Italian Pavilion and lunch MyCity Digital Lounge 1 Expo 2015 Site App provides John with a map view of nearby bike stations 5 Social networking at Cirque du Soleil Supermarket of the Future and Innovation Corner by 2 You can add hospitality experiences for your clients
Big Data is revolutionizing Underwriting, Risk Scoring and Cap Market predictions as it exposes all the causal and correlated patterns Expect the leading firms to uncover the key unknown unknown patterns within 10 years For example: plastics for syringes predicting GDP, fresh vegetable prices predicting roof damage,... Below: large US Insurer Car Claims pattern data Unknown unknowns Known unknowns Known Fines for Age Marital status exceeding Accidents the speed limits Da y Ti m e Spe ed Curves and braking Mileage "Social " risk factor Text messa ges sent Call length claims value (Euro) Copyright 2015 Accenture All rights reserved. 13
Big Data Analytics for Insurance price optimization 1/4 Coast-to-Coast Accident Data Traditional actuarial data Telematics data Social media data External data (es. telephony) Copyright 2014 Accenture All rights reserved. 14
Big Data Analytics for Insurance price optimization 2/4 Adding Telematics data Age Marital status Fines for exceeding the speed limits Accidents Day Time Speed Curves and braking Mileage claims value (Euros) High value claims are not correlated to mileage, but to breaking, curves and speed. In addition, in this group, most accidents happen early morning and late at night Traditional actuarial data Telematics data Social media data External data (es. telephony) Copyright 2014 Accenture All rights reserved. 15
Big Data Analytics for Insurance price optimization 3/4 Adding Telco operator data Fines for exceeding Age Marital status the speed limits Accidents Day Time Speed Curves and braking Mileage "Social" riskf factor Text messages sent Call lenght claims value (Euro) Traditional actuarial data Telematics data Social media data External data (es. telephony) Copyright 2014 Accenture All rights reserved. 16
Big Data Analytics for Insurance price optimization 4/4 Adding Social media Analysis (Facebook conversations) Copyright 2014 Accenture All rights reserved. 17
Social Media Listening and Social Network Analysis
Use Social Network Analysis to understand Client Relations and Needs (European P&C and Life Insurers) 1 Only considering corporate relationships 2 Also considering strong transactional relationships SMEs community Work, 3% Professional network, 12% Acquaintance, 20% Family, 35% Neighborhood, 47% Key Finding: 55% of customers were related to one another. Customers classified as Alpha/Omega and Leader/Follower Alpha-user and Alpha-user and Bridges Bridges Other roles Othe Princes, 3 Omega-user Alpha-user Omega-user Alpha s and and Bridges Stakes Other and roles Omega-user Omega s Bridges Weather-vanes Considering all kind of relationships -user -user -user -user -user -user -user -user -user
Credit in partnership between bank and crowd Big Data-based STP credit scoring even for small SME loans Santander UK Funding Circle partnership Goldman Sachs setting up their own bank after helping Lending Club s IPO But, future blockchain IPO book building in the crowd? Copyright 2014 Accenture All rights reserved. 20
Monetize Big Data insights from Payments and Cards through merchant and customer communities Member banks silent attrition triggers Retail co-branded cards affinity value propositions Hotel classification Airline trends Customer taxi demand behavior Link, Like, Love app Go Social app Lifestyle and Spend Communities Internally implemented by the client Copyright 2015 Accenture All rights reserved. 21
The best Financial Advice is given in the moment of the experience A Moven-based Banking Experience Exclusively by Accenture 22
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