The Digital Bank Powered by Analytics. Agoria 4 th of November Edwin Van der Ouderaa

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1 The Digital Bank Powered by Analytics Agoria 4 th of November Edwin Van der Ouderaa

2 The bank of the future will be Digital or it will not be The Everyday Bank is currently the dominant new business model The benchmark in sales effectiveness used to be a NBA-driven sales conversation once a month Banks with good internet channels have a touch point every week that they can use for sales Strong smartphone banking gives a touch point every day Consumer 3.0 buys 50%+ digital (search, selection, price discovery, purchase). The Everyday Bank needs to be part of the value chain of the digital ecosystem to stay relevant A digital bank is: Always on, sensing 24*7 inside and outside the firewall Real-time: NBA, pricing, risk selection, STP, capital consumption and liquidity Wearable and omni-channel, focusing on the Zero Moment of Truth Works with Pull instead of Push Zero Moment of Truth Copyright Google Jim Lecinski Copyright 2013 Accenture. All rights reserved. 2

3 Mr and Mrs Consumer 3.0 are driving the new digital behavior The change in behavior is happening fast across every demographic with a 73% increase in those using the internet for research and purchase in the last 3 years Bank are seen as a facilitator, not a destination 80% use Smartphone for shopping search 70% uses the Smartphone in the shop 80% of customers trust peer and crowd recommendations. They also participate in communities of tens and hundreds of millions Only 14% trust store employees. People prefer social on-line advise in a 4:1 margin over commercial recommendations Only 20% shop for brand over price but 64% will spend 5-10 minutes surfing for a better price even after the initial on-line price discovery However, personal advise is allowed to cost 8 to 15% where it is critical and adds value Sources: Accenture _Customer3_Final compilation of studies and Multi-channel Distribution Surveys Copyright 2013 Accenture. All rights reserved. 3

4 Behavioral micro-segmentation informing a bottom-up distribution strategy and individual Next Best Action (1) High Value Seniors 40k customers 350 value (3) Mass Med Value 700k customers 55 value (5) High Value Adults 50k customers 330 value (2) Mass Seniors 600k customers 120 value (4) Youth 130k customers 25 value (6) High APH, Low Value 30k customers value Copyright 2013 Accenture. All rights reserved. 4

5 Sberbank Big Data pilot project Collecting social data Searching criteria Available records quantity We ve created custom java tool to: search selected social networks for profiles that matches available client data download all publicly available data for these profiles as it is. Profiles found by search criteria Vkontakte Foursquare Facebook Demographic s (name, birthdate) 1.7 mln. clients 2.5 mln. profiles 300k profiles with high matching probability Contacts (mobile, ) 1.2 mln. contact s 19k profiles 124k profiles

6 Sberbank Big Data pilot project Processing social data ~1TB Raw data Hadoop pilot cluster 13 machines, 208 cores and 65 TB disk space in total Reports We ve stored all available data on pilot cluster Merged it into several big files (what is optimal for HDFS) Created java classes for access to source pieces of data Implemented MapReduce tasks for: Precise matching of clients with social profiles Calculation of analytical attributes to ease further analysis of data Identifying family groups of clients Identifying techogeeks Identifying opinion leaders Ran these tasks

7 Sberbank Big Data pilot project Activity in Vkontakte per demographic groups

8 Some findings: 2017 clients with high technologies adoption potential were identified Techno-geek check-list: lives in a city with population count in millions, graduated from technical university, male, years old, technical words in interests, posts, shares. Sberbank Big Data pilot project Clients with potential to adopt new technologies

9 Sberbank Big Data pilot project Sberbank Big Data pilot project Opinion leaders 36 Opinion leaders among Sberbank clients were identified Average profile of Sberbank client in Foursquare Works in center, lives close to city border Most shopping is done near living place Use underground Prefer bars and cafés to restaurants Prefer sport entertainment to art Prefer parks within city bounds for recreation

10 To establish a customer focussed Analytics CoE with 4 key priorities What has been the approach? I want my Bank to understand my needs when they contact me I want a consistent experience across all touch-points Our customer DIRECT MAIL CONTACT CENTRE BRANCH WEBSITE MOBILE /TABLET Centre of Excellence Generate Insights Manage Campaigns Define Propositions Embed Analytics Comprehensive view of the Targeted campaigns with Data driven macro Enhance decision making * * customer base, new KPI s propensity model boosters * * segments identified (insight based not instinct) Detailed profiling of End-to-end measurement Propositions tailored to Support additional business * * * actionable sub-segments meet needs * areas and geographies

11 We have shifted the focus from product to customer Leading the shift to customer-centricity and insight based decision making Expanding our understanding of the customer base Monitoring trends and movements Embedding insights in the organisation

12 High Level Multi-Dimensional Segmentation Approach Customer Data Analysis Micro-Segment Generation Demographics Sample sub-segment profiling Early Professionals Mobile Transactors High Savers Pop 20k Credit Grade Avg. Dep, Inv & CR Bal Behaviours APH 2.7 Avg. Lending Balance Curr. Acc. Holding Channel Usage Lifetime Value

13 Targeted campaigns are driving sales uplift and we continue to focus on increasing execution across more channels Revenue Agenda Increasing volume of targeted campaigns month on month Proven sales uplift (Q4 12 to Q1 13) Strong campaign uplift versus control group Embedding Analytics Provided insights across multiple business areas in Q1 focus on producing tangible business benefits Our focus now is to expand our data set and drive more campaigns to more channels with closer to real-time feedback and execution

14 Proposition Development Process using insights from data From Macro Groups down to individual college campus branches Macro Sample Campus Population Campus Population Share 07 v 11 Share 07v 11 Main Bank Account BoI Customer X% Recent product taken out in other branch Online % Mirco XK 4K UGrad Resourcing (Student Store) Advisory X Per 000 students Manager Per 000 X student customer Classification: Amber 1K XK PGrad 1K XK Staff Staff 50% A% 42% B% BoI A 50% C% 42% D% AIBB XK Customer Activity FY % NPS XK XK XK Stud Grad Prem Main Stud % Main Base FNRs % Grad Base FNRs % Student Base FNRs Grad Main APH Stud Grad Main 14

15 Why BBVA wanted to become an omni-channel bank: Capturing the Zero Moment of Truth Customer-centric Omni-channel Main objectives business model, not only for retail banking but also for the wholesale sector, with distribution models that are leaders in efficiency and highly Revenue Growth More efficient distribution model Customer Experience leveraged on innovation and technology. Source: BBVA Corporate Mission Event based & multichannel distribution Real-time engines Managing the ZMOT Personalized pricing in every channel More tailored transaction proposals Decrease operations done inside channels Remote relationship managers for more efficient selling Reuse the wealth of what is already available Voice of the customer Lean processes Channel experience Customer experience to include a more personalized service/ ideas in line with client interests Source: BBVA, Accenture analysis ZMOT Copyright Google Jim Lecinski Copyright 2013 Accenture All rights reserved. 15

16 Personalization of proposition based on Real Time Next Best Action Social Technical Public Geo-positioning and mobile ATM, voice and other channels A Recent events (real time or near real time) Monthly batch information B He has deposited an extraordinary amount of money in the current account He has browsed the Bank s web on deposits and investment funds sections Recent complaint because high level of commissions His wife has cancelled her payroll account Advanced Investment Profile (diversifier) Low cash (end of month balance low) and high level of expenditure in the last three months Has business with online banks Traditional channels: and phone In his shared Facebook profile he says he would like to go to the next M. Knopfler concert 5 Mr Smith Input info Bank Neo Metrics Higher conversion rates Bank s Offer Real-time NBA (A+ B driven) Output offer VS Traditional analytics (only B driven) Lower conversion rates 16

17 4,3 bln$ e-mortgages written in less than 2 years using augmented reality and GPS Up to 50% branch resources moved to virtual bank and personalized support Smartphone App Virtual branch and paperless mortgage

18 Big Data Analytics and Monetization Telecom internal churn prediction and prevention * 2 and cross-sell * 3 Sale of geo-flows per micro-segment to retailers, FMCG and FS institutions DAP: Digital Analytics Platform