the digital bank - agoria · sberbank big data pilot project processing social data ~1tb raw data...
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The Digital BankPowered by Analytics
Agoria4th of November 2013 1.3Edwin Van der Ouderaa
• 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
The bank of the future will be Digital or it will not be
The “Everyday Bank” is currently the dominant new business model
Zero Moment of Truth Copyright © Google Jim LecinskiCopyright © 2013 Accenture. All rights reserved. 2
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
Sources: Accenture 13-2848_Customer3_Final compilation of studies and Multi-channel Distribution Surveys 2012-2013
• 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
Copyright © 2013 Accenture. All rights reserved. 3
Behavioral micro-segmentation informing a bottom-up distribution strategy and individual Next Best Action
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5
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• 50k customers• €330 value• …
(6) High APH, Low Value
• 30k customers• -€380 value• …
(1) High Value Seniors
• 40k customers• €350 value• …
(2) Mass Seniors
• 600k customers• €120 value• …
(3) Mass Med Value
• 700k customers• €55 value• …
(4) Youth• 130k customers• €25 value• …
(5) High ValueAdults
Copyright © 2013 Accenture. All rights reserved. 4
Demographics (name, birthdate)
Contacts (mobile, email)
Vkontakte Foursquare Facebook
1.7 mln. clients
1.2 mln. contact
s
300k profiles with high matching probability
2.5 mln.profiles
19k profiles
124k profiles
Searching criteria
Availablerecordsquantity
Profiles found by search criteria
Sberbank Big Data pilot projectCollecting social data
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.
Sberbank Big Data pilot projectProcessing social data
~1TB Raw data
Hadoop pilot cluster13 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
Activity in Vkontakte per demographic groups
Sberbank Big Data pilot project
Clients with potential to adopt new technologies
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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
Sberbank Big Data pilot projectSberbank Big Data pilot project
Generate Insights
• Comprehensive view of the customer base, new KPI’s
• Detailed profiling of actionable sub-segments
Manage Campaigns
• Targeted campaigns with propensity model ‘boosters’
• End-to-end measurement
Define Propositions
• Data driven ‘macro’ segments identified
• Propositions tailored to meet needs
Embed Analytics
• Enhance decision making (insight based not instinct)
• Support additional business areas and geographies
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** * ** * * *
Centre of Excellence
To establish a customer focussed Analytics CoE with 4 key priorities…
Our customer
DIRECT MAIL EMAIL CONTACT CENTRE
BRANCH WEBSITE MOBILE /TABLET
“I want my Bank to understand my needs when they contact me”
“I want a consistent experience across all touch-points”
What has been the approach?
� Expanding our understanding of the ‘customer base’
� Monitoring trends and movements
� Embedding insights in the organisation
We have shifted the focus from ‘product’ to ‘customer’Leading the shift to customer-centricity and insight based decision making
Sample sub-segment profiling
Early Professionals 20k20k
APH
2.72.7
Mobile Transactors
High Savers
Credit Grade
Avg. Dep, Inv & CR Bal
Avg. Lending Balance
Channel Usage
Curr. Acc. Holding
Pop
High Level Multi-Dimensional Segmentation Approach
Customer Data Analysis
Demographics
Behaviours
Lifetime Value
Micro-Segment Generation
� Increasing volume of targeted campaigns month on month
� Proven sales uplift (Q4 ‘12 to Q1 ’13)
� Strong campaign uplift versus control group
� Provided insights across multiple business areas in Q1 – focus on producing tangible business benefits
Revenue Agenda
Embedding Analytics
Our focus now is to expand our data set and drive m ore campaigns to more channels with closer to real-time feedback and exec ution
Targeted campaigns are driving sales uplift and we continue to focus on increasing execution across more channels
14Classification: Amber
Proposition Development Process – using insights from dataFrom Macro Groups down to individual college campus branches
Online %
Stud Grad
Main
BoI Customer
Resourcing (Student Store)Customer ActivityFY2012
XKXK XK
XK
Per 000
studentsX
% Main Base FNRs
% Student Base FNRs
Share ’07v’11
50% 42%
BoI AIB
Campus
Population
4K 1K
UG
1K
Gr Staff
50% 42%
Stud Grad Prem Main
Advisory
Manager Per 000
student
customerX
1.24 2.02 2.51
Stud Grad Main
Share ’07v’11
50% 42%
BoI AIB
Campus
Population
4K 1K
UG
1K
Gr Staff
50% 42%
Share ’07 v ’11
A% B%
A B
Campus
Population
XKXK
UGrad
XK
PGrad Staff
C% D%
% Grad Base FNRs
45%
NPS
Main Bank Account X% Recent product
taken out in other branch
Sample
Macro
Mirco
APH
Why BBVA wanted to become an omni-channel bank:Capturing the Zero Moment of Truth
“Customer-centric Omni-channel
business model, not only for retail
banking but also for the wholesale
sector, with distribution models that
are leaders in efficiency and highly
leveraged on innovation and
technology.”Source: BBVA Corporate Mission
Main objectives
Customer Experience
More efficient
distribution model
RevenueGrowth
� Voice of the customer
� Lean processes
� Channel experience
� Customer experienceto include a more personalized service/ ideas in line with clientinterests
� Decrease operations done inside channels
� Remote relationshipmanagers for more efficient selling
� Reuse the wealth of what is alreadyavailable
� Event based & multi-channel distribution
� Real-time engines
� Managing the ZMOT
� Personalized pricing in every channel
� More tailoredtransaction proposals
Source: BBVA, Accenture analysisZMOT Copyright © Google Jim Lecinski
Copyright © 2013 Accenture All rights reserved. 15
Personalization of proposition based onReal Time Next Best Action
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Neo Metrics
Monthly batch informationRecent events (real time or near real time)
Bank
Traditional analytics (only B driven)
Real-time NBA (A+ B driven)
Mr Smith
Bank ´s Offer
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
In his shared Facebook profile he says he would like to go to the next M. Knopfler concert
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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 : email and phone
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5
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Higher conversion
rates
Lower conversion
rates
Input info
Output offer
VS
A B
Social Technical Public
Geo-positioningand mobile
ATM, voiceand other channels
4,3 bln$ e-mortgages written in less than 2 years using augmented reality and GPS
Virtual branch and paperless mortgage
Smartphone App
Up to 50% branch resources moved to virtual bank and personalized support
DAP: Digital Analytics Platform
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, F MCG and FS institutions