The Digital Bank
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Agoria
4th of November 2013 1.3
Edwin Van der Ouderaa
The bank of the future will be Digital or it will not be
The “Everyday Bank” is currently the dominant new business model
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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:
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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.
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Mr and Mrs Consumer 3.0 are driving the new digital behavior
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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 13-2848_Customer3_Final compilation of studies and Multi-channel Distribution Surveys 2012-2013
Copyright © 2013 Accenture. All rights reserved.
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Behavioral micro-segmentation informing a bottom-up distribution
strategy and individual Next Best Action
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5
1
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3
(1) High Value
Seniors
(2) Mass
Seniors
• 40k customers
• €350 value
• …
• 600k customers
• €120 value
• …
Copyright © 2013 Accenture. All rights reserved.
(3) Mass Med
Value
• 700k customers
• €55 value
• …
(5) High Value
Adults
• 50k customers
• €330 value
• …
(4) Youth
• 130k customers
• €25 value
• …
(6) High APH,
Low Value
• 30k customers
• -€380 value
• …
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Sberbank Big Data pilot project
Collecting social data
We’ve created custom java tool to:
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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
Searching
criteria
Demographic
s (name,
birthdate)
Contacts
(mobile, email)
Available
records
quantity
1.7 mln.
clients
1.2 mln.
contact
s
Vkontakte
Foursquare
Facebook
2.5 mln.
profiles
300k profiles
with high
matching
probability
19k
profiles
124k
profiles
Sberbank Big Data pilot project
Processing social data
~1TB Raw data
• 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
Hadoop pilot cluster
• Implemented MapReduce tasks for:
13 machines,
208 cores and
65 TB disk space in total
• 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
Reports
• Ran these tasks
Sberbank Big Data pilot project
Activity in Vkontakte per demographic groups
Clients with potential to adopt new technologies
Some findings:
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2017 clients with high technologies adoption potential were identified
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Techno-geek check-list: lives in a city with population count in millions,
graduated from technical university, male, 22-34 years old, technical words
in interests, posts, shares.
Sberbank Big Data pilot project
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
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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
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
EMAIL
BRANCH
WEBSITE
MOBILE /TABLET
Centre of Excellence
Generate Insights
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Comprehensive view of the
customer base, new KPI’s
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Detailed profiling of
actionable sub-segments
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Manage Campaigns
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Targeted campaigns with
propensity model ‘boosters’
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End-to-end measurement
3
Define Propositions
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Data driven ‘macro’
segments identified
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Propositions tailored to
meet needs
Embed Analytics
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Enhance decision making
(insight based not instinct)
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Support additional business
areas and geographies
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
High Level Multi-Dimensional Segmentation Approach
Customer Data Analysis
Micro-Segment Generation
Demographics
Sample sub-segment profiling
Credit Grade
Early
Professionals
Mobile
Transactors
High
Savers
Pop
20k
Avg. Dep, Inv & CR Bal
Avg. Lending Balance
Behaviours
APH
Curr. Acc. Holding
2.7
Channel Usage
Lifetime Value
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
Proposition Development Process – using insights from data
From Macro Groups down to individual college campus branches
Macro
Sample
Share ’07 v ’11
Campus
Campus
Population
Share ’07v’11
Main Bank Account
Population
XK
4K
UGrad
UG
Mirco
XK
1K
1K
XK
PGrad
Staff
Gr
Staff
50% 42%
A% B%
BoI
A
X
Manager
X
Classification: Amber
X% Recent product
taken out in other branch
XK
XK
XK
XK
Stud
Stud Grad Prem Main
AIBB
Customer Activity
Resourcing (Student Store)
Advisory
50%
D%
C% 42%
Online %
BoI Customer
Main
APH
FY2012
Per 000
students
Per 000
student
customer
Grad
% Main
Base FNRs
45%
NPS
1.24
2.02
2.51
Stud
Grad
Main
% Grad Base
FNRs
% Student
Base FNRs
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Why BBVA wanted to become an omni-channel bank:
Capturing the Zero Moment of Truth
Main objectives
“Customer-centric Omni-channel
business model, not only for retail
banking but also for the wholesale
sector, with distribution models that
Revenue
Growth
are leaders in efficiency and highly
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
More
efficient
distribution
model
Decrease operations
done inside channels
Remote relationship
managers for more
efficient selling
Reuse the wealth of
what is already
available
Customer
Experience
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.
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Personalization of proposition based on
Real Time Next Best Action
Social
Technical
Geo-positioning
and mobile
A
Public
ATM, voice
and other channels
Recent events (real time or near real time)
Monthly batch information
B
He has deposited an extraordinary amount of money in the
current account
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Advanced Investment Profile (diversifier)
He has browsed the Bank´s web on deposits and investment
funds sections
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Low cash (end of month balance low) and high level
of expenditure in the last three months
Recent complaint because high level of commissions
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His wife has cancelled her payroll account
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In his shared Facebook profile he says he would like to go to
the next M. Knopfler concert
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3
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Mr Smith
Bank´s Offer
Higher
conversion
rates
Input
info
Has business with online banks
Traditional channels: email and phone
Bank
Neo Metrics
Output
offer
Real-time NBA
Traditional analytics
(A+ B driven)
(only B driven)
Lower
conversion
rates
VS
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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
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