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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4

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3

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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2017

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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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1

2

3

4

5

4

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