The Future of Finance.

1. From
Data
to
Impact
Big
Data
Marke,ng
Use
Cases
for
the
Finance
Industry
12
May
2015,
Wijs

2. 2
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What
is
going
on?
Forces
at
Work
Personal
Data
Protec,on
&
Privacy
Lowering
Cost
of
Data
Infrastructure
Teradata
Cloudera
Hortonworks
EMC
Business
Intelligence
is
becoming
Data
Science
Reporting
SPSS / SAS
Python Pandas
R
Spark
Online
self-­‐
service
profiling
&
targe,ng
prolifera,on
Facebook Atlas
Google Ads
Campaigns
Agencies
The
Customer
at
the
Center
Conversation
In-boundOut-bound
CRM
Millenials
@
The
Customer
Side
Co-creation
Ecosystems
Programs
Waterfall

3. 3
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What
is
going
on?
The
Ba/lefields
of
Data

4. 4
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Banks
vs
Digitals
Personal
Same
for
everyone
Fast
Slow
Intui,ve
Sta,c
Integrated
Siloed
Everywhere
Have
to
search
for
what
I
need
Relevant
Doesn’t
surprise
me

5. 5
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The
Big
Enterprise
Challenge
Data
Silos

6. 6
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The
Status
Quo
is
Limited
to
Insights
Gave
up
on
Customer
360
a]er
large
investments
in
Datawarehouses
Use
hindsight
in
BI/
Analy,cs
solu,ons
building
complex
diagnos,c
models
for
customer
segmenta,on
Hire
an
army
of
data
scien,st
to
use
big
data
and
visualiza,on
tools
to
discover
insights
Rely
on
Rule
Engines
to
apply
segmenta,on
for
recommenda,ons
and
targe,ng
Most
Many
Several
Few
Rowan
Curran,
March
2015,
Forrester
Research:
„Digital
experience
delivery
vendors
have
generally
fallen
short
in
their
use
of
predic>ve
analy>cs
to
contextualize
digital
customer
experiences.
Many
of
these
vendors
offer
simple,
rules-­‐based
recommenda>ons,
segmenta>on,
and
targe>ng
that
are
usually
limited
to
a
single
customer
touchpoint.”

7. 7
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How
You
Measure
Success
IMPACTRELEVANCY

8. 8
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Relevancy
–
What
Customer
Experience
is
All
About
PuHng
the
Customer
Upfront
and
Central
OFFER
THE RIGHT
PERSON
THE RIGHT
TIME
THE RIGHT
CHANNEL
THE RIGHT
IMPROVED FREQUENCY
IMPROVED SEPARATION

9. 9
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processing
flow
key functions
The
Google/Facebook/LinkedIn
Architecture
Customer
centric:
Profiling,
Analy>cs
and
Ac>ons
@
the
Speed
of
Light
streaming ingest
user identification
behavior observation &
tracking
profile establishment
targeting support: preference learning
& contextualization
micro-segmentation
network analysis
service delivery (newsfeeds, timelines,
search, check-ins, ads …)
data
layer
consumer
data capturing & ingestion profiling & service enablement customer experience
online transaction and analytical processing on shared data platform
real-time / in-session /
user-level analytics,
scoring & targeting (for
ad, service, next best
offer, recommendations)
model training
collaborative learning
deep learning
reporting
operational processing
channels
portal
mobile
ads
…
service
applications
interactive
service calls
behavioral feedback data
service
interactionuser behavior
observations
(streaming)
data flows
(streaming)
data flows
(streaming)
data flows
profile
enquiries

10. 10
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The
Big
Enterprise
Challenge
Enterprise
IT
Architecture
Where
is
the
customer?

11. 11
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Lily
Enterprise
OperaMonal
Customer
AnalyMcs
Data/Models
OperaMonal
Systems
External
Data
Contract/Product
Data
Customer
Opera,onal
Data
Reference
Data
ReporMng
/
AnalyMcs
Enterprise
BI
and
repor,ng
Enterprise
Analy,cs
Applica,ons
Marke,ng
and
Social
Data
Customer
Interac,on
Data
Campaign
Data
ERP/CRM
Data
Data
Warehouse
Data
Service
Desk
Customer
CRM
and
IVR
Systems
Web
and
Mobile
Mobile
Apps
Customer
Website
Channel
/
Campaigns
Mail
SMS
Print
Broadcast
Marke,ng
Campaign
Mgt
Sales
Office
Agent
/
Advisor
Structured
Unstructured
Online
Feedback
External
Social
Partner
Apps
Partners
Apps
Social
Media
Structured
Unstructured
Input
“MANAGE
CHAOS“
–
Manage
core
metrics,
don’t
try
to
control
everything
Ken
Rudin,
Director
of
Analy,cs,
Plugged
in
Enterprise
Architecture
–
Improving
exis>ng
BI
landscape

12. 12
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customer removes multiple
products from portfolio
6
OCT
customer
churns
11
NOV
manual attrition score (bi-monthly)
portfolio size (weekly)
The
Importance
of
Real-­‐Mme
Customer
DNA
&
Scoring
Figh>ng
A/ri>on
Before
it
is
Too
Late

13. 13
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win-back period
customer removes multiple
products from portfolio
6
OCT
customer
churns
11
NOV
win-back sensitivity
manual attrition score (bi-monthly)
portfolio size (weekly)
The
Importance
of
Real-­‐Mme
Customer
DNA
&
Scoring
Factor
In
Win-­‐back
Sensi>vity

14. 14
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win-back period
win-back sensitivity
Lily attrition score (continuous)
portfolio size (weekly)
customer
churns
11
NOV
customer
retention
actions
Lily alerts for in-
creased attrition risk
customer removes multiple
products from portfolio
6
OCT
The
Importance
of
Real-­‐Mme
Customer
DNA
&
Scoring
Timely
Alerts
and
Ac>ons
for
the
Greatest
Impact

15. 15
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2015
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One
Customer
DNA
that
Serves
Many
Use
Cases
Enterprise
side
of
the
equa>on:
“CLTV”
CUSTOMER
LIFETIME
ATTRITION
ACTIVATION
MARGIN
PERSONAL
ADVISE
CUSTOMER
SUPPORT
UP
SELLING
PERSONAL
ADS
PARTNER
PROGRAMS
RISK
PROGRAMS
CHURN
REDUCTION
ACQUISITION
360
view
for
advisor
Content
recommenda,on
Micro
campaigns
Anonymous
Call
predic,ons
Script
recommenda,ons
Online
offers
121
Campaign
Personalized
ads
Personalized
Social
Support
partners
apps
Merchant
offers
Akri,on
programs
Iden,fy
Fraud

16. 16
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Case
1:
Increased
customer
value
and
reduced
helpdesk
calls
Predict who is going to call and what their issue will be...
And take action before they call
HAPPY
CUSTOMERS
FEWER
CALLS
HUGE
SAVINGS
A
personally
relevant
video
is
delivered
based
on:
• Customer
data
• Specific
solu,on
• Preferred
products
RESULTS

17. 17
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Case
2:
Decreasing
A[riMon
for
Retail
Bank
• Created
thresholds
and
set
alerts
based
on
con,nuous
trending
scores
on
all
available
data
and
delivered
more
predic,ve
ac,ons.
• Alerts
sent
to
bank’s
outbound
systems
to
take
ac,ons
reducing
akri,on
by
10%
Result
• Compe,,ve
pressure
on
the
retail
business
• Need
to
substan,ally
lower
akri,on
rate
(22%)
• Increase
customer
life,me
value
Objec,ves
• Aggregated
all
customer
data
(ATM,
branch,
call
center,
web,
mobile,
payment
system,
etc.)
• Built
individual
Customer
DNA
based
on
hundreds
of
metrics
• Focused
on
the
high
value
customers
(HVC)
based
on
CLTV
metric
• Informed
outbound
systems
of
HVCs
at
risk
based
on
con,nuous
akri,on
scoring
Solu,on
“
NGDATA
is
cri>cal
in
the
way
we
capture,
analyze
and
generate
ac>onable
intelligence
from
Big
Data.
With
Lily
in
place,
we
were
able
to
find
and
act
on
the
customers
most
at
risk
of
a/ri>on
in
a
>mely
and
effec>ve
manner.”
—
CIO,
Large
InternaMonal
Bank

18. 18
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2015
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Case
3:
Merchant-­‐funded
Mobile
Offers
Fortune
50
US
Retail
Bank
Individual
Coupon
delivery
–
Average
targe,ng
precision
increased
by
5-­‐7x,
results
in
increased
redemp,ons
and
loyalty
Result
• Improve
coupon
redemp,on
rate
through
real-­‐,me,
loca,on-­‐based
personalized
offers
Objec,ves
• Real-­‐,me
ingest
of
payment
transac,ons
• Behavior-­‐based
MCC
preference
learning
• Loca,on-­‐
and
preferences-­‐based
coupon
selec,on
&
delivery
in
mobile
wallet
• Evaluate
performance
between
collabora,ve
filtering
&
KB-­‐based
preference
learning
Solu,on
“
Introducing
Big
Data
and
Machine
Learning
not
only
resulted
in
higher
performance,
but
it
allowed
us
to
introduce
disrup>ve
business
concepts
and
opportuni>es.”
—
Senior
Vice
President

19. 19
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Case
4:
Customer
DNA
Large
US
Wealth
Management
Bank
Real
Time
AcMonable
Customer
DNA
–
Allows
agents
to
provide
beker
and
more
efficient
advice.
Building
increased
customer
loyalty
Result
• Improve
financial
advice
sugges,ng
the
right
investment
at
the
right
,me
to
the
right
customer
Objec,ves
• Real
,me
ingest
of
the
investment
history
of
the
customer
• Monitor
all
customer
interac,ons
(payments,
CC,
calls,
IVR,
mobile
and
online,
...
• Learning
on
new
investment
opportuni,es
• Develop
customer
DNA
and
preferences,
with
a
focus
on
the
poten,al
new
investments
in
line
with
the
individual
customer
profile
Solu,on
“
Tradi>onal
advice
channels
must
reinforce
the
value
of
comprehensive
planning
through
automated,
real-­‐>me
and
personalized
advisor
rela>onships
if
they
wish
to
maintain
their
margins
and
marketshare.”
—
Senior
Vice
President,
Customer
Intelligence

20. Thank
you!
Ques,ons?
stevenn@ngdata.com