Hadoop Summit 2015

1. Customer
Conversa-on
Modeling
Hadoop
Summit,
June
11th
2015

2. 2
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2015
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§ Big
Data
Technology
§ ArCficial
intelligence
§ Complex
AnalyCcs
and
BI
§ Decision
systems
EXPERTISE
Introducing
NGDATA
§ 70+
employees
§ Offices
in
the
US
and
Europe
§ SoTware
Vendor
–
on-­‐premise
enterprise
COMPANY
Lily
Enterprise
§ OperaConal,
Omni-­‐channel
Customer
AnalyCcal
Profile
Data
Management
PlaYorm
ECOSYSTEM
Transforming
Customer
Experience
in
Financial
Services
and
Communica-ons
§ Global
partnerships
with
pioneers
and
blue
chip
customers
in
Financial
Services
and
CommunicaCons
PRESENCE

3. 3
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2015
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permission
Relevancy:
What
Customer
Experience
is
All
About
OFFER
THE RIGHT
PERSON
THE RIGHT
TIME
THE RIGHT
CHANNEL
THE RIGHT
IMPROVED FREQUENCY
IMPROVED SEPARATION

4. 4
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2015
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permission
The
Customer
Journey
A
Con&nuous
Conversa&on
CUSTOMER
LIFETIME
ATTRITION
ACTIVATION
PERSONAL
ADVISE
CUSTOMER
SUPPORT
UP
SELLING
PERSONAL
ADS
PARTNER
PROGRAMS
RISK
PROGRAMS
CHURN
REDUCTION
ACQUISITION

5. 5
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2015
NGDATA
®,
Inc.
ConfidenCal
–
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permission
Op-mizing
the
Customer
Conversa-on
Central
Data
Availability
for
Consistency
CUSTOMER
LIFETIME
ATTRITION
ACTIVATION
MARGIN
PERSONAL
ADVISE
CUSTOMER
SUPPORT
UP
SELLING
PERSONAL
ADS
PARTNER
PROGRAMS
RISK
PROGRAMS
CHURN
REDUCTION
360
view
for
advisor
Content
recommendaCon
Micro
campaigns
Anonymous
Call
predicCons
Script
recommendaCons
Online
offers
121
Campaign
Personalized
ads
Personalized
Social
Support
partners
apps
Merchant
offers
AcriCon
programs
IdenCfy
Fraud
ACQUISITION

6. 6
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2015
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–
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permission
Customer
Conversa-on
Modeling
4
Steps
1.
customer
idenCficaCon
2.
customer
profiling
3.
customer
behavior
analysis
4.
acConability
feedback

7. 7
Copyright
2015
NGDATA
®,
Inc.
ConfidenCal
–
DistribuCon
prohibited
without
permission
Customer
Conversa-on
Modeling
Customer
Iden&fica&on
1.
customer
idenCficaCon
2.
customer
profiling
3.
customer
behavior
analysis
4.
acConability
feedback
§ anonymous
vs
idenCfied
§ browser
fingerprint,
cookie
id,
internal
id
§ device
vs
person
§ person
vs
household

8. 8
Copyright
2015
NGDATA
®,
Inc.
ConfidenCal
–
DistribuCon
prohibited
without
permission
Customer
Conversa-on
Modeling
Customer
Profiling
1.
customer
idenCficaCon
2.
customer
profiling
3.
customer
behavior
analysis
4.
acConability
feedback
§ master
composiCon
§ customer-­‐level
metrics
§ temporal
and
other
dimensions
§ conCnuous,
real-­‐Cme
scoring
of
metrics
CONTRIBUTED+ OBSERVED+
CALCULATED+ PREDICTED+
Fields' Aggregates'
Formulas' Scores'
Facts+
CRM+
DWH+
Custom+
Metrics+
Behavior+
TransacBons+
Clickstream+
PredicBve++Models+
Machine+Learning+

9. 9
Copyright
2015
NGDATA
®,
Inc.
ConfidenCal
–
DistribuCon
prohibited
without
permission
Customer
Conversa-on
Modeling
Customer
Behavioral
Analysis
1.
customer
idenCficaCon
2.
customer
profiling
3.
customer
behavior
analysis
4.
acConability
feedback
§ evoluCon
of
metrics
over
Cme
§ trend
&
acceleraCon
are
more
important
than
value
§ pacern
recogniCon
(e.g.
seasonal
behavior)

10. 10
Copyright
2015
NGDATA
®,
Inc.
ConfidenCal
–
DistribuCon
prohibited
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permission
Customer
Conversa-on
Modeling
Ac&onability
1.
customer
idenCficaCon
2.
customer
profiling
3.
customer
behavior
analysis
4.
acConability
feedback
§ anomaly
detecCon
§ alerts
triggering
acCons
§ business
rules
§ act
on
inacCvity
!

11. 11
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2015
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permission
Improve
Your
Conversa-ons
with
Customer
DNA
Inbound
Outbound
more
relevant
&
precise
target
lists
Mass
MarkeCng
Individual
individual-­‐level
data-­‐driven
approach,
alerts,
using
channel
preferences,
past
purchase
history
personalizaCon
Online
Service
Desk
anCcipaCon,
guided
conversaCon

12. 12
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2015
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CCM
with
Hadoop
Architecture
itx
enCty
metrics
calculaCon
engine
DNA
metrics
update
monitor
alert
manager
rule
engine
metric
updates
triggers
Flume
HDFS
KaCa
Zoo-­‐
keeper
Spark
models
HBase
Phoenix
Calcite
lambda architecture

13. 13
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2015
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Storing
Data
▪ customer-level multi-dimensional primitive aggregates
− time travel
− day > week > month
− efficiency at update & retrieval
▪ on-the-fly metrics representation

14. 14
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2015
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Interes-ng
Conversa-onal
Metrics
▪ next-best-action scoring
▪ per-offer propensity scoring
▪ journey scores
! distinction between ‘slow’ (classic) and
‘fast’ metrics
− socio-demographic (slow): define WHAT to do
− behavior-based (fast): define WHEN & HOW
(channel)
▪ Leverage the complete and up-to-
date profiles through metrics
▪ Use DNA to capture intentions and
apply alerts to trigger actions
▪ Avoid rule engine nightmare and
keep rules as simple as possible
▪ Avoid post scoring rules – focus
on actions
€

15. 15
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2015
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Advanced
CCM
▪ cross-metric pattern alignment
▪ cliques: group behavior (cluster / graph analysis)
− social leaders
− households
− network effects
▪ automatic, unsupervised segmentation

16. 16
Copyright
2015
NGDATA
®,
Inc.
ConfidenCal
–
DistribuCon
prohibited
without
permission
Customer
DNA:
A
Fundamental
Break
from
the
Past
Tradi&onal Approaches
Lily Opera&onalizes Analy&cs
Real
Time
ConCnuous
Long
Data
IntegraCon
Slow
Update
cycles
Limited
Data
Volumes
Incomplete
Customer
picture
Manual
Work,
Limited
audience
Expensive
Specialized
Staffing
SegmentaCon,
Low
precision
Atomic
Level
Complete
View
Automated
Learning
Omni
Channel
Impact
One
SoluCon
MulC
Use

17. Ques-ons?
stevenn@ngdata.com