Leverage Big Data to Enhance Customer Experience in Telecommunications – with Skytree and Hortonworks

© Hortonworks Inc. 2011 – 2014. All Rights Reserved
Webinar: Leveraging Big Data to Enhance
Customer Experience in Telecommunications
We Do Hadoop
Sanjay Kumar
General Manager, Telecom
Hortonworks
Alexander Gray, PhD
CTO
Skytree

The New Landscape of the Telecom Industry
Service
Providers
Social Media &
Mobile:
Explosion of rich
customer data through
Social Media and Mobile
Apps for customer
sentiment & Interests
Customer
Expectation:
With the cultural impact of
web and mobile,
customers are expecting
greater levels of service
and responsiveness
Competitive
Differentiation:
As other service
providers deliver similar
levels of telecom service
and coverage, other
areas of service levels
are needed
New Digital
Ecosystem:
Greater value of Data
on digital ecosystem for
Customers and
Partners driving Data
Monetization
Internet Of Things:
Explosion of data from IOT
with benefits aligned with
insight not correlated to
data volumes

Service Provider Focus
Service
Providers
Customer Experience Management
-  Enhance End-to-end Experience of Customer
-  Become Trusted Partner to Customer
-  Awareness of customer’s needs when and where needed
New Business & Consumer Services
-  New Digital & Infrastructure Services
-  Data Monetization
-  M2M, IoT, Analytics-as-a service
Network Optimization
-  Move to Software Driven Networks
-  Leverage Network Data Assets
-  Self optimizing and provisioning

Hortonworks in Telecom
Hortonworks. We do Hadoop.

Hadoop for the Enterprise:
Implement a Modern Data Architecture with HDP
Customer Momentum
•  330+ customers (as of year-end 2014)
Hortonworks Data Platform
•  Completely open multi-tenant platform for any app & any
data.
•  A centralized architecture of consistent enterprise services
for resource management, security, operations, and
governance.
Partner for Customer Success
•  Open source community leadership focus on enterprise
needs
•  Unrivaled world class support
•  Founded in 2011
•  Original 24 architects, developers,
operators of Hadoop from Yahoo!
•  600+ Employees
•  1000+ Ecosystem Partners

HDP delivers a completely open data platform
Hortonworks Data Platform 2.2
Hortonworks Data Platform provides Hadoop for the Enterprise: a centralized architecture
of core enterprise services, for any application and any data.
Completely Open
•  HDP incorporates every element
required of an enterprise data
platform: data storage, data
access, governance, security,
operations
•  All components are developed in
open source and then rigorously
tested, certified, and delivered as
an integrated open source platform
that’s easy to consume and use by
the enterprise and ecosystem.
YARN: Data Operating System
(Cluster Resource Management)
1 ° ° ° ° ° ° °
° ° ° ° ° ° ° °
ApachePig
° °
° °
° ° °
° ° °
HDFS
(Hadoop Distributed File System)
GOVERNANCE BATCH, INTERACTIVE & REAL-TIME DATA ACCESS
Apache Falcon
ApacheHive
Cascading
ApacheHBase
ApacheAccumulo
ApacheSolr
ApacheSpark
ApacheStorm
Apache Sqoop
Apache Flume
Apache Kafka
SECURITY
Apache Ranger
Apache Knox
Apache Falcon
OPERATIONS
Apache Ambari
Apache
Zookeeper
Apache Oozie

Traditional systems under pressure
Challenges
•  Constrains data to app
•  Can’t manage new data
•  Costly to Scale
Business Value
Clickstream
Geolocation
Web Data
Internet of Things
Docs, emails
Server logs
2012
2.8 Zettabytes
2020
40 Zettabytes
LAGGARDS
INDUSTRY
LEADERS
1
2 New Data
ERP CRM SCM
New
Traditional

Tomorrow: A Data-Centric Model for Your Business
DATA-CENTRIC
Limitations:
•  Multiple copies of data
•  Difficult cross-system integration
•  Upper-limit on data volumes
before harming performance
Advantages:
•  One version of the data
•  No need for cross-app integration
•  System scales linearly
APP-CENTRIC
App1 App 2 App 3 App 4 App 5 App 6
App Centric will break down
with x10, x100,x1000…
Need to shift to Data Centric

Social
Media
Sentiment
The View from the Customer
Call Center
Interaction
Quality
of
Service
Lifestyle &
Interests
Clickstream
Geolocation
Web Data
Internet of Things
Docs, emails
Server logs
Streaming: Network
Probes, Click Stream,
Sensor, Location
Batch: Call
Detail Records
On-Line: Customer
Sentiment
Unstructured: Txt,
Pictures, Video,
Voice2Text

DELIVERY
The Destination: Data-Centric Operations
Clickstream
Geolocation
Web Data
Internet of Things
Docs, emails
Server logs
Streaming: Network
Probes, Click Stream,
Sensor, Location
Batch: Call
Detail Records
On-Line: Customer
Sentiment
Unstructured: Txt,
Pictures, Video,
Voice2Text
Personal Data Analysis &
Customer Insight Services
To Customer & Partners
Hadoop Distribution with Yarn: Allows central source of data across all mediums of ingestion and interaction
Existing & Legacy Systems can Contribute and Participate: May extend the life of existing and legacy systems from enriched data
New Applications interact with Data Lake, not each other: Next Generation Apps build around data and can deliver to customers and partners

IT Operations
Business Functions
-  CEM
-  Marketing
-  Security
-  Network
`
Customer
Go-to-market
Communication Service Provider Adoption Journey
EDW
Data

Oﬄoad

HDP

Landing
Zone

HDP

DataLake
Real-‐7me
Streaming

HDP
DataLake

Network

Op7miza7on

Dynamic

Network

Provisioning

Top
Customer

Driven

Provisioning

Threat

Detec7on

Real-‐Time

Threat

Analy7cs

Dynamic

PaHern

Detec7on

Customer

Sen7ment

Dynamic

Customer

Proﬁle

360
Customer

Household
View

Loca7on
Based
CEM
&

Real-‐7me
customer

response

Context
Aware

Loca7on
Based

Promo7ons

Context
Aware

Target

Marke7ng

Next
Best

Ac7on

Cyber
Security

Analy0cs-‐as-‐a-‐service

Personal

intelligence

Hadoop-‐as-‐a-‐service

Industry

Brokering

M2M/IoT

Customer Experience Management & Marketing Journey
HDP

Landing
Zone

HDP

DataLake

Real-‐7me
Streaming

HDP
DataLake

Dynamic

Customer

Proﬁle

360
Customer

Awareness

&
Household

View

Loca0on
Based

CEM
&
Real-‐0me

customer
response
Next
Best

Ac0on

Customer

Sen0ment

Customer
Aware

Loca0on
Based

Promo0ons

Mul0-‐
channel

Customer

Scoring

Models

Data Centric Customer Experience Management
Functional
Area
Core Functional
Components
Description Problem Addressed Business Benefits
Customer
Experience
Manageme
nt
Central Data
Lake for 360
Customer
View
Visibility of customer household
view across services and
accounts through ingestion of
account service and event data
into a central Data Lake with
views into granular customer’s
service experience
-  Silo view of customer in
different systems
-  Social media unstructured
data does not fit into existing
EDW
-  Complete view into
customer experience
across all services
-  Reduction in Customer
Churn
-  Increased Loyalty
Dynamic
Customer
Profile
Summarized instant view of
customer across service
identifiers and customer key
performance metrics and ‘net
promoter scores’. Used for
immediate view of customer
profile
-  How to react to customer
contact & events based on
their experience
-  What is the customer’s
experience level
-  Next Best Action based on
customer’s experience with
service provider (Retail /
Call Center)
-  Greater targeted marketing/
advertising
Real-time
Event
Streaming for
Next Best
Action
Real-time streaming of network
event data to identify customer
location
-  How to determine next best
action when and where they
are most appropriate to a
customer
-  Marketing and CEM analysis
is after the fact; need for
real-time
-  Context sensitive
promotions 10x customer
acceptance
-  Improves customer
experience levels and
customer retention

HDFS
Raw
Event

Storage

CEM: Real-time Streaming, 360 Customer Data Lake and
Dynamic Customer Profile Solution
1
°
°

°
°
°

°
°

HBase
Processed

Event
Storage

°
°
°

°
°
°

°
°
N

°

Mul0tenant
Processing:
YARN

(Hadoop
Opera7ng
System)

Metadata
Management
HCatalog

Hive
/
Tez

(Interac7ve

Query)

ISV

(YARN
Apps,
i.e.

HPA
/
LASR)

Slider

(Always-‐on

Services)

HBase
/Accumulo

Real-‐0me
Serving

°
°
°

°
°
°

°
°
N

Streaming
Event
Processor:
Storm

Machine

Learning

(Spark)

Indexing

(Lucene)

Rules
Processing

(Drools)

In-‐Line
Memory

(Spark)

Message

Queues

Log

Files

Web

Services

JMS
Enrich
Events
with

Customer
info

And
Score
Matrix

Update
Data

Lake

Real-time Intelligent Action
-  Marketing Promotions
-  Next Best Action
-  Dynamic Network Provisioning
Network

Probe

Events

ODBC /
JDBC
Rest API
Native API
Messaging
Platrom:
Ka]a

Update Customer
Profile and Scores
External
Customer
Data

References

HDFS
Raw
Event

Storage

CEM: Real-time Streaming, 360 Customer Data Lake and
Dynamic Customer Profile Solution
1
°
°

°
°
°

°
°

HBase
Processed

Event
Storage

°
°
°

°
°
°

°
°
N

°

Mul0tenant
Processing:
YARN

(Hadoop
Opera7ng
System)

Metadata
Management
HCatalog

Hive
/
Tez

(Interac7ve

Query)

ISV

(YARN
Apps,
i.e.

HPA
/
LASR)

Slider

(Always-‐on

Services)

HBase
Processed

Event
Storage

°
°
°

°
°
°

°
°
N

Streaming
Event
Processor:
Storm

Machine

Learning

(Spark)

Indexing

(Lucene)

Rules
Processing

(Drools)

In-‐Line
Memory

(Spark)

Message

Queues

Log

Files

Web

Services

JMS
Enrich
Events
with

Customer
info

And
Score
Matrix

Update
Data

Lake

Real-time Intelligent Action
-  Marketing Promotions
-  Next Best Action
-  Dynamic Network Provisioning
Network

Probe

Events

ODBC /
JDBC
Rest API
Native API
Messaging
Platrom:
Ka]a

Update Customer
Profile and Scores
External
Customer
Data

References

ML to determine NPS &
other Scores/Metrics
ML Real
time event
score

Functional
Area
Example Use
Case
Hortonworks - Hadoop SkyTree – Machine Learning
Customer
Experience
Management
360 Degree
Customer &
Household View
- Computational
Net Promotor
Score & other
Customers Metrics
Collection data across sources into
Hadoop Data Lake for 360 degree view of
Customer and Household: Yarn enabled
Hadoop Architecture – Single set of data
across the entire cluster with multiple
access methods
Ingestion: Multiple sources of unstructured
and structured data include, CDR,
clickstream, network probe & log records,
sensor, IVR Voice-2-Text, social media,
OSS/BSS, etc
Process & Store: Yarn enabled Architecture
– Single set of data across the entire cluster
with multiple access methods. Distributed
storage in HDFS and many processed
workloads managed by Yarn
Query & Alerts: Schema on read allows
multiple methods for queries and alerts
through different applications or through
HDP tools (Hive, Hbase, Storm, etc)
Customer
Sentiment and
Churn Detection

Thank You!
Sanjay Kumar
General Manager, Telecom
Hortonworks

CONFIDENTIAL

Bigger Data. Better Insights.™
CONFIDENTIAL

Machine Learning and Telecom
Alexander Gray, PhD
CTO, Skytree

CONFIDENTIAL

Machine Learning on Big Data
Next step in Big Data Journey – AnalyEcs and Machine Learning to
Make BeFer Decisions:
-‐  Churn – From PredicEon to PrevenEon
-‐  Net Promoter Score
Requires a 360 Degree View of Customers

CONFIDENTIAL

External DataInternal Data
Big Data
Environment
DataData
Data warehouse
E-Mail
CRM
Single Customer View
with improved decision making
capabilities based on Customer data
Big Data
Enabling innovative products
& services, customer
satisfaction
Analytics
Churn propensity and prevention,
Product Sentiment, Recommendations
and more.
Customer 360o View

CONFIDENTIAL

TARGET
CONVERT
RETAIN

Advanced MarkeEng AnalyEcs
• Lead
Scoring

• Segmenta7on

• Ad
Op7miza7on

• Ad
Targe7ng

• Campaign
Op7miza7on

• Direct
Marke7ng

• Algorithmic
Pricing

• Recommenda7on/
Personaliza7on

• Promo/Coupon
Planning

• Cross/Upsell

• Clickstream

• Product/Service
Op7miza7on

• Market
Basket
Analysis

• Churn
Predic7on

• Spend
Behavior
Analysis

• Social
Media
Analysis

• Engagement/Cul7va7on

Op7miza7on

• Customer
Life7me
Valua7on

• Loyalty/Referral
Op7miza7on

Customer Lifecycle OpEmizaEon

CONFIDENTIAL

UElizing data: The tradiEonal approach

TradiEonally, human domain experts dig into the data via
– VisualizaEon tools
– Basic data analysis
– Querying a database to seek paFerns
– “Thinking hard” about the underlying processes
And extract insights, plots, and decision rules that uElize the paFerns they ﬁnd
“Tradi7onal
business
intelligence”

CONFIDENTIAL

UElizing data: The tradiEonal approach

Human experts are very good at asking certain kinds of quesEons, but they are
limited in the ways they can process data
This is the age of Big Data: lots of nontrivial paFerns, subtle, nonlinear relaEons
that are not visible to tradiEonal analyEcs and visualizaEon tools
Missed paFerns è Missed accuracy è Missed opportuniEes!

CONFIDENTIAL

UElizing data: Machine Learning
Machine Learning is the modern science of ﬁnding subtle, nonlinear
paFerns in data, that can be used to:
– PREDICT outcomes and guide acEons, e.g.:
•  Provide targeted recommendaEons to customers
•  Signal the need to service before equipment failure

– DISCOVER insights to inform decisions, e.g.:
•  Which variables among a set of thousands have the most weight in
determining an important outcome?

“Advanced
analy7cs”

CONFIDENTIAL

UElizing data: Machine Learning
Machine Learning is the modern science of ﬁnding subtle, nonlinear
paFerns in data, that can be used to:
– PREDICT outcomes and guide acEons, e.g.:
•  Provide targeted recommendaEons to customers
•  Signal the need to service before equipment failure

– DISCOVER insights to inform decisions, e.g.:
•  Which variables among a set of thousands have the most weight in
determining an important outcome?

“Advanced
analy7cs”

Machine
Learning
empowers
human
experts
with

addi7onal
insights
that
were
not
available
before

•  It
is
not
Human
vs.
Machine,
but
Human
and

Machine
together,
best
of
both
worlds

CONFIDENTIAL

Net Promoter Score (tradiEonal approach)
Net Promoter Score (NPS) is deﬁned as
% Promoters -‐ % Detractors
where Promoter = 9-‐10, Detractor = 0-‐6 on a scale of 0-‐10 in answer to the
quesEon "How likely is it that you would recommend our company/product/
service to a friend or colleague?”
Thus, NPS ranges from -‐100 to 100.
How good a score is depends on what your compeEtors’ scores are

CONFIDENTIAL

Using ML to improve Net Promoter score
Skytree can improve your
Net Promoter Score"
Given a set of exisEng customer NPSs,
Skytree can tell you which variables
(gathered from other data in the
organizaEon) are signiﬁcant in
producing the NPS score
Skytree can tell you WHY, thus
informing acEons to improve the NPS
score and hence customer loyalty
Instead of using NPS, Skytree could predict
customer loyalty directly, without the
approximaEons required by NPS
Whereas NPS puts all customers in just 3
categories (favorable, neural, not favorable),
Skytree enables targeEng of each customer
individually, giving more accurate and
focused personalized markeEng
Skytree can improve customer
loyalty directly"

CONFIDENTIAL

Data ML can use
28

Customer
Demographic
Data

-‐
Primary
household
member’s
age

-‐
Gender
and
marital
status

-‐
Number
of
adults

-‐
Primary
household
member’s
occupa7on

-‐
Household
es7mated
income
and
wealth
ranking

-‐
Number
of
children
and
children’s
age

-‐
Number
of
vehicles
and
vehicle
value

-‐
Credit
card

-‐
Frequent
traveler

-‐
Responder
to
mail
orders

-‐
Dwelling
and
length
of
residence

Customer
Internal
Data:
Informa7on

-‐
Market
channel

-‐
Plan
type

-‐
Bill
agency

-‐
Customer
segmenta7on
code

-‐
Ownership
of
the
company’s
other
products

-‐
Dispute

-‐
Late
fee
charge

-‐
Discount

-‐
Promo7on/save
promo7on

-‐
Addi7onal
lines

-‐
Toll
free
services

-‐
Rewards
redemp7on

-‐
Billing
dispute

Customer
Internal
Data:
Usage

-‐
Weekly
average
call
counts

-‐
Percentage
change
of
minutes

-‐
Share
of
domes7c/interna7onal
revenue

Customer
Contact
Records

-‐
Customer
calls
to
service
centers

-‐
Company’s
mail
contacts
to
customers

-‐
Customer
contact
category:
customer
general

inquiry,
customer
requests
to
change
service,

customer
inquiry
about
cancel

Cancel
Reason
Codes

-‐
Unacceptable
call
quality

-‐
More
favorable
compe7tor’s
pricing
plan

-‐
Misinforma7on
given
by
sales

-‐
Customer
expecta7on
not
met

-‐
Billing
problem,

-‐
Moving

-‐
Change
in
business

A
typical
Telco
set
of
variables
might
include:

CONFIDENTIAL

PredicEng Customer Churn
Cost
of
churn:
lost
revenue
+
marke7ng

costs
to
replace
depar7ng
customers

Goal:
predict
customers
at
high
risk
of

churning
while
there
is
s0ll
0me
to
do

something
about
it.

Model
inputs
/
features:

•  Customer
micro-‐segments

•  Customer
behavior

•  Customer
characteris7cs

•  Customer-‐company
interac7on

•  Micro-‐segment
migra7on

•  Note:
much
of
this
requires
fusing

disparate
unstructured
data
sources

Machine
Learning
can
help:

•  Predict
customers
at
high
risk
of
churn

months
in
advance
of
actual
or
passive
churn

•  Customer
micro-‐segmenta0on
–

iden7ﬁca7on
of
customer
segments
through

unsupervised
learning.

Model
outputs
/
interpretability:

•  Iden7ty
of
high-‐risk
churners:
scoring
churn-‐
risk
of
each
customer

•  Rela7ve
importance
of
ML
features:

•  where
are
customers
experiencing
issues

with
products
or
services?

•  Iden7ﬁca7on
of
poten7al
improvements

to
products
or
services
with
highest

impact
on
revenues.

CONFIDENTIAL

PrevenEng Customer Churn: PredicEng Impact of MarkeEng AcEons
Maximize
revenue
by
iden7fying

marke7ng
ac7ons
with
highest
probability

of
posi7ve
outcome

•  Tailor
marke7ng
ac7on
to
specific
high-‐
risk
customers

•  Minimize
offers
to
happy
customers.

Poten7al
Model
inputs:

•  Previous
customer
offers
and
the

outcome
of
those
offers

•  Customer
micro-‐segments
and

migra7on
over
7me
of
customers

through/between
micro-‐segments

•  Customer-‐specific
features,
including

company-‐customer
interac7ons

Machine
Learning
Tasks:

•  Rank
and
score
poten7al
marke7ng
ac7ons
on
a

per-‐customer
basis

•  Iden7fy
micro-‐segments
as
basis
for
targe7ng

marke7ng
ac7ons

•  Predict
customer
life7me
value

Examples
of
Model
Outputs
/
Interpretability:

•  List
of
scored
marke7ng
op7ons,
specific
to
each

customer

•  Iden7fica7on
of
marke7ng
ac7ons
having

greatest
reten7on
impact.

•  Reducing
marke7ng
expense
to
retain
happy

customers.

•  Es7ma7on
of
impact
on
customer
life7me
value

of
possible
marke7ng
ac7ons.

CONFIDENTIAL

Other ML OpportuniEes in Telecom
OperaEonal:
•  Prevent SDN aFacks and related fraud
•  Predict most VULNERABLE POINTS in networks
•  Predict device/ component FAILURE
•  Detect ANOMALOUS behavior, trigger alerts
•  AutomaEc PROVISIONING

CONFIDENTIAL

Typical Data Science Workﬂow: Disparate Tools, Manual Processes
Data Prep:
Transform and fuse
data sets using various
tools
Method SelecEon:
Manually pick and try mulEple

Test:
ConEnually verify accuracy
Deployment:
Export model for producEon
Real-‐Eme Scoring
Results
New
Data`
Parameter SelecEon:
Iterate on diﬀerent
parameters for best results
Pull holdout
data for test
Feature ExtracEon:
Use subset of data due
to performance issues

CONFIDENTIAL

•  Parallelize without sacriﬁcing accuracy
Built to Scale From the Ground Up for Big Data
•  Massive Hadoop scaling with TrueScaleTM
•  Runs directly on Hadoop
nodes
•  Minimize internode traﬃc
•  Net result: near linear scalability
•  Algorithms deeply opEmized

•  In memory execuEon
P
A
R
A
L
L
L
E
Z
E
I
CPU
CPU
CPU
CPU

In Memory
ExecuEon

Skytree
Fast
Internode
Communica7on

Hadoop
Data
Node
Hadoop
Data
Node
Hadoop
Data
Node
Hadoop
Data
Node
Hadoop
Data
Node
Hadoop
Data
Node
Hadoop
Data
Node
Hadoop
Data
Node
Hadoop
Data
Node
Skytree
Skytree
Skytree
Skytree
Skytree
Skytree
Skytree
Skytree
Skytree
In Memory
ExecuEon

CONFIDENTIAL

Skytree Streamlines and Automates the Data ScienEst Workflow
BeFer PredicEon/
Results
Data Prep:
Broad ML
transformaEons
speed data
extracEon/cleansing
New Data
Single click AutoModel™:
Automated method and
parameter selecEon quickly
derives & verifies best models
Feature ExtracEon:
Use all data you need
for beFer results

Unified
Skytree
Environment

Single Step Train-‐Tune-‐Test
Deployment:
Run on Skytree with streaming
data or export model for
producEon

CONFIDENTIAL

Dataset
Size

(Rows)
Accuracy

(Norm. Gini)
100,000
87.8%

200,000
90.1%

400,000
91.3%

800,000
92.6%

1,600,000
93.4%

3,200,000
94.4%

•  Source Dataset: Pascal Large Scale
Learning Challenge DNA dataset
•  4M-‐row dataset was held out for
tesEng.
•  6 training datasets from 100K
through 3.2M rows, arranged into
200 columns, were used.
•  Tuned StochasEc GBT, trees limited
to 5000
•  No featurizaEon applied.

100,000
200,000
400,000
800,000
1,600,000
3,200,000

86.00%

88.00%

90.00%

92.00%

94.00%

96.00%

Accuracy

(Normalized
Gini)

Dataset
Size
(Rows)

Accuracy
as
a
Func0on
of
Data

Set
Size

Scalability Drives BeFer Accuracy

CONFIDENTIAL

Taming the Complexity of ML via AutomaEon
•  Reduce data scienEsts' Eme by 90 – 95%
•  Reduce 60 hours of data science experiment Eme
into 4 hours
•  Allowing data scienEsts’ to do more strategic tasks
•  Reduce total model experiment Eme by
25 – 75%
•  Compress a 3 month ﬁnal model build into 1 month
•  Deploy models faster
•  Reduce compute Eme by up to 30%
•  Reduce compute Eme from 35 days to 30 days
•  Save compute cost and resource
•  Get equivalent or beFer model results
0
20
40
60
80

With
AutoModel

Grid
Search

Time
to
Build
Final
Model
using
Skytree

Automa7on
vs.
manually
by
skilled
data

scien7st
(in
hours)

0
5
10
15

With
AutoModel

Grid
Search

Total
Time
Elapsed
to
Complete
Experimenta7on

using
Skytree
Automa7on
vs.
manually
by
skilled

data
scien7st
(in
weeks)

CONFIDENTIAL

Explaining the models to extract insights

CONFIDENTIAL

Func0onal

Area

Example
Use
Case
Hortonworks
-‐
Hadoop
SkyTree
–
Machine
Learning

Customer

Experience

Management

360
Degree
Customer

&
Household
View

-‐
Computa7onal
Net

Promoter
Score
&

other
Customers

Metrics

Collec7on
data
across
sources
into
Hadoop

Data
Lake
for
360
degree
view
of
Customer

and
Household:
Yarn
enabled
Hadoop

Architecture
–
Single
set
of
data
cross
the

en7re
cluster
with
mul7ple
access
methods

Inges7on:
Mul7ple
sources
of
unstructured

and
structured
data
include,
CDR,

clickstream,
network
probe
&
log
records,

sensor,
IVR
Voice-‐2-‐Text,
social
media,
OSS/
BSS,
etc

Process
&
Store:
Yarn
enabled
Architecture
–

Single
set
of
data
across
the
en7re
cluster

with
mul7ple
access
methods.

Distributed

storage
in
HDFS
and
many
processed

workloads
managed
by
Yarn

Query
&
Alerts:
Schema
on
read
allows

mul7ple
methods
for
queries
and
alerts

through
different
applica7ons
or
through

HDP
tools
(Hive,
Hbase,
Storm,
etc)

•  Understand
which
variables
are
significant
in

producing
the
NPS
score

•  Understand
the
WHY
for
an
NPS
score,
thus
informing

ac7ons
to
improve
it
and
hence
customer
loyalty

•  Finally,
the
poten7al
to
predict
customer
loyalty

directly,
without
the
approxima7ons
required
by
NPS

•  Skytree
enables
targe7ng
of
each
customer

individually,
giving
more
accurate
and

focused
personalized
marke7ng

Customer
Sen7ment

and
Churn
Detec7on

•  Tailor
marke7ng
ac7on
to
specific
high-‐risk

customers

•  Minimize
offers
to
happy
customers.

•  Rank
and
score
poten7al
marke7ng
ac7ons
on
a
per-‐
customer
basis

•  Iden7fy
micro-‐segments
as
basis
for
targe7ng

marke7ng
ac7ons

•  Predict
customer
life7me
value

CONFIDENTIAL

Bigger Data. Better Insights.™
CONFIDENTIAL

Thanks!
Alexander Gray, PhD
CTO, Skytree

Next Steps…
Download the Hortonworks Sandbox
Learn Hadoop
Build Your Analytic App
Try Hadoop
Learn more with our partnership
http://hortonworks.com/partner/skytree/
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•  Including Pre and Post event community meetups
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