Enhancing a customer experience has become essential for communication service providers to effectively manage customer churn and build a strong, long lasting relationship with their customers. This has become increasingly challenging as customer interactions occur across multiple channels. Understanding customer behavior and how it applies across channels is the key to ensuring the best level of experience is achieved by each customer. In this webinar Hortonworks and Apigee discuss how service providers can capture and visualize customer behavior across customer interaction points like call center events (IVR and chat) and combine it with network data, to predict customer calls and patterns of digital channel abandonment using Hadoop and predictive analysis and visualization tools.. We will identify ways to develop a 360 degree view across a customer’s household through an HDP Data Lake and visualize customer interaction patterns and predict expected behavior using Apigee Insights to identify and initiate the Next-Best-Action for a customer to ensure a superior level of customer experience.

1. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Predicting Customer Experience through
Hadoop and Customer Behavior Graphs
with Hortonworks and Apigee
We do Hadoop.

2. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Your speakers…
Sanjay Kumar
General Manager, Telecom
Hortonworks
Sanjeev Srivastav
VP Data Strategy
Apigee

3. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Enhancing Customer Experience with Hadoop
We Do Hadoop
Sanjay Kumar
General Manager, Telecom
Hortonworks

4. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
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

5. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
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

6. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
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

7. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
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

8. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Modern Data Architecture emerges to unify data & processing
Modern Data Architecture
• Enable applications to have access to
all your enterprise data through an
efficient centralized platform
• Supported with a centralized approach
governance, security and operations
• Versatile to handle any applications
and datasets no matter the size or type
Clickstream
Web
&
Social
Geoloca3on
Sensor
&
Machine
Server
Logs
Unstructured
SOURCES
Existing Systems
ERP
CRM
SCM
ANALYTICS
Data
Marts
Business
Analytics
Visualization
& Dashboards
ANALYTICS
Applications
Business
Analytics
Visualization
& Dashboards
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
HDFS
(Hadoop Distributed File System)
YARN: Data Operating System
Interactive Real-TimeBatch Partner ISVBatch Batch
MPP
EDW

9. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
HDP delivers a completely open data platform
Hortonworks Data Platform 2.3
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

10. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Only HDP delivers a Centralized Architecture
HDP is uniquely built around YARN serving as a data operating system that provides multi-tenant Resource
Management, consistent Governance & Security and efficient Operations services across Hadoop applications.
Hortonworks Data Platform
YARN
Data Operating System
• A centralized architecture of
consistent enterprise services for
resource management, security,
operations, and governance.
• The versatility to support multiple
applications and diverse workloads
from batch to interactive to real-time,
open source and commercial.
Key Benefits
• Multiple applications on a shared data set
with consistent levels of service: a
multitenant data platform.
• Provides a shared platform to enable new
analytic applications.
• Delivers maximum cost efficiency for
cluster resource management. Fewer
servers fewer nodes.
Storage
YARN: Data Operating System
Governance Security
Operations
Resource Management
Existing
Applications
New
Analytics
Partner
Applications
Data Access: Batch, Interactive & Real-time

11. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
HDP: Any Data, Any Application, Anywhere
Any Application
• Deep integration with ecosystem
partners to extend existing
investments and skills
• Broadest set of applications through
the stable of YARN-Ready applications
Any Data
Deploy applications fueled by clickstream, sensor,
social, mobile, geo-location, server log, and other
new paradigm datasets with existing legacy
datasets.
Anywhere
Implement HDP naturally across the
complete range of deployment options
Clickstream
Web
&
Social
Geoloca3on
Internet
of
Things
Server
Logs
Files,
emails
ERP
CRM
SCM
hybrid
commodity appliance cloud
Over 70 Hortonworks Certified YARN Apps

12. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
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

13. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
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

14. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Hadoop Driver: Enabling the data lakeSCALE
SCOPE
Data Lake Definition
• Centralized Architecture
Multiple applications on a shared data set
with consistent levels of service
• Any App, Any Data
Multiple applications accessing all data
affording new insights and opportunities.
• Unlocks ‘Systems of Insight’
Advanced algorithms and applications
used to derive new value and optimize
existing value.
Drivers:
1. Cost Optimization
2. Advanced Analytic Apps
Goal:
• Centralized Architecture
• Data-driven Business
DATA LAKE
Journey to the Data Lake with Hadoop
Systems of Insight

15. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Journey to Enhanced Customer Experience
Real-time
Event Action
based on
Customer
Context &
Location
Collection of
Data for
Customer
Insights
(Data Science)
Actionable
Insights
through
Customer
Models (BI)
Current State
of the
Customer
(Dynamic
Customer
Profile - NPS)

16. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
HDFS
Raw
Event
Storage
Customer Experience Journey – Data Lake for Collection
1
°
°
°
°
°
°
°
HBase
Processed
Event
Storage
°
°
°
°
°
°
°
°
N
°
Mul3tenant
Processing:
YARN
(Hadoop
Opera.ng
System)
Metadata
Management
HCatalog
Hive
/
Tez
(Interac.ve
Query)
ISV
(YARN
Apps,
i.e.
HPA
/
LASR)
Slider
(Always-­‐on
Services)
Message
Queues
Log
Files
Web
Services
JMS
Update
Data
Lake
Network
Probe
Events
ODBC /
JDBC
Rest API
Native API
External
Customer
Data
References

17. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
HDFS
Raw
Event
Storage
1
°
°
°
°
°
°
°
HBase
Processed
Event
Storage
°
°
°
°
°
°
°
°
N
°
Mul3tenant
Processing:
YARN
(Hadoop
Opera.ng
System)
Metadata
Management
HCatalog
Hive
/
Tez
(Interac.ve
Query)
ISV
(YARN
Apps,
i.e.
HPA
/
LASR)
Slider
(Always-­‐on
Services)
Message
Queues
Log
Files
Web
Services
JMS
Update
Data
Lake
Network
Probe
Events
ODBC /
JDBC
Rest API
Native API
External
Customer
Data
References
Customer Experience Journey – Actionable Insights

18. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
HDFS
Raw
Event
Storage
1
°
°
°
°
°
°
°
HBase
Processed
Event
Storage
°
°
°
°
°
°
°
°
N
°
Mul3tenant
Processing:
YARN
(Hadoop
Opera.ng
System)
Metadata
Management
HCatalog
Hive
/
Tez
(Interac.ve
Query)
ISV
(YARN
Apps,
i.e.
HPA
/
LASR)
Slider
(Always-­‐on
Services)
Message
Queues
Log
Files
Web
Services
JMS
Update
Data
Lake
Network
Probe
Events
ODBC /
JDBC
Rest API
Native API
External
Customer
Data
References
State of the Customer
- Net Promoter Score
- Appetite for Info score
- Target Advertising Profile
- # Dropped calls today/month
- Sentiment / Churn Score
HBase
/Accumulo
Real-­‐3me
Serving
°
°
°
°
°
°
°
°
N
In-­‐Line
Memory
(Spark)
Update Customer
Profile and Scores
Customer Experience Journey – Current State of Customer

19. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
HDFS
Raw
Event
Storage
Customer Experience Journey – Real-time Event Interaction
1
°
°
°
°
°
°
°
HBase
Processed
Event
Storage
°
°
°
°
°
°
°
°
N
°
Mul3tenant
Processing:
YARN
(Hadoop
Opera.ng
System)
Metadata
Management
HCatalog
Hive
/
Tez
(Interac.ve
Query)
ISV
(YARN
Apps,
i.e.
HPA
/
LASR)
Slider
(Always-­‐on
Services)
HBase
/Accumulo
Real-­‐3me
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:
KaLa
Update Customer
Profile and Scores
External
Customer
Data
References
State of the Customer
- Net Promoter Score
- Appetite for Info score
- Target Advertising Profile
- # Dropped calls today/month
- Sentiment / Churn Score

20. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Hortonworks for Customer Experience
Functional
Area
Solution
Components /
Use Case
Description Problem Addressed Business Benefits
Customer
Experience
Management
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
- Incorporating customer call center
discussions into customer view
- Complete view into customer
experience across all services
- Reduction in Customer Churn
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 and context at this moment in
time
- Next Best Action based on
customer’s experience with
service provider (Retail /Call
Center)
- Greater targeted marketing/
advertising
Real-time 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 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

21. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Customer Experience Management & Marketing Journey
HDP
Landing
Zone
HDP
DataLake
Real-­‐.me
Streaming
HDP
DataLake
Dynamic
Customer
Profile
360
Customer
Awareness
&
Household
View
Loca3on
Based
CEM
&
Real-­‐3me
customer
response
Next
Best
Ac3on
Customer
Sen3ment
Customer
Aware
Loca3on
Based
Promo3ons
Mul3-­‐channel
Customer
Scoring
Models

22. Telco Customer Care using Hadoop &
Customer Behavior Analytics
Sanjeev Srivastav / ssrivastav@apigee.com

23. Agenda
The changing landscape in customer care
• Interactions across multiple channels
• Key experience management strategies
Data lakes for ongoing analysis and action
Visualize and predict
• Sequences of activity across call center and operations data
• Next-best-actions, churn, etc. based on activity sequences
23

24. 24
TheChangingLandscape!
inCustomerCare!
24
Traditional models of customer
behavior were largely based on
customer profiles.
New focus on behavior models.

25. 25
DataLakes!
25
Investments in storing customer
engagement data across various
interaction channels

26. 26
Visualize&Predict!
26
Granular customer activity across
channels

27. Enterprises see interaction fragments
27
Email Analytics
Mobile Analytics
Web Analytics
Retail Analytics
Social Analytics
Call Center
AnalyticsTime

28. Understand each customer’s journey
28

29. Identify common interactions and influences
29

30. Contextual and individualized actions
30
Customer journey
insights
Individual and
contextual actions
Real
time
?

31. Use case – call center + operations data
Predict the
customers likely
to contact the
call center
Predict the likely
reason of the
next call
Avert the next
call
31

32. Use case – call center + operations data
Predict the
customers likely
to contact the
call center
Use sequence
of events +
customer profile
Predict the likely
reason of the
next call
Illustrate with
individual
customer
experience
Avert the next
call
Act during call
or after call
32

33. Use case – digital channel abandonment
Interaction
sequences ending
in abandonment of
a digital channel
Profile of
customers and
relevant metrics
Interactions after
abandonment
Evaluate
customer
preferences
Likelihood of
abandonment
Influence
engagement
with customers
33

34. Responding to trends: Key technology strategy
Data Lake for cross-channel
experience
Journey analytics
Predictive analytics
Actionability
§ Batch and real-time engagement
§ APIs and interfaces with existing systems
§ Models for recommendations and targeting
§ Next best actions
§ Customer journey visualization
§ Time and sequence query interface
§ Raw data, not summarized or aggregates
§ Time sequences preserved
Users
Apps
Developers
Models + APIs
Data + APIs
Backend
End–to-end System
34

35. Apigee Insights Demo
• Customer interaction across various “channels”
– Web site
– IVR
– Call center agents
– Retail stores
• Visualize
– “Channel surfing”
– Customer satisfaction
• Act
– Segmentation, based on customer experience
35

36. Visualize: repeated calls to a call center
• Calls center data
• Operations data
• Network data
36
IVR change
25
Escalation
226,359 (66,532
dropoff)
Network
9,340
Education
18,588 (6,119 dropoff)
Internal issue
9,121
Equipment issue
9,121
Internal issue
661
(blank)
576
Network
528
No sound
27
No pic
29
Education
107 (35 dropoff)
Equipment issue
765 (243 dropoff)

37. Visualize: Customer profiles with path analysis
37

38. Predict: using customer profiles and events
Event
sequences
before t
(training)
Prediction
at time=t
Events
after t (test)
38
Machine learning
@scale
Ok to have small
number of
historical events
that correlate with
predicted events

39. Apigee Insights platform and tools
Machine learning (ML)
Segment customers
Individualized actions
ML feedback
§ APIs for customer engagement and recording customer responses
§ Engine for hyper-personalization; in-context recommendations
§ Tool for business analysts; data interfaces to business systems
§ Models of behavior (Apigee GRASP + 3rd party models)
§ Flexible modeling time windows
Users
Apps
Developers
Models + APIs
Data + APIs
Backend
End–to-end System
39

40. Next Steps…
Download the Hortonworks Sandbox
Learn Hadoop
Build Your Analytic App
Try Hadoop 2
More about Apigee & Hortonworks
http://hortonworks.com/partner/apigee
Contact us: events@hortonworks.com

41. © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Q&A