Using Big Data to Drive Customer 360

Using Big Data to Drive a True
Customer 360
Capture Value from Big Data with an Enterprise Data Hub
Vijay Raja - Solutions Marketing Manager
Amy O’Connor - Big Data Evangelist

2© Cloudera, Inc. All rights reserved.
Agenda
• Customer 360 - An Industry Perspective
• Key Challenges in Driving a Customer 360
• Customer 360 - Why status quo won’t work
• Driving Customer 360 with Hadoop
• How to Iteratively build a true Customer 360?
• Key Customer 360 use cases
• Customer 360 case studies

Customer 360 – An Industry Perspective
- What is Customer 360?
A holistic real-time view of your
individual customers
Across all products, systems, devices
and interaction channels
In order to deliver a consistent,
personalized, context specific and
relevant experience

Customer 360: Experience Expectations
Personalized
to reflect preferences and
aspirations
Relevant
in the moment to
customer’s needs and
expectations
Consistent
across all channels,
brands, and devices
Contextualized
to present location and
circumstances

Key Challenges in Driving a Customer 360
DATA SILOS DATA VOLUMES
NEW DATA SOURCES COSTS OF DATA PROCESSING
• Multiple Data Silos
• Often store overlapping and
conflicting info
• Issue compounded with
multiple business units
Care
Product Catalog
CRM
Ordering
Billing
Legacy
Enterprise
Inventory
OSS
Network
Customer Care
Product Catalog
Ordering
Billing
CRM
Legacy
Enterprise
Inventory
Supply Chain
PoS
• Data growing at ~100% YoY
• A typical mobile service
provider generates approx. 5
– 30 Billion Call Detail
Records (CDRs) every day
Clickstream Location/ GPS
Call center
Records
Social Media
• Semi/ Un-Structured Data
Sources
• Streaming/ Real-time data
• Critical for building a True 360
view
• Cost prohibitive
• $30,000 and $100,000 (USD)
per TB – Cost of storing data
in relational database
systems per year

Polling Question
What are some of the key challenges you face with respect to your Customer
360 journey?
o Data Silos – Data spread across a number of silos
o Data Volumes / Growth – High rate of data growth
o New/ Unstructured Data Sources
o Cost of Data Storage & Processing
o All of the above

Consumer activity data sits in silos
• Most organizations have a static version
of the customer profile in their data
warehouse
• Mainly structured data
• Only internal data
• Only “important” data
• Only limited history
• Activity data – clickstream data, content
preferences, customer care logs, is kept
in BU silos or not kept at all
Customer 360 view: Why status quo won’t work
AnalystData
Analyst Data
Analyst DataAnalystData
AnalystData

A New Way Forward…

Bridge Silos of Data to Drive A True Customer 360…
Care
CRM
PoS
Ordering
Billing
Billing
Inventory
Supply Chain
Legacy
Enterprise
Structured Data Islands
Clickstreams
Social Media
Machine Data
Sensor Data
Log Files
Other Unstructured Sources
Semi-Structured Data Islands
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Encrypt and KeyTrustee
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
PROCESS, ANALYZE, SERVE
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
Sentry, RecordService
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners
Enterprise Data Hub

EDH based Architecture for Effective Data Mgmt.
Enterprise
Data
Warehouse
Enterprise Data Hub
Data Sources
DataIngest–
StreamingorBatch
Business
Intelligence/
Reporting Tools
Network
Usage
CRM
Inventory
Clickstream Sensors
Machine Logs Social
Billing
Ordering
Structured
Unstructured /Semi-Structured
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners

Customer 360 – Traditional Data Flow Diagram
Location
Social
Clickstream
ETL/Stored
Procedures
Enterprise Data
Warehouse
Segmentation & Churn
Analysis
BI Tools
Marketing Offers
Data Marts /
Aggregations
Billing/
Ordering
CRM/ Profile
Marketing
Campaigns

Customer 360 – Traditional Data Flow Diagram
Location
Social
Clickstream
ETL/Stored
Procedures
Enterprise Data
Warehouse
Segmentation & Churn
Analysis
BI Tools
Marketing Offers
Data Marts /
Aggregations
Billing/
Ordering
CRM/ Profile
Marketing
CampaignsOther/ New Data Sources
– Mobile, Sensors, Apps,
Network Logs, Files
Does not model
easily into traditional
database schema
Limited
Processing
Power
Limited
Processing
Power
Storage scaling very
expensive. Not
designed for ELT
Loss in Fidelity
Manual work. Few
automated system feeds.
Based on sample/
limited data

Customer 360 – Flow with EDH
Location
Social
Clickstream
BI Tools
Online & Mobile Apps
Billing/
Ordering
CRM/ Profile
Marketing
Campaigns
Sqoop or Native
Connector
Flume
Secure Kafka
Cluster
Search
EDW
Sqoop or Native
connector to Impala
SQL via Impala
Solr
HBase
N/W Logs
Call Center
Apps
Network
Other Structured
Sources

Who are you?
Where are you?
What have you purchased?
What content do you
prefer?
Who do you know?
What can you afford?
What is your value to the
business?
How / why have you
contacted us?
A Sample Customer 360° Profile

How to Iteratively Build a True Customer 360?
Customer
Data Source
Start with ingesting the
“best” version of your
customer profile
Find your common
identifiers across
datasets: customer
name, number, IMEI,
IMSI
IMEI
ChannelsPurchase History
Add New Data SourceCommon IdentifierCurrent Source
Enrich with additional
demographic information
(purchase history or channels)
from other systems / sources
Deliver A Use Case
Deliver a specific use case based
on the profile with new data
sets:
• Customer Lifetime value
• Next Best offer
• Omni Channel
Enrich Your Profile
• Enrich your customer
profiles with purchase
behavior
• Continue to enhance
with each new use case
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners

How to Iteratively Build a True Customer 360?
Customer
Data Source
Start with ingesting the
“best” version of your
customer profile
Find your common
identifiers across
datasets: customer
name, number, IMEI,
IMSI
IMEI
ChannelsPurchase History
Add New Data SourceCommon IdentifierCurrent Source
Enrich with additional
demographic information
(purchase history or channels)
from other systems / sources
Deliver A Use Case
Deliver a specific use case based
on the profile with new data
sets:
• Customer Lifetime value
• Next Best offer
• Omni Channel
Enrich Your Profile
• Enrich your customer
profiles with purchase
behavior
• Continue to enhance
with each new use case
Location Clickstream
Continue to add new data sources iteratively to
enhance your customer profile with new use cases
Call center
Social Media Apps
External
Data
New Data Sources
OPERATIONS
Cloudera Manager
Cloudera Director
DATA
MANAGEMENT
Cloudera Navigator
Optimizer
BATCH
Sqoop
REAL-TIME
Kafka, Flume
UNIFIED SERVICES
RESOURCE MANAGEMENT
YARN
SECURITY
FILESYSTEM
HDFS
RELATIONAL
Kudu
NoSQL
HBase
STORE
INTEGRATE
BATCH
Spark, Hive, Pig
MapReduce
STREAM
Spark
SQL
Impala
SEARCH
Solr
SDK
Partners

From Static to Dynamic, Real-Time Micro-Segmentation…
Traditional Segmentation
• Age
• Gender
• Average Spend
• Price Plans
• Usage history
• Data, Voice, Text
• Billing history
• Device Upgrade
• Age
• Gender
• Average Spend
• Price Plans
• Usage history
• Data, Voice, Text
• Billing history
• Device Upgrade
• Location
• Social Influence
• Applications Used
• Content Preferences
• Usage Details
• Roaming Analysis
• Travel Patterns
• Device History
• Other products/ services
• Bundling preferences
• Offer history
• Campaign Adoption
History
• Call center Tickets
• QoS History
• Household Analysis
• Lifetime Value
• Churn Score
• Clickstream Info
• Channel Preferences
• Survey
Real-Time Micro-
Segmentation

Customer 360° - Key Use Cases

Customer 360 – Key Use Cases
Churn Prevention & Customer
Retention
Targeted Marketing &
Personalization
Proactive Care
• Churn Modeling & Prediction
• Rotational/ Social Churn
• Customer Lifetime Value
• Sentiment Analytics
• Price Elasticity Modeling
• Customer micro-segmentation
• Next Best Offer
• Campaign Analytics
• Geo-Location Analytics
• Recommendation Models
• Proactive Care Dashboard
• Customer Lifetime Value
• Subscriber Analytics
• QoS Analytics
• Real-Time Alerts

Targeted Marketing & Personalization
Collate the Data Sources Micro-Segmentation Drive Personalized Campaigns
Devise Micro- segments based on
combining multiple factors:
• Age
• Location
• Spending History
• Channel Preferences
• Content Preferences
• Apps Usage
• Social Influence
• Churn Score
• Lifetime Value
• Usage Patterns
• Data Usage
Drive Personalized Campaigns for specific
micro-segments
Retention campaign for high value
customers with iPhone who recently
shared a negative social sentiment
Upsell campaign for high-data users
with family to move over to a family
bundle
Geo-Location based targeted
advertising for specific customer micro-
segments
1

Churn Prediction & Analytics
Customer Data
Account Activity
Social Media
Contact
Status
Voice
Text
Data
Tweets
Handles
VisualizationVisualization
Data Wrangling
Usage/ Activity
Analysis
Sentiment
Analysis
QoS Issues/
Customer Care
Tickets
Targeted Retention
Campaigns
Churn Prediction &
Modeling
Big Data enabled Churn prediction models enable Telcos
to identify “at-risk” customers and proactively target
them with retention programs
Churn Score
2

Proactive Customer Care
Proactive Care
Dashboard
Customer Service Team
Big Data enabled Churn prediction models enable Telcos to
identify “at-risk” customers and by proactively target them with
retention programs
When a High Value Customer’s Quality of Service (QoS) or Experience Index falls below a certain threshold,
an alert is sent to the Network and Customer Care team for appropriate resolution
Enterprise Data Hub
Security and Administration
Unlimited Storage
Process Discover Model Serve
Customer
Lifetime Value
QoS Analytics
Customer
Experience Index
EXPERIENCE INDEX for HIGH
VALUE CUSTOMERS
ALERTS
Network Ops Team
3

360° View To Optimize Customer Journey
• Billions of events generated every day
• Needed to bring together multi-
structured data from new sources and
multiple channels
• Optimize customer journey
• Centralized, real-time 360 degree
customer view that spans many devices
and data sources
• Improved Data warehouse performance
– Life extended up to 3X times
CUSTOMER 360
TELECOMMUNICATIONS
» CUSTOMER 360°
» DATA INTEGRATION
» BETTER CUSTOMER SERVICE
» JOURNEY ANALYTICS

360° View of Retail Customers / Behavior
• Many different data sources integrated
(click streams, in-store POS, online
ordering, and social media)
• Understanding of abandoned online
shopping cart behavior
• Optimized operational investments by
attributing revenue to the appropriate
channel
• Increased customer insight informs
supply chain plans
• Improved ability to explain and predict
returns
CUSTOMER 360
RETAIL / ONLINE
» CUSTOMER 360°
» PROCESS IMPROVEMENT
» BETTER CUSTOMER SERVICE
» PREDICTIVE ANALYTICS

Delivering an Omni-Channel Customer
Experience
Challenge:
• Gain 360⁰ view of data across mobile,
web & retail channels
• 29 tx/ min- high volumes of user,
clickstream & mobile data
Solution & Impact:
• Single, integrated omni-channel Customer
view across web, mobile or retail
• “Send Again” capability for increased
customer conversions.
CUSTOMER 360
FINANCIAL SERVICES
» CUSTOMER 360
» OMNI-CHANNEL
» PERSONALIZATION

Improved segmentation & targeted
marketing by leveraging new data sources
Challenge:
• Changing customer spending patterns
and demographics
• Increasing data from semi/un-structured
data sources
Solution & Impact:
• Cloudera – Intel Solution: Enabled fine-
grained segmentation to improve
marketing results
• Increased web-sales conversion
CUSTOMER 360
ENTERTAINMENT
» CUSTOMER 360
» IMPROVED SEGMENTATION
» IMPROVED SECURITY

Learn More about our Industry Solutions
http://www.cloudera.com/solutions

Thank you
vijay.raja@cloudera.com
aoconnor@cloudera.com

Customer 360: Reference Architecture
3rd party
alerting
Reports,
dashboards, apps
Near Real Time
Event Processing
Flume
Spark
Storage
Solr
Batch Event
Processing
Impala
Spark
Interactivity
Impala
Solr
Ingest
Sqoop
Flume
HDFS
HDFS API
HBase
HBase

Using Big Data to Drive Customer 360

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