The Big Picture webinar series explores how industries define their strategies for understanding their consumers better using data. From issuing better healthcare to smarter product and services recommendations, data is the fueling foundation for success. Cloudera is a modern platform that gives analytic access to users that need to understand their customers across multiple touch points and multiple enterprise systems. Cloudera not only unlocks the promise of true customer 360 but it also leverages advanced capabilities for data science and machine learning. In this webinar, we take a look at how data scientists can leverage Cloudera to identify and predict a common customer loyalty use case in telecommunications. We will explore the data, design our features, and then leverage Apache Spark to help us make some predictions on the accuracy of our finding. All within a secure and collaborative environments utilizing the Cloudera Data Science Workbench.

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Learned behaviors in churn
SeanAnderson, Solutions Marketing
Amy O’Connor, Big Data Evangelist

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The Big Picture
Webinar Series
Exploring trends in
customer intelligence and
machine learning

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Know Your Customer - Solutions
• Marketing Systems
(Salesforce, Omniture,
CRM)
• Clickstream Data
Primary
Data
Source
• Clickstream
• NPS Systems
• Support Call Logs
• Social Feeds
Primary
Data
Source
Understand Your Customer Learn Behaviors Improve Interactions
• Shopping Cart Platforms
• Geolocation
Primary
Data
Source

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Customer Churn Analytics is
proactively and predictably figuring
out when a customer would churn
or has a high probability of
churning.
Customer Churn Analytics

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Fourth Industrial Revolution brings both
unprecedented opportunity and an accelerated
speed of change1
Acquiring new customers can cost five times more
than satisfying and retaining current customers2
1. World Economic Forum, Global Competitiveness Report 2016-2017
2. Alan E. Webber, "B2B Customer Experience Priorities In An Economic Downturn: Key Customer Usability Initiatives In A Soft Economy

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Customers view interactions as a relationship
PROCESS
PRODUCT
Speed
OTT Media
Device…
PROMOTION
Cross-sell offers
Discounts
Contracts…
PACKAGING
Bundles
Household
Data/Media… Order
Installation
Billing…
PEOPLE
Field technicians
Care center
Retail employees…
PLACE/CHANNEL
Mobile / Web
Retail outlet
Care center…
PRICE
Fixed
Usage based
pre/post paid…

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Impact of traditional churn analytics
Multi-channel interactions are
materially worse than single-channel,
whether digital or not
McKinsey2015EUTelecomSurvey

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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
Dynamic Micro-
Segmentation
From static to dynamic, real-time micro-segmentation…

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To meet customer 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

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Batch or High Latency
Segment- or Cohort-Based
Limited Analytic Options
Supports Research
Real-Time and Immediate
Individual User-Based
Operational Analytics
Actionable Insights
Legacy Approach Current Demands
Reports and Summaries Granular and Ad Hoc
Using more data & advanced analytics

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What Happened?
• Reporting on siloed data
• Monitoring and being
reactive
Why did churn happen?
• Simple analysis on 2-3 data
sources yielding limited
segmentation
• Prescriptive response
When will Customer
Churn?
• Use of all data sources
• Predictive and deeper
insights through predictive
modelling, machine learning
• Proactive response
BusinessValue
REACTIVE PROACTIVE
Most organizations employ a
combination of these
Approaches to customer churn
analytics

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BI Solutions Real-Time AppsSearch EDWDiscover Machine Learning
Customer Data Internal Systems External Sources
Only with Cloudera: Analytics for customer churn

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BI Solutions Real-Time AppsSearch EDWDiscover Machine Learning
Customer Data Internal Systems External Sources
Only with Cloudera: Analytics for customer churn
Spark Streaming
Leadership in Spark
Integrated with EDH
Flexible Storage
Store any and all Data.
Kudu - Fast Analytics on
Fast Data
Real-Time Data
Processing
Streaming Ingest
Kafka & Flume - Real-Time
Data Ingest for streaming,
high volume data
Deployment
Flexibility
Data Security
Four pillars of security: Perimeter,
Access, Visibility, and Data
+ Record Service
Centralized Mgmt.
Cloudera Manager for
centralized cluster
management
Manage Multiple Clusters – On
Premise or Cloud environment
- On Premise or Cloud

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Manage Data Growth & Derive new
insights on customers
Challenge:
• 100% Data growth annually, 110 data
sources
• Derive new analytical insights on
customers & network usage
Solution & Impacts:
• Deployed key Customer 360 use
cases – Campaign optimization (DPI),
churn analytics, social churn & lifetime
value analytics
• 1 campaign – InApp purchases -
netted 1% uplift in revenue
TELECOMMUNICATIONS
» DRIVE CUSTOMER INSIGHTS

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TELECOMMUNICATIONS
» CUSTOMER 360°
» DATA INTEGRATION
» BETTER CUSTOMER SERVICE
» JOURNEY ANALYTICS
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

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Customer Churn at Cloudera
Identify key behaviors for churn and sentiment

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Cloudera – Key Enabling Capabilities
Ideal for real-time analytics on
IoT and time series data.
Simplifies Lambda architectures
for running real-time analytics on
streaming data
Leading analytic SQL engine
running natively in Hadoop. Impala
provides the fastest insights, at
high-concurrency, with the familiar
access necessary for powering BI
and analytics across the business.
Kudu: Real-Time Offers Impala: Self Service BI Data Science Workbench
Collaborative hub for enterprise
data science and an integrated
development environment for
running Python, R, & Scala with
support for Spark

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Impala: The Analytic Database for Hadoop

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Introducing Cloudera Data Science Workbench
Self-servicedata science for the enterprise
Accelerates data science from
development to production with:
• Secure self-service environments
for data scientists to work against
Cloudera clusters
• Support for Python, R, and Scala,
plus project dependency isolation
for multiple library versions
• Workflow automation, version
control, collaboration and sharing

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Telco Use Case Demo
Featuring The Data Science Workbench

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Thank you