Driving Better Products with Customer Intelligence 

1© Cloudera, Inc. All rights reserved.
Driving Better Products with Customer
Intelligence
August 2, 2017

Your Speakers for Today…
Amy O’Connor
Big Data Evangelist
Clarke Patterson
Head of Product
Marketing
Mike Becker
Sr Manager of Big
Data

Polling Question
How would you want to interact with your phone/internet service provider?
• distinct touch points (one service at a time)
• holistic relationship (a relationship over multiple engagements)

CUSTOMER &
CHANNEL
DATA-DRIVEN
PRODUCTS
SECURITY, RISK &
COMPLIANCE
Data is driving business transformation

Potential Data Sources are Everywhere
Digital/Mobile
Digital Media
• Teradata Aprimo
• IBM Unica
• Oracle Eloqua
• X+1
Web Logs
• Microsoft IIS
• Apache
• nginx
• Google GWS
Clickstream/UX
• Adobe Omniture
• IBM Coremetrics
• IBM Tealeaf
• Google Analytics
Premium
• Webtrends
Mobile Application
SMS
Transaction CRM/Call Center Demographics
Loyalty/Retentio
n Social
Retail
Mobile
Web
Channel
Distributor
Bot
Call Center
Indirect
Kiosk
Embedded
Commerce
Service
Billing
Customer Lifecycle
• Acquisition
• Churn
• Cross-Sell
• Upsell
CRM
• MS Dynamics
• Oracle/Siebel
• Salesforce
• SAP
Online Chat
• Oracle RightNow
• Moxie Live Chat
• LivePerson
• Instant Service
• Oracle Live Help
• BoldChat
• Zendesk Zopim
• Kana Live Chat
IVR
• Avaya
• Cisco
• Nortel
• Nuance
Data Broker /
Syndicate
• Acxiom
• CoreLogic
• Datalogix
• eBureau
• ID Analytics
• Intelius
• PeekYou
• Rapleaf
• Recorded Future
• IHS Polk
• Nielsen
• InfoScout
• Symphony IRI
• Gfk
Behavior
Loyalty
• Aimia
• Brierley+Partners
• Comarch
• Epsilon
• Kobie
• ICF Olson 1to1
• Merkle
• Clutch
• CrowdTwist
• DataCandy
• Deluxe
• Inte Q
• ICLP
Survey
• ABA
• Medallia
• Forsee
• Allegiance
• Walker Information
Direct
• Twitter
• Facebook
• Bazaarvoice
Listening/Manageme
nt
• Sprinklr
• Crimson Hexagon
• Radian6
• Lithium
• Simply Measured
• Curalate
• Datasift
Voice of the
Community
• CSAT
• NPS

The Data Journey
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

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 Source
Common
Identifier
Current 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
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

Cloudera Enterprise – The Platform for Customer 360
Location
Social
Clickstream
BI Tools
Online & Mobile Apps
Billing/
Orderin
g
CRM/ Profile
Marketing
Campaigns
Search
EDW
N/W
Logs
Call Center
Apps
Networ
k
Other
Structured
Sources
Internal Systems External Sources
BI Solutions Real-Time AppsSearch Data Science
Workbench
SQL
Machine
Learning
Systems Data
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

Managing dataflows can be a
daunting task
Build
Development
processes are far more
complex and drawn out
than they need to be
Execute
The economics of data
have changed, giving
way to a choice of
executing and
deployment options
Evolve
Architectures are
constantly changing
and have more
stringent SLA’s

Build
Not all
developers are
created equally
>_
Integrations are
abundant and
unnecessarily rigid
Build-to-deploy takes
far longer than
necessary

Execute
Multiple deployment
options exist yet
constraints limit making
use of them
Mixed workloads are the
norm, must handle both
batch and streaming
11001001001001101001
00101010010010010010
10100100100101010101
01001001001010100100
11010001110010100100
10010010100101110101
Scalability is a must, both
today and into the future

Operate
Increasingly, the business
expects SLA’s on the
quality and timeliness of
data
Architectures are
constantly evolving, with
new versions or new
projects regularly being
added
Data, and it’s structure,
will inevitably change,
causing wide spread
impact

StreamSets Data Operations
Platform
EFFICIENCY
Intent Driven Flows
Batch & Streaming Ingest
In-stream Sanitization
MASTER
Availability & Accuracy
Proactive Remediation
MEASURE
Any Path
Any Time
MAP
Dataflow Lineage
Live Data Architecture
CONTROL
Drift Handling
Stage & Flow Metrics
Lineage & Impact Analysis
AGILITY
Flexible deployment
Exception Handling
Seamless Evolution
EVOLVE (Proactive)
REMEDIATE (Reactive)
DEVELOP OPERATE
CloudClusterStandalone
StreamSets Data Collector Dataflow Performance Manager
Edge

Polling Question
What are your current challenges when building and maintaining data pipelines?
1. We have too many pipelines to manage.
2. Data sources (data or app version) are constantly changing, requiring
frequent updates to pipelines
3. We don't have an effective way to blend batch and streaming workloads
4. It's difficult to gain visibility into the performance of our dataflow pipelines

15 | © 2016 RingCentral, Inc. All rights reserved.
Driving Better Products
with Customer
Intelligence

Worldwide Leader in Cloud Communications & Collaboration
12+
Years of Innovation
18%
Revenue committee
to R&D
2,200+
Team Members
(67% Customer-Facing)
80+
Countries Global
Coverage
350K+
Business Customers Magic Quadrant
Worldwide Leader
12+
Years of Innovation
18%
Revenue committed
to R&D
2,200+
Team Members
(67% Customer-Facing)
80+
Countries Global
Coverage
350K+
Business Customers Magic Quadrant
Worldwide Leader

Big Data Org
 Big Data Applications and Infrastructure
 Four Teams:
• Big Data Development
• Data Operations
• Reporting
• Advanced Analytics (Data Science)

Before Cloudera
 Oracle Data Warehouse
• Continue to use Data Warehouse extensively
• Became expensive as our data Volume grew
• New data sources wouldn’t work easily with Unstructured Data (Variety)
 Began using Cloudera in 2013

RingCentral Analytics Use Cases Using Hadoop
 Product Usage Analytics
 Marketing Analytics
 Quality of Service (QOS)
 Carrier Cost Optimizations
 Fraud Analytics
 Universal Service Fund – Taxes

Product Usage Analytics

Background
21
 All Departments need Product Usage Data
 There are Multiple Data Pipelines that deliver data to multiple
separate reports
 No consistent way of retrieving all product usage for a given
account
 Requires many man hours to create a report for customers or
segments

Department Use Cases
 Product
• General Usage for Capacity Planning
• Feature Usage
 Product Marketing
• Adoption Metrics
 Finance
• Financial Analysis
• Billing
 Sales
• Sales Reps for Customer Discussions
• Churn Prediction
 GCC
• Customer Support

Approach
Create a single data base which tracks all product usage and ties the
information to a single user
• Track daily activity per user per day
• Provides ability to roll up product usage for any product by account and segment
• Gives RC ability to quickly determine usage by many different dimensions
• Multiple Metrics per product:
• Glip
• Telephone
• API Platform
• Contact Center
• Ring Central Conferening
• RC Meetings
• Others
23

Example
Glip VOIP SMS RC Integrations
Date Company User Active Posts Attachments
Minutes of
use Total Calls Incoming Outgoing RingOut Incoming Outgoing SalesForce Google
6/1/17 ACME John Doe Yes 16 2 45 10 4 6 0 2 0 0 3

Example
Glip VOIP SMS RC Integrations
Date Company User Active Posts Attachments
Minutes of
use Total Calls Incoming Outgoing RingOut Incoming Outgoing SalesForce Google
6/1/17 ACME John Doe Yes 16 2 45 10 4 6 0 2 0 0 3
GLIP Usage Only

Advantages
 Single Place to retrieve Product Usage
 Can Roll up information based on Use Case
 Control what information is available to user based on Need to
Know
 Simplifies consolidation of usage for various use cases

Marketing Analytics

What is 4D?
• The Digital Data Discovery Digest (4D) provides a Single Source of Truth (SSOT) and a 360 degree view
of our prospects & customers enabling interdepartmental alignment on source data for KPIs.
• Additionally 360 degree data and predictive modeling enable more targeted Marketing spend.
• 4D links all digital behavior (for converted leads – i.e. users that fill out a web form) to in funnel data (i.e.
Marketing Automation, CRM, product usage data, etc.)
• 4D enables associations of all people data to a company leveraging RingCentral’s Total Addressable
Market (TAM) DB.
• 4D provides complete flexibility and is vendor agnostic in its design enabling future customization and
changes in definition, classifications, etc. with minimal effort.
RingCentral 4D Project Overview

Core Design Objectives: Data is aggregated in a data pool where it can be standardized, unified, enriched
and transformed providing a common data set following MDM best practices. All departments can then
utilize summary tables and data cubes for reporting while maintaining alignment on the core data used for
all measurements.
4D Reporting Architecture

Closed Loop Feedback: Data is fed back into the digital tech stack enabling improved digital segmentation
and omnichannel tactical communication improvements.
For example: Automagically recommend the next best action for prospects based upon their digital behavior
and engagement behavior in various channels and/or with various RingCentral departments.
4D Production Value

QOS

QOS Analytics
 Measure Quality of Our Calls
 Need to Measure Quality across Multiple Systems
• SONUS – Carrier Gateway
• ACME – Internet Gateway
• RTCPXR – IOT from VOIP Phone

SONUS/ACME QOS Architecture
SONUSRC
Media
Server
ACMERingCentral
VOIP
Carrier
Core External
Carrier
Network
Customer’s
ISP
Provider
ACME ACME SONUS {}
ACME SONUS SONUS
RTCPXR
{}

QOS Call Record Correlation
Indexed CDR with
Common Key
Loaded Directly
into Hbase and
Vertica with
Multiple CDRs
Correlated QOS
Record with Meta
Data for Call
Generated after 12
Hours written to
HDFS and Vertica

Convert from Indexed Format to Consolidated Record
Date Time Company ID Source RCSessionID SONUS
Carrier
SONUS MOS
Score
ACME
Location
ACME MOS
Score
RTCPXR
Location
RTCPXR
MOS Score
6/1/17 20:15 12345 SONUS qwerty Carrier_ID 4.5
6/1/17 20:15 12345 ACME qwerty New York 4.2
6/1/17 20:15 12345 RTCPXR qwerty New York 4.2
SONUS ACME RTCPXR
Date
Company
ID Time RCSessionID Carrier MOS other Location MOS ISP Location MOS Other
6/1/17 12345 20:15
qwerty US Based
Carrier 4.5 New York 4.2 ACME New York 4.2
Indexed UCDR
Correlated UCDR

Carrier Cost
Optimizations

Carrier Cost Optimization
 SONUS is source system
 Correlate Carrier Legs of Call to Trunk
 Trunk corresponds to Carrier
 Carrier Rates are uploaded and applied to SONUS
 Generate Reports for Carrier Costs

Call Rating Logic

Carrier Cost Optimizations

Rated Calls Rolled up by Carrier Partner

Fraud Analytics

Business Problem Overview
Use Customer Traffic To Determine Abuse
o Fraudulent users send telephone traffic to high cost areas – we have to watch for it
o The quicker this usage is detected and investigated the quicker it can be stopped

Network Terminating Costs by Day
Fraudulent traffic started on Friday and
was stopped on Monday
(Dark Green)

Vendor Costs and Minutes
vendor daily cost dollars
increased dramatically
vendor daily minutes
increased as well

Finance
Federal Universal Service Fund (USF)

USF – source wiki
The Universal Service Fund (USF) is a system of telecommunications
subsidies and fees managed by the United States Federal
Communications Commission (FCC) intended to promote universal
access to telecommunications services in the United States. The FCC
established the fund in 1997 in compliance with the Telecommunications
Act of 1996. The fund reported a total of $7.82 billion in disbursements
in 2014,[1] divided among its four programs. The fund is supported by
charging telecommunications companies a fee which is set quarterly. As
of the third quarter of 2016, the rate is 17.9% of a telecom
company's interstate and international end-user revenues.

USF
 Fee is passed onto Customer
 Only Call’s that terminate or originate in the US
• Determine US State of Origin of Call
• Determine US State of Termination of Call
 Categorize calls into:
• Intrastate
• Interstate
• International
 Calculate volume that is International or Interstate as percent of total

USF – Terminate on RC Network

USF – Originate on RC Network

RingCentral Analytics Use Cases Using Hadoop
 Product Usage Analytics
 Marketing Analytics
 Quality of Service (QOS)
 Carrier Cost Optimizations
 Fraud Analytics
 Universal Service Fund – Taxes

Q&A

Q&A Session
Amy O’Connor
Big Data Evangelist
Clarke Patterson
Head of Product
Marketing
Mike Becker
Sr Manager of Big
Data

Thank you

Driving Better Products with Customer Intelligence

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