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

Page 1 © 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)

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Apa

che

Pig

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HDFS (Hadoop Distributed File System)

GOVERNANCE BATCH, INTERACTIVE & REAL-TIME DATA ACCESS

Apache Falcon

Apa

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asca

ding

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pach

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Bas

e A

pach

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ccum

ulo

Apa

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Sol

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pach

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park

Apa

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Sto

rm

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


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 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

-  Complete view into customer experience across all services

-  Reduction in Customer Churn

-  Increased Loyalty


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

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HBase Processed Event Storage

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Mul0tenant Processing: YARN

(Hadoop O

pera7ng System)

Metadata M

anagement HCatalog

Hive / Tez

(Interac7ve Query)

ISV

(YARN Apps, i.e. HPA / LASR)

Slider

(Always-‐on Services)

HBase /Accumulo Real-‐0me Serving

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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 ° °

° ° °

° °


° ° °

° ° °

° ° N

°

Mul0tenant Processing: YARN

(Hadoop O

pera7ng System)

Metadata M

anagement HCatalog

Hive / Tez

(Interac7ve Query)

ISV

(YARN Apps, i.e. HPA / LASR)

Slider

(Always-‐on Services)


° ° °

° ° °

° ° 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


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 Data Internal Data Big Data

Environment Data Data

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

Skytree can tell you which variables are important in how your customers assigned their net promoter score

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


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


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