Big game changers for telco

Big Game Changers for Telco
Disruptive Technologies for Changing the Game

Dr. Arvind Sathi
October 18, 2012

© 2012 IBM Corporation

Overview

•  What is Big Data

•  What is driving Big Data Tsunami

•  Use Cases

•  Advanced Analytics Platform

•  Implementation of Big Data Analytics

2 © 2012 IBM Corporation

Many of us are still struggling with what “Big Data” means……


What is Big data?
•  Volume
•  5 Exabytes every 10 minutes in 2013
•  5 Petabytes of location data every 100 days for
a large CSP
•  30+ Petabytes of user generated data in
Facebook
•  As of 2010, AT&T had 193 trillion CDRs

•  Velocity
•  Mobile data growth compounded 78%,
projected to 10.8 Exabytes per month in 2016
•  Online advertisement bidding process in 80
milliseconds

•  Variety
•  Structured, unstructured text, voice, video,
RFID tags, maps, seismic data, medical events
•  Call center conversations and chat sessions in
many languages

•  Veracity
•  Disgruntled ex-employees, competitors
crowding public data on brands
•  Deceptive data – service companies offering to
“Like” a product

Veracity

If you google “Tether Verizon iPhone to iPad”
The responses have varying level of Veracity
They include sales pitch for Verizon as well as
Process for Jailbreaking iPhone

How do we ingest this information, organize it,
prioritize it, and make it available on customer
touch points,


Overview



•  Use Cases




Today’s customer is more empowered than ever before
Customers
now
The
Internet
and
Everyone
is
an
This
is
changing

have
unlimited
social
networking
inﬂuencer
–

driving
the
en,re
way

access
to
have
created
a
purchase
decisions
service
providers

informa,on
and
more
informed
and
brand
manage
their

can
instantly
share
buyer
percep5ons
commerce

it
with
the
world
regardless
of
processes
using
new

credibility
tools
to
drive

success

>25% 70% 64% 57%

of
the
global
of
customers
use
of
customers
rely
of
standout

popula,on
is
on
Internet
search
as
on
organiza,ons
are

the
internet
their
primary
recommenda,ons
more
likely
to
use

source
when
buying
social
tools

7
informa,on

Resulting in changing relationship with service providers
In
case
of
bad
experiences,
they
exchange
informa6on
with
their

friends/family
and
infrequently
engage
with
the
provider

Mature
Markets
Emerging
Markets

Attempt to re-dial/re-connect 45% 46% 9% 53% 43% 4%

78%
/
87%

Avoid providers friends/family Avoid
Providers
with

21% 57% 22% 31% 56% 13% poor
experience

have poor experience with

73%
/
85%

Tell friends /family about my poor
12% 61% 27% 24% 61% 15% Tell
friends/family

experience

about
their
poor

experience

Contact the customer service 6% 45% 49% 14% 59% 27%

Switch providers – e.g.use
5% 31% 64% 6% 38% 56%
different SIM

My provider contacts me when I
5% 32% 63% 5% 28% 67%
have a poor experience

Always Most of the time/Sometimes Never
Source: 2011 IBM Global Telecom Consumer Survey, Global N= 10177; Mature Countries N=7875


Service Providers can find Social Network leaders

Group with no leader
§  Leaders are 1.2 times more likely to churn compared with non-leaders.
§  There are two types of leaders: disseminating leaders and authority leaders. The former are
closely connected to their group using outgoin calls, while the latter are connected through a
larger proportion of incoming calls.
§  When a disseminating leader churned, additional churns were 28.5 times more likely. When
an authority leader left the group, additional churns were 19.9 times more likely.
§  Typically, there is a very limited time between leaders’ churn and the churn of the followers.

Automation is opening new opportunities for data collection and
analytics

Example: Wall Street Journal
reported pilot programs to use
smart phones to buy and bag
grocery items. Smart phones can
also deliver and apply coupons.

Opportunity for analytics:
•  Opportunity to analyze customer profile and coupon uptake.
•  CSP customer profile can provide additional insights to the grocery store –
internet viewing, mobility, TV viewing, habits, etc. – driving intelligent
campaigns to deliver coupons.
•  Grocery purchase behaviour, jointly with CSP profile can drive Television
Advertising.
Source: Wall Street Journal and IBM Analysis

Monetization of data – emergence of a market place

11 www.lumapartners.com, reprinted with permission © 2012 IBM Corporation

Overview



•  Use Cases



Getting closer to consumers with the Mission Control Center

The room features:
§  Social listening frameworks and
protocols
§  Social listening software
§  Data integration software (“mash-up”)
§  Data visualizations and dashboards

The goal of the project is to
“take the largest sports brand
in the world and turn it into
largest participatory brand in
the world.”

13 Also see http://www.youtube.com/watch?v=InrOvEE2v38 © 2012 IBM Corporation

Product knowledge hub – faster product onboarding and
central repository for product knowledge
Call Center Web Chat

Problem
•  Data is fragmented across CSP intranet, manufacturer
site and third parties
•  None of them provide a complete recipe to a customer
•  Customer needs a step by step process, some of which
is manufacturer dependent and some CSP dependent.
•  A plenty of information is available on third party sites –
e.g., You Tube.
Solution
•  Search and locate all the data associated with tech
support from all possible sources Product Knowledge Hub
•  Normalize and index the data
•  Parse the queries and use context specific search to
locate relevant information
•  Once the problem is understood, direct the customer to a
web page which answers the question, including video
and step-by-step tutorial
Results
Consumer
•  Improved call center efficiencies CSP Data
Feedback
•  Calls can be diverted to web self service
Manufacturer Third Party
•  CSP seen as central repository for product knowledge
Web Site Web Sites
•  Improved product on-boarding


Network Analytics
CSP network node topology mapped onto Google Maps reporting the current video traffic with associated
KPIs (network errors ratio average, alerts for node errors exceeding threshold, etc...)

Traffic audience per channel being
multicasted onto the CSP network
with associated KPIs (Packet
Loss retransmission efficiency
average, MPEG error ratio, etc…)


Network Analytics Channel 1
Channel 2 2 Millions of Set-Top Boxes messages analyzed in real-time
Broadcast TV KPIs to detect video degradation quality causes :
KPIs
- Network node (switch/router)
Encoder
- Set-Top Box firmware/hardware
KPIs
- Channel encoding errors

Cognos dashboard

Network Management
Switches,
CSP routers,…
Network nodes Network
topology Administrator

DSLAM IBM Netezza
Alerts on
defect
detection
Marketing
Home Gateway Statistics

KPIs
Home Network STB STB STB
STB STB STB STB
CRM
Help Desk

Ip=233.136.0.127; MPEG error ratio=0.5; firmware
16 version=V2.1;model=XXX;MAC- IBM InfoSphere Stream
Address=000430123456;LinkChain=Node1-Node12- 10 000 msg/s
Node123-Node1234;Message=Statistic;PacketLoss=54

Are your campaigns location driven…………………


Social Media and CSP data can be aligned, and analyzed to create customer
insight which can be used both for CSP products as well as for third parties.

CSP Products CSP Hosted B2B Business

New Product Dev New Product Dev

Marketing / Sales Marketing / Sales

Customer Service Customer Service

External Customer Insight
Micro Purchase
Sentiments
Social Media Segments Intentions
Network
Behavior Event
Internal Communities
Patterns Triggers Data
Social Media
Location Usage Demographics Interactions

The Vision of Trigger-Based marketing with Location and full customer
features captured and analyzed, allows for a Social CRM
Retailer Fan Page
Retailer Customer Product Catalog
Profile

Telco
Customer Profile
1) Registers with Retailer, gives
Permissions to Retailer and
2) Follows a friend’s Telco
post on FB and clicks
the Like button on a
camera she likes
4) Receives a 3) Intelligent Advisor
message with an platform processes
offer reminding her Lisa’s activity for
6) Lisa to stop by if she’s relevant actions using
uses promo in the area Intelligent Telco and
code to Advisor Platform Retailer information
purchase
offer at POS
5) Receives
promo code for offer
while passing by the
store

Customer Action Telco / Retailer Action

Overview



•  Use Cases



Big Data Analytics Platform to Support Many Use Cases

Industry (1) Deliver smarter (2) Transform Operations (3) Build Smarter
Imperatives services that generate to Achieve Business & Networks
new sources of revenue Service Excellence

Executive
Stakeholders Chief Marketing Chief Operating Chief Network
Officer Officer Officer
•  Real Time CDR •  Real Time CDR Analytics •  Real Time CDR
Analytics and Ingest for and Ingest for Analytics and Ingest for
•  Intelligent Campaigns •  Revenue Leakage •  Network Optimization
Big Data
•  Customer Profile/ Prevention
Business •  Service Quality
Scenarios Location Monetization •  Fraud Detection Analytics
•  Next Best Action
•  Ad Effectiveness
Analysis with Social
Media


Big Data Architecture using a Sports Television analogy.

The commentators converse with the
audience in real-time. They sense what
is happening in the game, prioritize next
Conversation layer best discussion, and keep the audience
engaged.

The directors orchestrate a number of
inputs – cameras, stock photos, replays,
Orchestration layer statistics, special appearances along
with commentators to keep the
production focused on the game.

The editors and the statisticians work in
the background to collate past statistics,
Discovery layer game replays, constantly discovering
interesting facts about the game.


Advanced Analytics Platform

Act /
Web / Cable Identify Assemble Score
Respond
Interactions

Conversation Level
Conversations
Opt-in / Opt-out Obfuscation DMZ

Model Management
Location Identity Integration
Resolution Engine
Command Center
CRM / POS
Orchestration Level

Orders
Unstructured Structured
Discovery Discovery

Bills Discovery Level

Monitoring Customer Comments
Topics that customers are talking about; gleaned from all the CRs, Emails, and Social Media
content. Each layer is a topic, and the word-cluster within it represents the synonyms for the
topic


Big Data view of the Customer

Personal Attributes
• Demographics

Timely Insights
•  Intent to buy

Life Events Social Media-based
360˚
• Life-changing event
Consumer Profiles
Products Interests
• Personal preferences

Relationships
• Personal, business

Monetizable intent to buy Life Events
products •  College: Off to Stanford for my MBA! Bye Chicago!
•  I need a new digital camera for my food pictures, •  Looks like we'll be moving to New Orleans sooner than I thought.
any recommendations around 300?
Intent to buy a house
•  What should I buy?? A mini laptop with Windows
7 OR a Apple MacBook!??! •  I'm thinking about buying a home in Buckingham Estates per a
recommendation. Anyone have advice on that area? #atx
Location announcements #austinrealestate #austin
•  I'm at Starbucks in Times Square

Identity Resolution

scrila34@msn.com Top 200
Customer

Job
Applicant

Criminal
Identity Thief Investigation


Real-time Adaptive Analytics

High Velocity
Sensor Scorer

Predictive Modeler
Analytics Engine
High Volume


Overview



•  Use Cases




Traditional data warehousing
has become too complex for many customers
Nearly 70% of data warehouses experience performance constrained
issues of various types

§  Too complex an infrastructure §  Too inefficient at analytics
§  Too complicated to deploy §  Too many people needed to maintain
§  Too much tuning required §  Too costly to operate

IT shops supporting business operations have to think about how to deliver more
critical analytics for the enterprise with shorter time to value

29
29 10/30/12 © 2012 IBM Corporation

We are observing an evolution
Where the industry has been Where the industry is going

§  Monolithic EDW (data) §  “Smart Consolidation”
§  Data and data mart sprawl §  Consolidate sprawl & reduce cost
§  Lack of enterprise agility §  Analytics delivered via appliances &
specialized systems (API’s)
§  Complex structure, process &
architecture – focused §  Time to value is paramount
§  Governance: limited or lacking §  Centralized data governance program
§  Everyone talking about Analytics §  Analytics integrated to real-time
business operations

30
30 10/30/12 © 2012 IBM Corporation
30 30

How to guide the animal spirit – Big Data Governance

Data can be stolen, manufactured and misused!

Where are the regulations
§  Variations across the world
§  Varying practices and back lashes
§  Location data and your smart phone
§  Driving data and your car
§  Transaction data and your credit card

Is it Big Data or Big Brother
§  Opt-in, conditional Opt-in vs. Opt-out
§  Generational divide
§  Data corruption, vulnerability

Bottom line
§  Data privacy must be addressed to the satisfaction of the consumers
§  Are there ways to adjust for data quality


Elaboration on Security – Business Problem

•  Can Telco data be correlated with social media to get an improved profile of the customer?
•  Can we use the resulting profile for use cases:
•  Acquisition
•  Product Introduction
•  Campaigns / responses
•  Care – assisted / self care
•  Loyalty and churn management
•  How about sharing these profiles with third parties?
•  Could we buy third party data and correlate with CSP information?
•  Under what condition can we interact with the customer and provided added value to
improve product, promotion, price, care or policies
•  How about Analytics in the Cloud? Can we ship CSP CRM data to a third party cloud?

We are observing two extremes, both are bad for business:
•  A conservative view that uses security to shut down any mingling of PII information with
social media
•  A liberal view of personalized communication with no regard to customer privacy
preferences.

Elaboration on Security – Options and related capabilities

Anonymous Personalized

PII data is obfuscated PII data includes opt-in
Data is summarized Different forms of permission seeking / management
Social media is correlated with masked data Insight created on a 1-to-1 basis
Inferences are projected to segments Trust and privacy is personalized and closely managed
Actions are broadcasted to segments

Data masking retains non PII content Rigorous management of privacy management
Identification and categorization of PII data No contamination of anonymous and personalized
Rigorous process for data masking Policies constantly managed and revised based on
customer and regulator feedback

Market experience is showing it is hard to manage information revealed selectively.
See Geoffrey A Fowler, “When the Most Personal Secrets get Outed on Facebook”, Wall Street Journal October 13, 2012.

Maturity Levels and Business Value Analysis
Breakaway – a company who’s generally considered to be best in class in their execution of
key business strategies, thereby able to exhibit the characteristics of an agile,
transformational and optimized organization. This classification excludes “bleeding edge” Breakaway

5

or pioneering aspects, however these may also be evident in such companies. Key
predictive performance indicators are used, modeling for outcomes and information is
utilized enterprise wide for multi-dimensional decision-making.

Differentiating – a company who’s execution of key business strategies through utilization of
information are viewed as generally better than most other companies, creating a degree Diﬀerentiating

4

of sustainable competitive advantage. Management has the ability to adapt to changes to
the business to a degree and measure business performance. Business leaders and
users have visibility to key information and metrics for effective decision-making.

Competitive – a company who’s capabilities generally are in line with the majority of similar
Competitive
3

companies, with growing ability to make decisions on how to create competitive
advantage. It is also the starting point to establish some consistency in key business
metrics across the enterprise.

Foundational – a company who’s capabilities to gather key information generally lag behind
the majority of peers, which could potentially result in a competitive disadvantage. Foundational

2

Information is not consistently available or utilized to make enterprise wide business
decisions. Still have a degree of manual efforts to gather information.
.
Adhoc – a company who’s just starting to develop capability to gather consistent information
in key functional areas, generally falling well behind other companies in the corresponding Adhoc

1

sector. Information beyond basic reporting is not available. Generally have time
consuming, manual efforts to gather information needed for day to day business
decisions.

How is your experience with social media …………………


Information Agenda teams are conducting analytics
workshops world wide across many industries.
Inputs Activities Outputs
Business Analyze current the assessment initiatives
Scope and planned IT Current State
Objectives &
SOA Vision Prioritized Business
Strategies Initiatives
Understand Conduct diagnostic interviewsopportunities
current business challenges /
Existing Business & IT
Understand business goals and SOA vision business
Assess quality of information delivered to the Environment

Assess current / desired Information Maturity level
Analyze key business scenarios Assessment
Existing Data
Collect Data
Environment
Review Information requirements
functional Delivery Capabilities
Analyze non
- Verify
Synthesize
Assess current / plannedGaps
Identify the architecture using
accelerators Develop
Recommendations
Provide Recommendations
Prepare a final report
Business & IT
Current & Document and Present
Information Mgt
Planned
Practices
Services Develop Roadmap and Optimization Plan
Prepare a final report
Recommendations

Information Agenda Accelerators Summary
Details

IA for Education IA Maturity Assessment IOD Reference Architecture


Social Media Maturity Model

Ad hoc Foundational Competitive Differentiating Breakaway
Capability: Marketing has Organizational Customer data Organization Customer
Monitor brand hired a set of accounts to from social engages in sentiment is
sentiment interns to collect media is social media integrated with
monitor social sentiment data collected and conversation to product and
media data on social media analyzed using influence marketing
sites (FB, Yelp, analytical tools customer processes
etc.) sentiment
Measurements
Brand Baseline Collected Measured Influenced to Influenced to
sentiment positive positive
direction direction
Identification of Baseline Low Medium High High
advocates /
ambassadors
Impact on Baseline Baseline Small Medium Large
brand / revenue


Conclusions

Big Data Analytics is bringing unprecedented changes to organizations across industries. The presentation
provided business solutions and provided a technical overview.

Business solutions:
•  Specific solutions – Network Analytics, Campaign Management, Profile Monetization
•  Significant business value by tapping and conquering volume, velocity, variety, and veracity
•  New applications, new business models, new partnerships

Technical solutions:
•  Overall architecture integrates with current DW platform using a three layer architecture – conversation,
orchestration, discovery
•  Significant technological gains in the last couple of years in each of these areas as well as their
integration.

Implementation:
•  Establish a road map based on current and target maturity levels
•  Big Data Governance an important issue to be addressed.
•  Do not leave Data Security behind!


Big Data Analytics – New Book Launching at Information On Demand 2012!
What’s the book about?
This book examines the drivers behind big
data, postulates a set of use cases, identifies
a set of solution components enabled by big
data, synthesizes a solution, and recommends
implementation approaches.
Who is this book for?
Business and IT leaders who are looking for
practical advice on how to drive immediate
business results with analysis of big data
Where can you get a copy?
•  Information On Demand 2012 Book Store (Bayside
Foyer, Mandalay Bay South Convention Center)
•  Book Signing by Author, Dr. Arvind Sathi
Ø Monday Oct 22 – 4:00 p.m.- 5:00 p.m. at conference
Book Store
•  Download e-book version at http://bit.ly/BigDataAnalyticsFlashbook
Join us at Information On Demand 2012 in Las Vegas! Oct 21 – 25, 2012
Registration link - http://www-01.ibm.com/software/data/2012-conference/

Big Data Analytics Book Description
Summary
The Big Data tsunami is already hitting organizations - a set of disruptive technologies to drive game
changers. Business leaders across the globe are seeking answers to the following questions:
• What is Big Data and what are others doing with it?
• How do we build a strategic plan for Big Data Analytics?
• How does Big Data change our analytics architecture?
Unlike many other Big Data Analytics blogs and books that cover the basics and technological underpinnings,
this book brings a practitioner’s view to Big Data Analytics. The author has drawn the material from a large
number of workshops and interviews with business and IT leaders.
About Author Audience Next Steps
Dr. Arvind Sathi is the World Wide Communication •  mid to Sr. mgmt •  Get a complimentary
Sector architect for the Information Agenda team at executives in network copy of the book at
IBM. His primary focus has been in creating visions operations, customer Information On Demand
and roadmaps for Advanced Analytics at leading service, sales, marketing, 2012 Book Store or
IBM clients in telecommunications, media and strategy or IT request the IBM sales
entertainment, and energy and utilities organizations •  IT service & software rep to order one for you
worldwide. He has conducted a number of provider community •  Request a briefing on
workshops on Big Data assessment and roadmap • Industries covered – Big Data Analytics for
development. Financial services, Public key stakeholders from IT
services, healthcare, retail, and Business in your
telecom, energy & utilities, organization
media & entertainment.

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