Big game changers for telco

© 2012 IBM Corporation

Big Game Changers for Telco Disruptive Technologies for Changing the Game Dr. Arvind Sathi October 18, 2012

© 2012 IBM Corporation 2

Overview

•  What is Big Data

•  What is driving Big Data Tsunami

•  Use Cases •  Advanced Analytics Platform

•  Implementation of Big Data Analytics


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






Today’s customer is more empowered than ever before This is changing the en,re way service providers manage their commerce processes using new tools to drive success

Everyone is an influencer – driving purchase decisions and brand percep5ons regardless of credibility

Customers now have unlimited access to informa,on and can instantly share it with the world

The Internet and social networking have created a more informed buyer

>25% of the global popula,on is on the internet

57% of standout organiza,ons are more likely to use social tools

70% of customers use Internet search as their primary source informa,on

64% of customers rely on recommenda,ons when buying


8

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

73% / 85% Tell friends/family about their poor experience

78% / 87% Avoid Providers with poor experience

Mature Markets Emerging Markets

45%

21%

6%

5%

46%

57%

61%

45%

31%

32%

9%

22%

27%

49%

64%

63%5%

12%

Attempt to re-dial/re-connect

Avoid providers friends/familyhave poor experience with

Tell friends /family about my poorexperience

Contact the customer service

Switch providers – e.g.usedifferent SIM

My provider contacts me when Ihave a poor experience

Always Most of the time/Sometimes Never

53%

31%

14%

6%

43%

56%

61%

59%

38%

28%

4%

13%

15%

27%

56%

67%

24%

5%

Source: 2011 IBM Global Telecom Consumer Survey, Global N= 10177; Mature Countries N=7875


9

Service Providers can find Social Network leaders

§  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.

Group with no leader


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


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Monetization of data – emergence of a market place

www.lumapartners.com, reprinted with permission


Overview






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.”

Also see http://www.youtube.com/watch?v=InrOvEE2v38


Product knowledge hub – faster product onboarding and central repository for product knowledge

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

•  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 •  Improved call center efficiencies •  Calls can be diverted to web self service •  CSP seen as central repository for product knowledge •  Improved product on-boarding

Call Center Web Chat

Product Knowledge Hub

Manufacturer Web Site

Third Party Web Sites

Consumer Feedback CSP Data


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…)


STB Home Network

Home Gateway

STB STB

STB STB

Encoder

Broadcast TV

CSP Network nodes

topology

STB STB

STB

Ip=233.136.0.127; MPEG error ratio=0.5; firmware version=V2.1;model=XXX;MAC-Address=000430123456;LinkChain=Node1-Node12-Node123-Node1234;Message=Statistic;PacketLoss=54

DSLAM

Switches, routers,…

Netw

ork Managem

ent

Cognos dashboard

KPIs

KPIs KPIs

Channel 1 Channel 2

KPIs

Alerts on defect

detection

Statistics

Network Administrator

Help Desk CRM

Marketing

IBM InfoSphere Stream 10 000 msg/s

2 Millions of Set-Top Boxes messages analyzed in real-time to detect video degradation quality causes :

- Network node (switch/router)

- Set-Top Box firmware/hardware

- Channel encoding errors

IBM Netezza

Network Analytics


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.

New Product Dev

Location

Communities Behavior Patterns

Event Triggers

Micro Segments Sentiments Purchase

Intentions

Usage Demographics Interactions

External Social Media

Network Data Internal

Social Media

Customer Insight

Marketing / Sales

Customer Service

New Product Dev

Marketing / Sales

Customer Service

CSP Products CSP Hosted B2B Business


The Vision of Trigger-Based marketing with Location and full customer features captured and analyzed, allows for a Social CRM

Customer Action Telco / Retailer Action

3) Intelligent Advisor platform processes Lisa’s activity for relevant actions using Telco and Retailer information

4) Receives a message with an offer reminding her to stop by if she’s in the area

6) Lisa uses promo code to purchase offer at POS

1) Registers with Retailer, gives Permissions to Retailer and Telco

Retailer Fan Page Retailer Customer

Profile

Intelligent Advisor Platform

5) Receives promo code for offer while passing by the store

Telco Customer Profile

Product Catalog

2) Follows a friend’s post on FB and clicks the Like button on a camera she likes


Overview



•  Use Cases

•  Advanced Analytics Platform



Big Data Analytics Platform to Support Many Use Cases

Big Data Business Scenarios

Industry Imperatives

(1) Deliver smarter services that generate new sources of revenue

• Real Time CDR Analytics and Ingest for

•  Intelligent Campaigns

•  Customer Profile/ Location Monetization

•  Next Best Action

• Ad Effectiveness Analysis with Social Media

(2) Transform Operations to Achieve Business & Service Excellence


•  Revenue Leakage Prevention

•  Fraud Detection

(3) Build Smarter Networks


•  Network Optimization

•  Service Quality Analytics

Executive Stakeholders Chief Marketing

Officer Chief Operating Officer

Chief Network Officer


Big Data Architecture using a Sports Television analogy.

Conversation layer

Orchestration layer

Discovery layer

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

The directors orchestrate a number of inputs – cameras, stock photos, replays, 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, game replays, constantly discovering interesting facts about the game.


Advanced Analytics Platform

Identify Assemble Score

Identity Resolution

Integration Engine

Command Center

Unstructured Discovery

Structured Discovery

Conversation Level

Orchestration Level

Discovery Level

DMZ Opt-in / Opt-out Obfuscation

Web / Cable Interactions

Conversations

Location

CRM / POS

Orders

Bills

Act / Respond

Model Management


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

Monetizable intent to buy products •  I need a new digital camera for my food pictures,

any recommendations around 300?

•  What should I buy?? A mini laptop with Windows 7 OR a Apple MacBook!??!

Location announcements •  I'm at Starbucks in Times Square

Life Events •  College: Off to Stanford for my MBA! Bye Chicago!

•  Looks like we'll be moving to New Orleans sooner than I thought.

Intent to buy a house •  I'm thinking about buying a home in Buckingham Estates per a

recommendation. Anyone have advice on that area? #atx #austinrealestate #austin

Personal Attributes • Demographics

Life Events • Life-changing event

Relationships • Personal, business

Timely Insights •  Intent to buy

Products Interests • Personal preferences

Social Media-based 360˚

Consumer Profiles


Identity Resolution

[email protected]

Job Applicant

Identity Thief

Top 200 Customer

Criminal Investigation


Sensor

Predictive Modeler

Scorer

Analytics Engine

High Velocity

High Volume

Real-time Adaptive Analytics


Overview





© 2012 IBM Corporation 29 10/30/12 29

§  Too complex an infrastructure

§  Too complicated to deploy §  Too much tuning required

§  Too inefficient at analytics

§  Too many people needed to maintain §  Too costly to operate

29

Traditional data warehousing

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

has become too complex for many customers

Nearly 70% of data warehouses experience performance constrained issues of various types

© 2012 IBM Corporation 30 10/30/12 30

We are observing an evolution

§  Monolithic EDW (data)

§  Data and data mart sprawl

§  Lack of enterprise agility §  Complex structure, process &

architecture – focused

§  Governance: limited or lacking §  Everyone talking about Analytics

§  “Smart Consolidation”

§  Consolidate sprawl & reduce cost

§  Analytics delivered via appliances & specialized systems (API’s)

§  Time to value is paramount

§  Centralized data governance program §  Analytics integrated to real-time

business operations

Where the industry has been Where the industry is going

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 Data is summarized Social media is correlated with masked data Inferences are projected to segments Actions are broadcasted to segments

PII data includes opt-in Different forms of permission seeking / management Insight created on a 1-to-1 basis Trust and privacy is personalized and closely managed

Data masking retains non PII content Identification and categorization of PII data Rigorous process for data masking

Rigorous management of privacy management No contamination of anonymous and personalized 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” 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 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

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

1

2

4

5

3

Adhoc

Differentiating

Breakaway

Competitive

Foundational


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


Information Agenda teams are conducting analytics workshops world wide across many industries.

SOA Vision Business

Objectives & Strategies

Assess current / planned architecture using accelerators Identify the Gaps

Analyze non - functional requirements -

Prepare a final report Provide Recommendations

Understand business goals and SOA vision

Review Information Delivery Capabilities

Conduct diagnostic interviews Understand current business challenges / opportunities

Inputs Activities Outputs Current State

Prioritized Business Initiatives Existing Business & IT Environment

Assessment Collect Data Verify Synthesize

Recommendations Summary Details

Analyze current and planned IT initiatives Scope the assessment

Analyze key business scenarios Assess current / desired Information Maturity level

IA for Education IOD Reference Architecture

Current & Planned Services

Business & IT Information Mgt

Practices

Existing Data Environment

Prepare a final report Develop Roadmap and Optimization Plan

Develop Recommendations Document and Present

IA Maturity Assessment

Information Agenda Accelerators

Assess quality of information delivered to the business


Social Media Maturity Model

Ad hoc Foundational Competitive Differentiating Breakaway

Capability: Monitor brand sentiment

Marketing has hired a set of interns to monitor social media data

Organizational accounts to collect sentiment data on social media sites (FB, Yelp, etc.)

Customer data from social media is collected and analyzed using analytical tools

Organization engages in social media conversation to influence customer sentiment

Customer sentiment is integrated with product and marketing processes

Measurements Brand sentiment

Baseline Collected Measured Influenced to positive direction

Influenced to positive direction

Identification of advocates / ambassadors

Baseline Low Medium High High

Impact on brand / revenue

Baseline Baseline Small Medium Large


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!


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.

What’s the book about?

Business and IT leaders who are looking for practical advice on how to drive immediate business results with analysis of big data

Who is this book for?

•  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

Where can you get a copy?

Big Data Analytics – New Book Launching at Information On Demand 2012!

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

Dr. Arvind Sathi is the World Wide Communication Sector architect for the Information Agenda team at IBM. His primary focus has been in creating visions and roadmaps for Advanced Analytics at leading IBM clients in telecommunications, media and entertainment, and energy and utilities organizations worldwide. He has conducted a number of workshops on Big Data assessment and roadmap development.

Audience •  mid to Sr. mgmt executives in network operations, customer service, sales, marketing, strategy or IT •  IT service & software provider community • Industries covered – Financial services, Public services, healthcare, retail, telecom, energy & utilities, media & entertainment.

Next Steps

•  Get a complimentary copy of the book at Information On Demand 2012 Book Store or request the IBM sales rep to order one for you •  Request a briefing on Big Data Analytics for key stakeholders from IT and Business in your organization


Technology

Big game changers for telco