ibm research

Addressing the Big Data Analytics Opportunities for Telco – Like-Minded Community Detection, Customer Targeting, Viral Marketing and Mobile Usage Data Monetization

IBM Research

A joint effort from IBM Global Research Labs (China Haifa and India) Contact: Harriet Cao ([email protected])A joint effort from IBM Global Research Labs (China Haifa and India) Contact: Harriet Cao ([email protected])

IBM Do not Distribute ©2012 IBM Corporation

Telco service subscribers are becoming more instrumented, more connected and smarter

2

It’s no wonder that we know so much

Instrumented

2.4 billion internet users 300 million websites

1.7 exabytes of data created and stored per year

6 billion mobile devices 1.2 billion mobile broadband

subscribers More than 300,000 iPhone

applications ± 60,000 iPad applications

A mass of conver-sations, based on two-way communication, often without the provider involved

VIRALPRODUCE

ON A LARGE SCALE

FAST

TWO-WAY

COLLABORATIVE

CONSUME

BLOGS

VIDEOSHARING

WIKI’s

FORUMS

Interconnected

900 million users – 80% outside US; 700 billion minutes of viewing per month; 130 friends per user

465 million regular users; 250 million tweets per day

135 million members – 60% outside US

Intelligent

More than 2/3 of global consumers surveyed agreed with the following statement:

“I know exactly which communication

products/services I need and I choose the provider who is the

best able to meet them.”

More than 2/3 of global consumers surveyed agreed with the following statement:

“I know exactly which communication

products/services I need and I choose the provider who is the

best able to meet them.”


3

Millions of events per second

Call Detail Records

Billing

CRM

Location

Account Mgt

Internet / Social Media

Mobile Usage

Dropped Calls

Outgoing International Calls

Call Duration

Extra Call

Contract Expiration

Entered new cell

New Top-Up

5 minutes left on pre-paid

Invoice Issued

internet data usage

Invoice Paid

Acquired new products

Change contracts

Brand Reputation

Customer Sentiment

Customer is roaming

Customer is at home

Changed Home Location

Application usage

URLs browsed

MDMEDW

Microsecond

Latency Required

Deep Insights on Single Subscriber

Streams of Insights Intelligent Actions

Telco CMOs need game changing capabilities to turn vast amounts of data into actionable insights in near real time ..

Dynamic Recommendation

& Promotion


& Promotion

Viral MarketingViral Marketing

Churn PreventionChurn Prevention

Whitespace customer targeting


Who istalking to

whom?

products, servicesInterests

Who’s buying what ? Who is interested in what

Insights on Like-Minded Community

Preferred Service

Preferred Channel

Recharge frequency

InterestsMobile browsing Pattern

Length of Time as

Customer

Recency + Frequency

+ ValueResponse to Media

Dropped calls


What is like-minded community, and why it matters to CMOs

What is it?

Deep Customer Insights

Like-minded Community

Socially well connected They exhibit similar taste, interests

Faster in Closing dealsAdding Stickiness to Your OffersSaving Money in Launching Campaigns

A groups of people

Why it matters?Why it matters?


Study shows that learning a new language can help olders to stay active and become healthier, RossettaStone targets AARP with partnership and

discounts

Some CMOs are doing that•

Study shows that learning a new language can help olders to stay active and become healthier, RossettaStone targets AARP with partnership and

discounts

Hi Team, please try to think of another few examples too!


6

Millions of events per second

Call Detail Records

Billing

CRM

Location

Account Mgt

Internet / Social Media

Mobile Usage

Dropped Calls

Outgoing International Calls

Call Duration

Extra Call

Contract Expiration

Entered new cell

New Top-Up

5 minutes left on pre-paid

Invoice Issued

Internet data Usage

Invoice Paid

Acquired new products

Change contracts

Brand Reputation

Customer Sentiment

Customer is roaming

Customer is at home

Changed Home Location

Application Usage

URLs browsed

MDMEDW

Microsecond

Latency Required

Deep Insights on Single Subscriber

Streams of Insights Intelligent Actions

IBM Big Data for Telco Solution Allow CMOs to identify the like minded communities from the data (both structured and unstructured), use that for marketing


& Promotion


& Promotion

Viral MarketingViral Marketing

Churn PreventionChurn Prevention



Who istalking to

whom?

products, servicesInterests

Who’s buying what ? Who is interested in what

Insights on Like-Minded Community

Preferred Service

Preferred Channel

Recharge frequency

InterestsMobile browsing Pattern

Length of Time as

Customer

Recency + Frequency

+ ValueResponse to Media

Dropped calls


Like minded community --- understand community like-mindness established by service

The coming pages are going to be the like-minded community screen shots

Demo using a small data set (maybe 10)

A Market analyst selects the customer attributes he is interested in establishing the like-minded community

Show a a drop down list allow muli-selections, options on demographics, service/product purchased, hobbies,) we will select service/product purchased, also hobbies

Show the identified communities with key metrics – Number of subscribers– How active, the total duration of call times – How dense, the connection density of– The like mindness

high light a community with highest like-mindness, click to show the common things they share (e.g how many people bought the same call plan, .. They are all sports fans, some of they are not using SSM, some of them are using) (perhaps through two pie chart, one for service/product purchased, one of hobbies, in

– show the network structure

Save as community established give name as “like-minded community for sports and service purchased”


Viral marketing on MMS for sports news

Campain manager needs to launch a new compain for providing real time sports game updates, news etc through MMS

Campaign manager selects the like-minded commnunity established ealier --- this “like-minded community for sports and service purchased”,

He put in the budget for the MMS promotion (can target 100 people)

Show the budget allocation to each community, which campain manager can change – provides convenient link allow the manager to further exampe the community

Further optimize which subscribes to select– Show those selected targets in a table with info

Their MSISDNCurrent service plans they are on (prepaid, SMM bundle, Ring etc)Top 3 hobbies (sports, gardening, culture, travel. Etc)The community they are in (link to the community again) a check box to allow further selection/disselection

Click on execution

Explain that this info can be send to IBM Unica to further trackign the campain response etc… (show Unica screen)


Behide scene, deep customer insights from unstructured data

Also take a look how we establishing the “hobbies” from the mobile usage data


Behide scene, Parallel SNA algorithms fully leveraging Netezza’s Asymmetric Massively Parallel Processing architecture

Graph Partition

Graph Partition

Graph Partition

Graph Partition

Graph Partition

Graph Partition

Local Computation in each partition

Local Computation in each partition

Status updateStatus update

Message Passing

Message Passing

Next Iteration 0

20

40

60

80

100

0 20 40 60 80 100 120

Timeline of OSN Data (Sec)

Ava

erag

e C

PU

Ult

. o

f S

PU

no

des

(%

)

Taking Weakly Connected Component (the essence is BFS) for example, all the graph computations are fully distributed to S-Blade nodes of Netezza cluster

Traditional SNA X-RIME on Netezza

Parallelization None. Fully parallelized

Memory bound Limited by single machine memory Can exceed total memory of the cluster

Scalability None. Near linearly scalable to # of SPU nodes

Fault-tolerant None. Handled by Netezza Analytics infrastructure

Data movement ETL data out of database Push computing to tables in-database

Comparison between traditional SNA and X-RIME on Netezza Comparison between traditional SNA and X-RIME on Netezza


PureData Telco Appliance: Front Office Digitization

High Performance Big Data Foundation–Designed for handling deep analytics on TB+ data size –Asymmetric Massively Parallel Processing architecture for top SQL

performance– In-database analytics with linear scale-up

Deep Customer analytics: Social Network Analytics in PureData

– In database analytics for analyzing billons of Call Data Records–Deep insight to discover Like-minded Communities based on

subscriber profiles, usage data, and social affinities and interactions–Starburst Performance: Faster adoption of products + Increased

Stickiness + Highly Productive Campaigns

Integrating Unstructured Content: Content analytics over big unstructured mobile usage data

–Mining Web Behavior–New segmentation models based on mobile data usage

BIG INSIGHTS(PURESYSTEM)

BIG INSIGHTS(PURESYSTEM)

PureData AnalyticsPureData Analytics

PureData AnalyticsPureData Analytics

ibm research

Documents

ibm corporationibm

ibm corporationstudy

exabytes of data

vast amounts of data

patternlength of time

actionable insights

real time

mobile devices