Post on 04-Jan-2016
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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 (hengcao@cn.ibm.com)A joint effort from IBM Global Research Labs (China Haifa and India) Contact: Harriet Cao (hengcao@cn.ibm.com)
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.”
IBM Do not Distribute ©2012 IBM Corporation
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
Dynamic Recommendation
& Promotion
Viral MarketingViral Marketing
Churn PreventionChurn Prevention
Whitespace customer targeting
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
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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?
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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!
IBM Do not Distribute ©2012 IBM Corporation
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
Dynamic Recommendation
& Promotion
Dynamic Recommendation
& Promotion
Viral MarketingViral Marketing
Churn PreventionChurn Prevention
Whitespace customer targeting
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
IBM Do not Distribute ©2012 IBM Corporation
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”
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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)
IBM Do not Distribute ©2012 IBM Corporation
Behide scene, deep customer insights from unstructured data
Also take a look how we establishing the “hobbies” from the mobile usage data
IBM Do not Distribute ©2012 IBM Corporation
Behide scene, deep customer insights from unstructured data
Also take a look how we establishing the “hobbies” from the mobile usage data
IBM Do not Distribute ©2012 IBM Corporation
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
IBM Do not Distribute ©2012 IBM Corporation
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