IBM Research

1. IBM Research Addressing the Big Data Analytics Opportunities for Telco – Like-Minded Community Detection, Customer Targeting, Viral Marketing and Mobile Usage Data Monetization A joint effort from IBM Global Research Labs (China Haifa and India) Contact: Harriet Cao (hengcao@cn.ibm.com)

2. Telco service subscribers are becoming more instrumented, more connected and smarter Interconnected Intelligent WIKI’s Instrumented BLOGS COLLABORATIVE VIRAL PRODUCE TWO-WAY FAST 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 • 2.4 billioninternet users • 300 millionwebsites • 1.7 exabytes of data created and stored per year • 6 billion mobile devices • 1.2 billion mobile broadband subscribers • More than 300,000iPhone applications • ± 60,000iPad applications ON A LARGE SCALE 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.” FORUMS VIDEO SHARING CONSUME A mass of conver-sations, based on two-way communication, often without the provider involved It’s no wonder that we know so much 2

3. Telco CMOs need game changing capabilities to turn vast amounts of data into actionable insights in near real time .. Streams of Insights Intelligent Actions Millions of events per second Viral Marketing Deep Insights on Single Subscriber Dropped Calls Call Detail Records Outgoing International Calls Preferred Service Call Duration Length of Time as Customer Dropped calls Extra Call Billing Dynamic Recommendation & Promotion Preferred Channel Invoice Issued Recency + Frequency + Value Microsecond Latency Required Invoice Paid Response to Media Recharge frequency Contract Expiration CRM Mobile browsing Pattern Acquired new products Interests Change contracts Churn Prevention Entered new cell Location Customer is roaming Customer is at home products, services Interests Who is talking to whom? Account Mgt New Top-Up 5 minutes left on pre-paid Who’s buying what ? Who is interested in what Whitespace customer targeting Changed Home Location Internet / Social Media Brand Reputation Customer Sentiment Insights on Like-Minded Community URLs browsed Mobile Usage Application usage internet data usage MDM EDW 3

4. What is like-minded community, and why it matters to CMOs What is it? Like-minded Community A groups of people • Socially well connected • They exhibit similar taste, interests Why it matters? Deep Customer Insights Faster in Closing deals Adding Stickiness to Your Offers Saving Money in Launching Campaigns

5. Study shows that learning a new language can help olders to stay active and become healthier, RossettaStone targets AARP with partnership and discounts 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! • Some CMOs are doing that

6. 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 Streams of Insights Intelligent Actions Millions of events per second Viral Marketing Deep Insights on Single Subscriber Dropped Calls Call Detail Records Outgoing International Calls Preferred Service Call Duration Length of Time as Customer Dropped calls Extra Call Billing Dynamic Recommendation & Promotion Preferred Channel Invoice Issued Recency + Frequency + Value Microsecond Latency Required Invoice Paid Response to Media Recharge frequency Contract Expiration CRM Mobile browsing Pattern Acquired new products Interests Change contracts Churn Prevention Entered new cell Location Customer is roaming Customer is at home products, services Interests Who is talking to whom? Account Mgt New Top-Up 5 minutes left on pre-paid Who’s buying what ? Who is interested in what Whitespace customer targeting Changed Home Location Internet / Social Media Brand Reputation Customer Sentiment Insights on Like-Minded Community URLs browsed Mobile Usage Application Usage Internet data Usage MDM EDW 6

7. 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”

8. 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 MSISDN Current 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)

9. Behide scene, deep customer insights from unstructured data • Also take a look how we establishing the “hobbies” from the mobile usage data

10. Behide scene, deep customer insights from unstructured data • Also take a look how we establishing the “hobbies” from the mobile usage data

11. Status update Message Passing Local Computation in each partition Graph Partition Graph Partition Graph Partition Graph Partition Graph Partition Graph Partition Next Iteration Behide scene, Parallel SNA algorithms fully leveraging Netezza’s Asymmetric Massively Parallel Processing architecture Taking Weakly Connected Component (the essence is BFS) for example, all the graph computations are fully distributed to S-Blade nodes of Netezza cluster Comparison between traditional SNA and X-RIME on Netezza

12. 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 PureData Analytics PureData Analytics BIG INSIGHTS(PURESYSTEM)