© 2013 IBM Corporation

A Deep Dive into “First of A Kind” Big Data Telco Solution Session #: ISA-3638

Sambit Sahu, Arvind Sathi, Tommy Eunice, Mathews Thomas Ken Kralick IBM

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Agenda

•  Motivation and Background •  IBM Research activities •  Advanced Analytics Platform •  Life Style Analytics

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Telco and cross industry data to create a unified view of the customer. Mobility information is becoming increasingly valuable…..

Structured Repeatable

Linear Monthly sales reports

Profitability analysis Customer surveys

Other    Industries  

Other  Data  

Industry  Reports  

Retail  

Social    Media  Data  

Customer  • Segment  

• Social  Network  • Demographics    • Sex,  Age  Group,  etc  

• Tenure  • Rate  plan  

• Credit  RaBng,  ARPU  Group  

Device  • Class  

• Manufacturer  • Model  

• OS  • Media  Capability    • Keyboard  Type  

TransacBons  • Voice,  SMS,  MMS  

• Data  &  Web  Sessions  • Click  Streams  

• Purchases  • Downloads  

• Signaling,  AuthenBcaBon  • Probe/DPI  

Network  • Availability    

• Throughput/Speed  • Latency  • LocaBon  • FaciliBes     Interface  

• Discovery  • NavigaBon  

• RecommendaBons  

Product/Service  • SubscripBons  

• Rate  Plans  • Media  Type  

• Category/ClassificaBon  • Price  

Starts,  Stops  Success  Rates  

Errors  

Throughput  Setup  Time  

ConnecBon  Time  Usage  

Recency  Frequency  Monetary  Latency  

Telco Data Cross Industry Data

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Building Context and Intent from Location data

•  Deriving location: location information may be derived using multi-modal information –  CDR data, tower data, device data, Wi-fi etc.

–  Accuracy of location information depends on data fidelity etc.

•  Building context: making sense of the location information –  Correlate location information with business data

–  Various other correlation rules may be used to build a rich context

•  Inferring intent: infer consumer level intents by leveraging location and mobility patterns

Deriving Location Inferring Intent Building Context

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Data Cell tower locations Wi-fi locations Device locations Device usage data – apps, web

sites Customer data – demographics

Refined locations Mobility Patterns Hang outs Hang outs correlated with business locations Mode of transportation Traveling buddies

Analytics

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Type of Location Analytics…..

Habitual Journey Patterns

Demographic customer profiles

Common origins and destinations

Direction of travel

Level of Mobility + Segmentation

Aggregated Mode of transport

Average journey times

Travel pattern anomalies

Accurate Location

Congestion

Real time traffic incident flags

Optimal route planning

Foot traffic Customer wait times

Individual mode of transport

Possible with event data More detailed data required

VCC Board Morph Update, June 2011

First-of-a-Kind Program §  Experimental technology-based

solutions engagements

§  Testing tomorrow’s innovations on today’s business problems

§  Yielding prototype solutions across a range of industries

§  Creating valuable intellectual capital for IBM’s portfolio

§  Value to IBM Clients

–  Early market advantage

–  Access to world class researchers

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FOAK Deliverables

•  Early thought leadership and experiences with new technologies

•  Working prototype of an innovative solution not yet available in the marketplace

•  The know-how to improve a business process or solve a problem

•  Software components, methodologies and tools

•  Press & media coverage

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Agenda

•  Motivation and Background

•  IBM Research activities •  Advanced Analytics Platform •  Life Style Analytics

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Two Scenarios: Aggregate and Individual

•  Aggregate Anonymized Analytics: Sensing City-scale people movement from Telco data and leveraging for Transit Optimization

•  Enriched Consumer Profile: Customer Analytics with Mobility Profiles from Telco Data

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Enriched Consumer Profiles for Enabling Telco Data Monetization

•  We develop enriched consumer profiles by deriving insights about consumer preferences, life style, and intent from location, mobility and call data joined with use case appropriate data sources.

•  Enriched consumer profiles are utilized to enable new services and effective campaign through targeted segmentation.

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Two Scenarios: Aggregate and Individual

•  Aggregate Anonymized Analytics: Sensing City-scale people movement from Telco data and leveraging for Transit Optimization

•  Enriched Consumer Profile: Customer Analytics with Mobility Profiles from Telco Data

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Sensing City Scale People Movement from Telco Data

Cities Demonstrated: Istanbul (Turkey), Dubuque (USA) for Transit Optimization and a series of subsequent client pipeline

Challenge Cities have very little real understanding of where citizens, goods and

transportation move during the day. Without this information it is difficult to accurately plan and manage the usage of roads and infrastructure.

Solution Using a variety of real time data from “smart phones”, GPS devices, terminals, traffic cameras, public transportation schedules and transit data, develop models of zonal density, flow of goods and origin / destination pairs. From these models, drive processes to manage this flow against a specific objective.

Benefits Evaluates the efficacy of existing transit system and transportation infrastructure; provides the structure for design incentive strategies to win new riders – information, incentives, services; optimize fleet operations in situations where demand outpaces supply; manage revenue through better zoning and permits. comprehensive solution that will address the management of congestion, fleet management, people attending events, and multimodal transit

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Sensing People Movement from Telco Data

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Identifying Meaningful Locations

Where People Live Where People Work

Istanbul Movement Analysis

- 4.7 million phones w. 3B+ events/week

- Accurate detection of home, work & meaningful locations

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Traffic Monitoring Uses basic analytics building blocks already seen to display time based traffic flow levels mapped to city road system. A snapshot at 8:30am:

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Commuter Pain Index

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Feeder Bus Route Optimization for M4 Metro Line on Anatolian side of Istanbul

Feeder bus routes based on demand to 4 metro stations on Kadikoy-Kartal metro line

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Optimal Bus Stop Location Design

•  Stops are added by considering the greatest potential demand for transit and accessibility at origin and destination

•  Some stops are added to far places in which demand to the area already served by existing stops is potentially large

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Two Scenarios: Aggregate and Individual

•  Aggregate Anonymized Analytics: Sensing City-scale people movement from Telco data and leveraging for Transit Optimization

•  Enriched Consumer Profile: Customer Analytics with Mobility Profiles from Telco Data

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Consumer Analytics with Enhanced Consumer Profiles

•  Derive advanced location/mobility attributes and patterns from Telco data to enrich consumer profiles with mobility context

•  Derive predictive model about consumers location and mobility patterns

•  Leverage enriched consumer profiles for data monetization opportunities by correlating and joining other data sources

•  Build an operational asset on IBM Big Data platform to enable Telco to extract mobility attributes and patterns efficiently

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Set of example mobility attributes

•  Base set of example mobility attributes – Home and work location

– Weekday top locations

– Weekend top locations

– Meaningful location detection

– Classification of where and when time spent

– Detecting tourism pattern

– Detecting specified habits related to mobility

–  Trip purpose

– Anomaly in mobility from baseline patterns

– Detecting who’s who in the household based on mobility pattern

•  Advanced predictive models (Next Best Location) – Likely place a person would be at a future time

– Likelihood of a person going to a Mall during this weekend

– When this person is likely to be a tourist

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Enhanced Micro-segmentation with Mobility Model

Mobility Patterns

Buying Patterns

Social Patterns

Demographics

• Gender • Age group • Address • Income

Historical buying patterns

Social network influencers

Mobility Model • Location and movement pattern (space, time) • Meaningful location detection • Meaningful location classification • Trip purpose • Estimated Duration of stay • Estimated Duration of travel • Mode of travel • Calling patterns • Detecting tourist patterns • Detecting student patterns • Estimated demographic profile of user of phone • Anomalies in regular patterns

Enhanced Attributes for Customer Segmentation

Retailer Customer Profile

Real Time Targeted Advertisement for IPTV

AAP (Advanced Analytics Platform)

3 - AAP catches the new football interest flag, his frequent sports shopping, and in realtime matches Tom’s profile with an offer for 20% off coupon to an Nike store.

4 - Tom is also an existing SMS Opt-In mobile cust.

5 – Tom receives targeted IPTV advertisements based on his IPTV, mobility and social profiles

2 - Tom is channel surfing, mostly sports channels, primarily football games where Nike advertises a lot (AAP enhances his customer profile, after 10 football games viewed in 1st month, with an interest flag as a “football fan”)

Enhanced Cust. Profile Interest / Mobile # / Email

1- Tom activates IPTV service with the America 50 package and adds the ESPN sports ala carte option (we have an initial customer profile with his fixed # and a mobile#)

A la carte option Sports Packages

tom@gmail.com  

212-­‐201-­‐1234  

Language Package

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Location Based Real Time Offering on Mobile Phone

Lisa

4 - AAP catches that Lisa is entering a mall, and matches her “Fashion” interest flag and “Perfume” preference, sends in realtime an offer for 20% off coupon for Byonce fragrance at Sephora in that mall.

5 - Lisa receives an SMS/email/App notification that her mobile app account contains a new offer for Beyonce perfume.

Beyonce Fan Page

2 - She follows a friend’s post on FB and clicks the Like button on the Beyonce Fan Page.

3 - Lisa’s IPTV viewing & mobile clickstream behaviors set her Interest flag to “Fashion” and one preference to “Perfume”.

6 - Lisa uses the mWallet app on her smartphone to purchase some perfume at POS via NFC.

1- Lisa is a mobile subscriber with Telco and downloads the mobile app and agrees to receive offers related to her interests.

AAP (Advanced Analytics Platform)

Retailer Customer Profile

Enhanced Cust. Profile Interest & Preference

IPTV a la carte option & Mobile Features/Apps

IPTV Lang Pkg & Mobile Pkg

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Agenda

•  Motivation and Background •  IBM Research activities

•  Advanced Analytics Platform •  Life Style Analytics

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2

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Advanced Analytics Platform

End-use Applications

Analytics Visualization

Big Data Analytics Warehouse

Predictive Analytics

Sense

Analyze Act

Search / Explore

KPIs

Dashboards

Drill-Downs

Reports

Marketing Campaigns

Rules Engine

Behavioral Analysis

Outcome Optimization

Propensity Scoring

Model Creation

Structured / Unstructured

Data

Data Governance Data Integration

ETL/ELT

Cha

nge

Cap

ture

Dat

a Q

ualit

y / V

alid

ity /

Sec

urity

- P

rivac

y

Form

at /

Uni

t Con

vers

ion

Con

solid

atio

n / D

e-du

plic

atio

n

Dat

a R

epos

itorie

s

Network Data

Customer Behavior Data

Cus

tom

er

Dat

a P

rodu

ct

Dat

a N

etw

ork

Topo

logy

D

ata

Con

tinuo

us F

eed

Sou

rces

Usage Data

Reference Data

Historical Analysis Data

Demographics Segmentation Location Past Actions

Propensity Scores Behaviors

Predictive Model Deployment

Actionable Insight

Stream Processing

Streaming Data

Operational Systems

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AAP Capabilities High Performance Historical analysis (Big Data Platform)

Model Based Analytics - behavioral scoring, micro segmentation, correlation detection analysis

Real-time scoring, classification, detection and action

Visualize, explore, investigate, search and report

Take action on analytics

IBM’s Advanced Analytics Platform (AAP) Supports Use Cases across the business with New Era Capabilities

Create new Services and Business Models Transform Operations Build Smarter

Networks Personalize Customer Engagements

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 Social  Informa-on  

   

Loca-on  Informa-on      

Customer  database  informa-on  

     

InfoSphere Streams Low Latency Analytics for streaming data

InfoSphere BigInsights Hadoop-based low latency analytics for

variety and volume

IBM Netezza BI and Ad Hoc Analytics

Structured Data

Customer database Coremetrics

Low Latency Analytics for streaming data

Data sources…..

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The carmel frappuccino in starbucks is just heavenly.

IBM  BigInsights  Text  Analy-cs  

Accelerators  

BigInsights  Custom  analyBcs  

First  Name:  Joe  Last  name:  Smith  Address:  1234  Anyroad  ….  [  X  ]  Likes  coffee  [  X  ]  Likes  frappuccino  [        ]  Likes  cappuccino  ….  [  posiBve]  SenBment  coffee      

Social Media Profile Creation

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URL Analysis- Extract Implicit User Profile

analysis"URL Analysis: for each user, report the most meaningful interests to describe her profile.

Large scale analysis

Update users profiles"

Consume"

Adaptive user segmentations: create new users segmentation by clustering similar interests

Data Cleansing

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CDR Relationship Analytics

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Collecting and analyzing in real-time millions of events from multiple sources to detect the right time to respond to the event

CDRs

Billing

CRM

Location

Account Mgt

Internet

Network

Millions of events per second

Microsecond Latency

Dropped Calls

Outgoing International Calls

Call Duration

Extra Call

Contract Expiration

Entered new cell

New Top-Up

5 minutes left on pre-paid

EDW

Invoice Issued

Predictive Models

3 dropped calls in 10 minutes

Customer is close to a store

Customer entered a shopping area

Invoice paid + called competitor

Smart phone browsing pattern

Customer is watching a video

Congested Cells

Invoice Paid

Acquired new products

Change contracts

Brand Reputation

Customer Sentiment from Social network

Customer is roaming

Customer is at home

Campaign

Management Invoke appropriate campaign

Score

Real-time Stream Analytics

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Campaign Management

Campaign Response

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Agenda

•  Motivation and Background •  IBM Research activities •  Advanced Analytics Platform

•  Life Style Analytics

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Determining Buddies, Hangouts, Life Style Example Lifestyle Attributes for marketing demonstration

§  Subscriber Lifestyles

§  Popular Locations

§  Subscriber Pairings Who Are You? Homebody Daily Grinder Delivering the Goods Globetrotter Nomad

10 Top Hangouts

Best Buddies

Next Steps •  Given the lifestyles, popular locations, and best buddy data => predict where

individuals or groups of similar individuals will be and when. •  Use time series modeling and clustering we can create time/location based marketing

campaigns targeted at homogenous groups in specific locales.

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© 2012 IBM Corporation

Buddies, Hangouts, Globtrotters Areas of mobility analytics

n  Individual Lifestyle and Usage profiles

n Popular Locations with specific profiles

n Who are the Buddies

n Predicting where people go

Who Are You? Homebody Daily Grinder Delivering the Goods Globetrotter Sofa Surfer

10 Top Hangouts

Mobile ID Buddy Rank

2702 1

1256 2

8786 3

4792 4

8950 5 39

What are Profiles

•  Lifestyle Profiles are defined by marketing analysts for specific use cases or marketing programs

•  Usage Profiles are created using data mining algorithms and define how a person uses services during the day

•  Location Affinity is created with algorithms and determines preferred locations for individuals throughout the day and week

•  Together these uniquely define a person with relation to how

the retailer or marketer might want to market to them

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Creating Groups of Mobility Profiles Enables Better Prediction for Certain Groups

l  profiles breakdown like this

l  Homebody, doesn't visit too many unique locations

l  Daily Grinder, back and forth to work, quiet weekends, makes stops along the way

l  Norm Peterson, inside the lines, no deviations

l  Delivering the goods, no predictable patterns, many different locales during the day

l  Globe Trotter, either not in town, or keeps their phone turned off

l  Rover Wanderer, spends evenings at various location (sofa surfers www.couchsurfing.org)

l  “Other”, is a group hard to categorize

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By Profile, when is it easy or difficult to predict where they will be?

Profile Day Time Predictability

Daily Grinder Thursday Dinner Highest

Daily Grinder Friday Afternoon Lowest

Homebody Saturday Night Highest

Homebody Wednesday Morning Lowest

These are the 2 most predictable profiles, yet there is diversity in their predictability. To best communicate with Daily Grinders, contact them on Thursday Afternoons just before dinner

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Preferred Locations of by profile type at Lunchtime Weekdays (Central Stockholm)

Delivering the Goods

Night Shifters

Daily Grinders

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What analysis is available (Anonymous Data)

From the mobility profiles, summarized, anonymous analysis is available

l  Summarized to ensure anonymity, analysis of popular locations by time of day and profile of subscribers is possible

l  For retailers this information can help understand what types of people are nearby at lunch time

l  What types of people prefer which areas. Some obvious results are Globe Trotters go to airports, Daily Grinders go to office buildings. Other non-obvious results show up also.

l  Are there predictable patterns that we can use to target certain groups in the future?

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What Makes this Possible?

l  Using the power of Netezza and modeling capabilities of SPSS we can literally throw all the data at data mining algorithms and create discrete clusters of subscribers by activity, mobility

l  Apply the data mining outputs to the entire subscriber base by creating detailed specific analyses for each subscriber refined by the mobility profiles

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Enriched Consumer Profile Hub

Customer  Profile  Hub  

IPTV    -­‐   SubscripBon  Billing  -­‐ VOD  Billing  &  viewed  -­‐   channel  viewing  history  -­‐ -­‐  contents  purchased  -­‐ Logs  &  Tuning  Events  -­‐   package  subscripBon  

Mobile  -­‐   LocaBon  -­‐   URL+App  Transac-ons  -­‐   xDRs  and  inb.  roaming  -­‐   RAN  (incl.  HLR/VLR)  -­‐   Top  Up  -­‐   Pkgs  -­‐   Billing  -­‐   SMS,  browing  URLs  

Other:  -­‐  Devices  -­‐  Dealer  Network  -­‐   Contact  Center  -­‐   Call  Recordings  -­‐   Trouble  Tickeing  -­‐   Campaign  Results  (Imagine)  -­‐   Loyalty  -­‐   CompeBBon  Website  -­‐   Retail  Transac-ons  

Fixed  -­‐   CDR  -­‐   URL  (IP)  -­‐ Radius  (IP-­‐Cust)  -­‐   Pkgs  -­‐   Billing  

Historical  TransacBons/    

Events  

Partners/Retailers  

AdverBsers  

Other/Internal  GIS  -­‐   Business  map  and  numbers  -­‐   Point  of  Interest  maps    

Consum

ers  o

f  new

 Insig

hts  

Feedback  

Social    Media  Data  

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Agenda

•  Motivation and Background •  IBM Research activities •  Advanced Analytics Platform •  Life Style Analytics

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