tony jebara - sense networks
TRANSCRIPT
Sense Networks
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Mining Customer Location Data to Provide Business Intelligence
Tony Jebara
Sense Networks
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www
A network of online places
Sense Networks
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A network of online people
Sense Networks
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From online to real networks? What’s next?
a network of real places
a network of real people
Online data is easy to get, what about the real world?
GPS LBS
Location Data
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GPS, LBS, and location data
GPS LBS
Location Data
VEHICLES APPS DEVICES CARRIERS
SENSE NETWORKS ANALYSIS, NETWORKS OF PLACES & PEOPLE, SEGMENTATION
Marketing Advertising Collaborative Filtering
Social Recommendation
Search
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The Old View of LBS Data
Single Ping @ Starbucks: No personalization, no targeting Can’t use a single ping, too much error in space & time… It’s not just about when and where but also about who
=
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The New View of LBS Data
Location history for understanding & personalization Store data over space and time to overcome accuracy issues Lesson: save your LBS data to segment your customers!
+ = Health & Fitness Young Adult Outdoorsy
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Who Has Historical Data?
Carriers
Infrastructure
Device Mfgrs
Ericsson Alcatel-Lucent
Nokia Siemens Networks
RIM
Bank of America Polaris
TruePosition
Nokia
Vodafone
Samsung
Other
WPP
AT&T
Newfield
Airsage
DATA
Qualcomm Publicis
Omnicom
Sprint
GloPos
Verizon T-Mobile
User
Ad/Content
Nielson
Loc-aid Pinch
WaveMarket
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Infrastructure Providers 2009 Q1
missing: small & enterprise players: Newfield, Airsage, ZTE, etc.
• Infrastructure looking for new competitive advantages • Along with Carriers: looking sources of top line growth via
• targeting, advertising, content delivery...
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Carriers and Prepay
• Prepay carriers know less and less about their customers • Now: Churn management & segmentation w/o billing info • Soon: targeting, advertising, content delivery...
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Ericsson Ad Orchestrator
• Infrastructure solution to ad targeting/segmentation… • How to get the segments?
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MacroSense Segmentation • Sense Networks’ solution: Convert massive per-user call/location data segments
Location & Call Data
Sense Networks MacroSense
Segmentation
SOURCE MONTHS PINGS USERS
18 10b 1.4m
4 8b 4m
12 2b 4m
MDN WEALTH AGE CHURN TRAVELER
6462123442 200,000 46 6% 30%
9174341434 35,000 43 4% 20%
6468762413 150,000 31 11% 85%
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MacroSense Architecture
Demo Tree
Server
Learning Engine
User Segment Output
Raw Customer
Call & Location
Data
Features1….
Features2.…
Features3….
Features4….
Features5…
……
……
……
……
……
……. SIC Tree
Server
Proprietary Non-PII Profiles
Delta
Normalizer
Roller
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GPS and location data
10+ million devices giving (lat,long,time,acc)… lingua franca
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Sense Networks
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CitySense: where is everyone • Citysense: real-time density of users at every street corner • Poisson models find most active bars/restaurants
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Next: where’s everyone like me Need to have a network of people
Each dot is a user
Dot’s color is user’s social cluster
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Network of People who is like whom? who co-locates with whom?
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Network of People Hard to say if User A is like User B…
… don’t just look if they co-locate physically … check if they overlap semantically (network of places)
User A User B
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Network of Places is place A like place B? Look at each place’s Flow, Commerce & Demographics
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Network of Places: Flow Look at flow A to B
Markov transition
Apply MVE on graph
Color code clusters in graph
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Encoding a Person’s Lifestyle 9 example users’ lifestyle matrices
no PII information
compute pair-wise similarity from matrices = how much two people co-locate semantically
… can then use machine learning to segment, predict, etc.
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Churn Advertising Marketing Collaborative Filtering Demographics++
People Network Segmentation
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Network of People: Segments
“Young & Edgy” • Out every night in young, racially diverse, low income neighborhoods
“Weekend Mole” • Out occasionally on weeknights, typically middle-aged, Latino, middle-income neighborhoods
“Mature Homebody” • Rarely goes out, typically spends nights in mature, white, higher income neighborhoods
Segmentation: Standard Output
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Proprietary & Confidential
Convert call, location & lbs data into Claritas type segments e.g. Pepsi wants to send ad to only “Young&Edgy” Move lbs into established advertising industry…
Raw Customer
Call & Location
Data
01 - Millionaires: Millionaires, as the name implies, collects America’s most successful achievers and old money. It ranks first for both median and per-capita income, salaries, self and investment income, home values and net worth, and ranks second behind the Urban Brahmins in higher education and professional/managerial occupations.
02 - Country Clubbers: Country Clubbers are one rung down from the Millionaires and ranks second on all measures of income and affluence and third in college and postgraduate education. These expensive suburbs are packed into our three great metropolitan strips, dubbed “Bos-Wash”, “Chi-Pitts”, and “San-San”, with the bulk (40%) in Bos-Wash. Much of this wealth is new money, earned and freely spent by captains of business and technology.
03 - Turbo Boomers: Turbo Boomers rank first in the large “baby boomers” age group of 35-44 and are concentrated in the rapid growth cities of Atlanta, Washington DC, Dallas, Denver, Los Angeles and San Francisco. They are heavy hitters, highly educated and employed in executive and professional occupations ranking second in marriage and fourth for household income.
…
MACROSENSE
SEGMENTATION
Segmentation: Car Buyer
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Proprietary & Confidential
High end large cars for wealthy, middle age families with kids
Low end small cars for lower income, middle/older age consumers
Recommendation & Marketing based on the Network Identifying Active New Car Shoppers
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Case Study: Churn
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Case Study: Apps
Sense Networks
Discussion: New LBS Opportunity
• LBS and Location more about segmenting customer • Store your data to understand your customer • Use MacroSense to convert LBS history into segmentation
• Infrastructure (Ericsson, Alcatel, NSN,…): best data sources • Carriers (AT&T, Sprint,…): want growth, ads, targeting • Advertisers (WPP, Nielsen,…): want segmentation
• Sense Networks: segments from location & call data • Enables online business, segmentation, personalization …from Offline LBS data