Tony Jebara - Sense Networks

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Tony Jebara - Sense Networks

  1. 1. Sense Networks Mining Customer Location Data to Provide Business Intelligence Tony Jebara 1
  2. 2. Sense Networks www A network of online places 2
  3. 3. Sense Networks facebook A network of online people 3
  4. 4. Sense Networks From online to real networks? What’s next? a network of real places GPS LBS Location Data a network of real people Online data is easy to get, what about the real world? 4
  5. 5. Sense Networks GPS, LBS, and location data Collaborative Marketing Advertising Search Social Filtering Recommendation SENSE NETWORKS ANALYSIS, NETWORKS OF PLACES & PEOPLE, SEGMENTATION GPS LBS Location Data VEHICLES APPS DEVICES CARRIERS 5
  6. 6. Sense Networks 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 6
  7. 7. Sense Networks The New View of LBS Data + = Health & Fitness Young Adult Outdoorsy Location history for understanding & personalization Store data over space and time to overcome accuracy issues Lesson: save your LBS data to segment your customers! 7
  8. 8. Sense Networks Who Has Historical Data? User Verizon T-Mobile Sprint Vodafone AT&T Device Mfgrs Carriers Nokia Samsung Google RIM Qualcomm Publicis DATA Newfield Bank of America WPP Polaris GloPos Ericsson Nielson Google Omnicom TruePosition Alcatel-Lucent Nokia Siemens Networks Airsage Other Loc-aid Infrastructure Pinch Ad/Content WaveMarket 8
  9. 9. Sense Networks Infrastructure Providers 2009 Q1 missing: small & enterprise players: Newfield, Airsage, ZTE, etc. •  nfrastructure looking for new competitive advantages I •  long with Carriers: looking sources of top line growth via A •  argeting, advertising, content delivery... t 9
  10. 10. Sense Networks Carriers and Prepay •  repay carriers know less and less about their customers P •  ow: Churn management & segmentation w/o billing info N •  oon: targeting, advertising, content delivery... S 10
  11. 11. Sense Networks Ericsson Ad Orchestrator •  nfrastructure solution to ad targeting/segmentation… I •  ow to get the segments? H 11
  12. 12. Sense Networks MacroSense Segmentation •  ense Networks’ solution: S Convert massive per-user call/location data  segments SOURCE MONTHS PINGS USERS Location & 18 10b 1.4m 4 8b 4m Call Data 12 2b 4m Sense Networks MacroSense MDN WEALTH AGE CHURN TRAVELER 6462123442 200,000 46 6% 30% Segmentation 9174341434 35,000 43 4% 20% 6468762413 150,000 31 11% 85% 12
  13. 13. Sense Networks MacroSense Architecture Raw Features1…. Roller Customer Normalizer Call & Features2.… Location Features3…. Data Features4…. Delta Features5… …… Demo …… Tree Proprietary Server …… Non-PII Profiles …… …… User SIC ……. Learning Segment Tree Engine Output Server 13
  14. 14. Sense Networks GPS and location data 10+ million devices giving (lat,long,time,acc)… lingua franca14
  15. 15. Sense Networks 15
  16. 16. Sense Networks CitySense: where is everyone •  itysense: real-time density of users at every street corner C •  oisson models find most active bars/restaurants P 16
  17. 17. Sense Networks 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 17
  18. 18. Sense Networks Network of People who is like whom? who co-locates with whom? 18
  19. 19. Sense Networks Network of People Hard to say if User A is like User B… User A User B … don’t just look if they co-locate physically … check if they overlap semantically (network of places) 19
  20. 20. Sense Networks Network of Places is place A like place B? Look at each place’s Flow, Commerce & Demographics 20
  21. 21. Sense Networks Network of Places: Flow Look at flow A to B Markov transition Apply MVE on graph Color code clusters in graph 21
  22. 22. Sense Networks 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. 22
  23. 23. Sense Networks People Network Segmentation Churn Advertising Marketing Collaborative Filtering Demographics++ 23
  24. 24. Sense Networks Network of People: Segments “Young & Edgy” • Out every night in young, racially diverse, low income neighborhoods “Weekend Mole” “Mature Homebody” • Out occasionally on • Rarely goes out, typically weeknights, typically spends nights in mature, middle-aged, Latino, middle- white, higher income income neighborhoods neighborhoods 24
  25. 25. Proprietary & Confidential Segmentation: Standard Output 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… SEGMENTATION 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 Raw professional/managerial occupations. Customer MACROSENSE 02 - Country Clubbers: Country Clubbers are one rung down from the Millionaires and ranks second on all measures of income and affluence and Call & third in college and postgraduate education. These expensive suburbs are packed into our three great metropolitan strips, dubbed “Bos-Wash”, “Chi-Pitts”, Location 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. Data 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. … 25
  26. 26. Proprietary & Confidential Segmentation: Car Buyer Recommendation & Marketing based on the Network Identifying Active New Car Shoppers High end large cars for Low end small cars for lower wealthy, middle age families income, middle/older age with kids consumers 26
  27. 27. Sense Networks Case Study: Churn 27
  28. 28. Sense Networks Case Study: Apps 28
  29. 29. Sense Networks Discussion: New LBS Opportunity •  BS and Location more about segmenting customer L •  tore your data to understand your customer S •  se MacroSense to convert LBS history into segmentation U •  nfrastructure (Ericsson, Alcatel, NSN,…): best data sources I •  arriers (AT&T, Sprint,…): want growth, ads, targeting C •  dvertisers (WPP, Nielsen,…): want segmentation A •  ense Networks: segments from location & call data S •  nables online business, segmentation, personalization E …from Offline LBS data

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