THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012

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THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012

  1. 1. THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME SPEAKER: Nick Weir CEO ChoozONTuesday, November 27, 12
  2. 2. Mining  Big  Data  and  the  3V’s: The  Business  and  Science  of  Big  Data Nick  Weir,  Ph.D.   CEO ChoozOn  Corpora,on Twi$er:  @usamaf March  21,  2012 GigaOM  Structure:  Data  Conference New  York,  NYTuesday, November 27, 12
  3. 3. Outline • Big  Data  all  around  us • The  role  of  Data  Mining  and  PredicEve  AnalyEcs • On-­‐line  data  and  facts • Case  studies  from  mulEple  verEcals: 1. Yahoo!  Big  Data 2. Social  Network  Data 3. ChoozOn  Big  Data  from  offersTuesday, November 27, 12
  4. 4. What  MaPers  in  the  Age  of  Big  Data?   1.Being  able  to  exploit  all  the  data  that  is  available   • not  just  what  youve  got  available   • what  you  can  acquire  and  use  to  enhance  your  acEons 2.  ProliferaEng  analyEcs  throughout  your  organizaEon • make  every  part  of  your  business  smarter 3.  Driving  significant  business  value   • embedding  analyEcs  into  every  area  of  your  business  can  help  you  drive   top  line  revenues  and/or  boPom  line  cost  efficiencies  Tuesday, November 27, 12
  5. 5. Why  Big  Data?Tuesday, November 27, 12
  6. 6. Why  Big  Data? A  new  term,  with  associated  “Data  Scien:st”  posi:ons:Tuesday, November 27, 12
  7. 7. Why  Big  Data? A  new  term,  with  associated  “Data  Scien:st”  posi:ons: • Big  Data:  is  a  mix  of  structured,  semi-­‐structured,  and  unstructured   data: –Typically  breaks  barriers  for  tradiEonal  RDB  storage –Typically  breaks  limits  of  indexing  by  “rows” –Typically  requires  intensive  pre-­‐processing  before  each  query  to  extract   “some  structure”  –  usually  using  Map-­‐Reduce  type  operaEonsTuesday, November 27, 12
  8. 8. Why  Big  Data? A  new  term,  with  associated  “Data  Scien:st”  posi:ons: • Big  Data:  is  a  mix  of  structured,  semi-­‐structured,  and  unstructured   data: –Typically  breaks  barriers  for  tradiEonal  RDB  storage –Typically  breaks  limits  of  indexing  by  “rows” –Typically  requires  intensive  pre-­‐processing  before  each  query  to  extract   “some  structure”  –  usually  using  Map-­‐Reduce  type  operaEons • Above  leads  to  “messy”  situaEons  with  no  standard  recipes  or   architecture:  hence  the  need  for  “data  scienEsts”   –Conduct  “Data  ExpediEons”   –Discovery  and  learning  on  the  spotTuesday, November 27, 12
  9. 9. What  Makes  Data  “Big  Data”?Tuesday, November 27, 12
  10. 10. What  Makes  Data  “Big  Data”? • Big  Data  is  Characterized  by  the  3-­‐V’s: –Volume:  larger  than  “normal”  –  challenging  to  load  and  process • Expensive  to  do  ETL • Expensive  to  figure  out  how  to  index  and  retrieve • MulEple  dimensions  that  are  “key” –Velocity:  Rate  of  arrival  poses  real-­‐,me  constraints  on  what  are  typically  “batch   ETL”  opera,ons • If  you  fall  behind  catching  up  is  extremely  expensive  (replicate  very  expensive  systems) • Must  keep  up  with  rate  and  service  queries  on-­‐the-­‐fly –Variety:  Mix  of  data  types  and  varying  degrees  of  structure • Non-­‐standard  schema • Lots  of  BLOB’s  and  CLOB’s • DB  queries  don’t  know  what  to  do  with  semi-­‐structured  and  unstructured  data.Tuesday, November 27, 12
  11. 11. The  DisEncEon  between  “Data”  and  “Big  Data”  is  fast   disappearing • Most  real  data  sets  nowadays  come  with  a  serious  mix  of  semi-­‐structured  and   unstructured  components: – Images – Video – Text  descripEons  and  news,  blogs,  etc… – User  and  customer  commentary – ReacEons  on  social  media:  e.g.  TwiPer  is  a  mix  of  data  anyway • Using  standard  transforms,  enEty  extracEon,  and  new  generaEon  tools  to  transform   unstructured  raw  data  into  semi-­‐structured  analyzable  data   • Hadoop  vs.  Not  Hadoop  -­‐    when  to  use  what  kind  of  techniques  requiring  Map-­‐Reduce   and  grid  compuEngTuesday, November 27, 12
  12. 12. Text  Data:  The  Big  Driver • While  we  speak  of  “big  data”  and  the  “Variety”  in  3-­‐V’s • Reality:  biggest  driver  of  growth  of  Big  Data  has  been  text  data • In  fact  Map-­‐Reduce  became  popularized  by  Google  to  address  the   problem  of  processing  large  amounts  of  text  data:   – Indexing  a  full  copy  of  the  web – Frequent  re-­‐indexing – Many  operaEons  with  each  being  a  simple  operaEon  but  done  at  large  scale • Most  work  on  analysis  of  “images”  and  “video”  data  has  really  been   reduced  to  analysis  of  surrounding  textTuesday, November 27, 12
  13. 13. To  Hadoop  or  not  to  Hadoop? When  to  use  techniques  requiring  Map-­‐Reduce  and  grid  compu?ng? • Typically  organizaEons  try  to  use  Map-­‐Reduce  for  everything  to  do  with  Big  Data – Inefficient  and  ojen  irraEonal – Certain  operaEons  require  specialized  storage • UpdaEng  segment  memberships  over  large  numbers  of  users • Defining  new  segments  on  user  or  usage  data • Map-­‐Reduce  is  useful  when  a  very  simple  operaEon  is  to  be  applied  on  a  large  body  of   unstructured  data – Typically  this  is  during  enEty  and  aPribute  extracEon – SEll  need  Big  Data  analysis  post-­‐Hadoop • Map-­‐Reduce  is  not  efficient  or  effecEve  for  tasks  involving  deeper  staEsEcal  modeling –  Good  for  gathering  counts  and  simple  (sufficient)  staEsEcs • E.g.  how  many  Emes  a  keyword  occurs,  quick  aggregaEon  of  simple  facts  in  unstructured  data,  esEmates  of  variances,   density,  etc… – Mostly  pre-­‐processing  for  Data  MiningTuesday, November 27, 12
  14. 14. Hadoop  Use  Cases  by  Data  TypeTuesday, November 27, 12
  15. 15. Tuesday, November 27, 12
  16. 16. Tuesday, November 27, 12
  17. 17. Big  Data  Applica:ons  and  Uses 100%  Capture IT  Log  &  Security   Forensics  &  AnalyEcs Find  New  Signal Predict  Events Product  Planning Automated  Device  Data Failure  Analysis AnalyEcs ProacEve  Fixes RecommendaEon AdverEsing SegmentaEon AnalyEcs Social  Media Hadoop  +    MPP  +  EDW Big  Data Cost  ReducEon Ad  Hoc  Insight Warehouse  AnalyEcs PredicEve  AnalyEcsTuesday, November 27, 12
  18. 18. So  this  Internet  thing  is  going  to  be  big! Big  Opportunity,  Big  Data,  Big  Challenges!Tuesday, November 27, 12
  19. 19. Stats  about  on-­‐line  usageTuesday, November 27, 12
  20. 20. Stats  about  on-­‐line  usage • How  many  people  are  on-­‐line  today?   – 2.1  Billion  (per  Comscore  esEmates) – 30%  of  world  PopulaEonTuesday, November 27, 12
  21. 21. Stats  about  on-­‐line  usage • How  many  people  are  on-­‐line  today?   – 2.1  Billion  (per  Comscore  esEmates) – 30%  of  world  PopulaEon • How  much  Eme  is  spent  on-­‐line  per  month  by  the  whole  world? – 4M  person-­‐years  per  monthTuesday, November 27, 12
  22. 22. Stats  about  on-­‐line  usage • How  many  people  are  on-­‐line  today?   – 2.1  Billion  (per  Comscore  esEmates) – 30%  of  world  PopulaEon • How  much  Eme  is  spent  on-­‐line  per  month  by  the  whole  world? – 4M  person-­‐years  per  month • How  many  hours  per  month  per  Internet  User? – 16  hours  (global  average) – 32  hours  (U.S.  Average)Tuesday, November 27, 12
  23. 23. Stats  about  on-­‐line  usage • How  many  people  are  on-­‐line  today?   – 2.1  Billion  (per  Comscore  esEmates) – 30%  of  world  PopulaEon • How  much  Eme  is  spent  on-­‐line  per  month  by  the  whole  world? – 4M  person-­‐years  per  month • How  many  hours  per  month  per  Internet  User? – 16  hours  (global  average) – 32  hours  (U.S.  Average) *Sources: Feb.2012 - from Go-Gulf.com compiled from Comscoredatamine.com, Nielsen.com, thisDigitalLife.com, PewInternet.orgTuesday, November 27, 12
  24. 24. Volume,  Velocity,  VarietyTuesday, November 27, 12
  25. 25. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day?Tuesday, November 27, 12
  26. 26. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  BillionTuesday, November 27, 12
  27. 27. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day?Tuesday, November 27, 12
  28. 28. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250MTuesday, November 27, 12
  29. 29. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day?Tuesday, November 27, 12
  30. 30. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day? – More  than  800MTuesday, November 27, 12
  31. 31. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day? – More  than  800M • YouTube:  Views/dayTuesday, November 27, 12
  32. 32. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day? – More  than  800M • YouTube:  Views/day – 4  Billion  views – 60  hours  of  video  uploaded  every  minute!Tuesday, November 27, 12
  33. 33. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day? – More  than  800M • YouTube:  Views/day – 4  Billion  views – 60  hours  of  video  uploaded  every  minute! • Social  Networks:  users  who  have  used  sites  for  spying  on  their  partners? –56%Tuesday, November 27, 12
  34. 34. Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day? – More  than  800M • YouTube:  Views/day – 4  Billion  views – 60  hours  of  video  uploaded  every  minute! • Social  Networks:  users  who  have  used  sites  for  spying  on  their  partners? –56% *Sources: Feb.2012 - from Go-Gulf.com compiled from Comscoredatamine.com, Nielsen.com, thisDigitalLife.com, PewInternet.orgTuesday, November 27, 12
  35. 35. Turning  the  three  Vs  of  Big  Data  into  ValueTuesday, November 27, 12
  36. 36. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve?Tuesday, November 27, 12
  37. 37. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent?Tuesday, November 27, 12
  38. 38. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc…Tuesday, November 27, 12
  39. 39. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc… Do  we  understand  what  a  community’s  sen:ment  is?Tuesday, November 27, 12
  40. 40. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc… Do  we  understand  what  a  community’s  sen:ment  is? •  What  is  the  emoEon?Tuesday, November 27, 12
  41. 41. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc… Do  we  understand  what  a  community’s  sen:ment  is? •  What  is  the  emoEon? •   Is  it  negaEve  or  posiEve?Tuesday, November 27, 12
  42. 42. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc… Do  we  understand  what  a  community’s  sen:ment  is? •  What  is  the  emoEon? •   Is  it  negaEve  or  posiEve? •   What  is  the  health  of  my  brand  online?Tuesday, November 27, 12
  43. 43. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc… Do  we  understand  what  a  community’s  sen:ment  is? •  What  is  the  emoEon? •   Is  it  negaEve  or  posiEve? •   What  is  the  health  of  my  brand  online? Do  we  understand  context  and  content?Tuesday, November 27, 12
  44. 44. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc… Do  we  understand  what  a  community’s  sen:ment  is? •  What  is  the  emoEon? •   Is  it  negaEve  or  posiEve? •   What  is  the  health  of  my  brand  online? Do  we  understand  context  and  content? •  What  are  appropriate  ads?Tuesday, November 27, 12
  45. 45. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc… Do  we  understand  what  a  community’s  sen:ment  is? •  What  is  the  emoEon? •   Is  it  negaEve  or  posiEve? •   What  is  the  health  of  my  brand  online? Do  we  understand  context  and  content? •  What  are  appropriate  ads? •   Is  it  Ok  to  associate  my  brand  with  this  content?Tuesday, November 27, 12
  46. 46. Turning  the  three  Vs  of  Big  Data  into  Value Do  we  understand  what  each  individual  is  trying  to  achieve? • What  is  user  intent? • Cri,cal  in  mone,za,on,  adver,sing,  etc… Do  we  understand  what  a  community’s  sen:ment  is? •  What  is  the  emoEon? •   Is  it  negaEve  or  posiEve? •   What  is  the  health  of  my  brand  online? Do  we  understand  context  and  content? •  What  are  appropriate  ads? •   Is  it  Ok  to  associate  my  brand  with  this  content? • Is  content  sad?,  happy?,  serious?,  informaEve?Tuesday, November 27, 12
  47. 47. Case  Studies Yahoo!  Big  Data 18Tuesday, November 27, 12
  48. 48. Yahoo!  –  One  of  Largest  DesEnaEons  on  the  Web More people visited Yahoo! in the past month than: • Use coupons 80%  of  the  U.S.  Internet  popula:on  uses  Yahoo!   • Vote –  Over  600  million  users  per  month  globally! • Recycle • Global  network  of  content,  commerce,  media,  search  and  access  products • Exercise regularly • 100+  properEes  including  mail,  TV,  news,  shopping,  finance,  autos,  travel,  games,  movies,   • Have children living at health,  etc. home • Wear sunscreen regularly • 25+  terabytes  of  data  collected  each  day • RepresenEng  1000’s  of  cataloged  consumer  behaviors Data is used to develop content, consumer, category and campaign insights for our key content partners and large advertisers Sources: Mediamark Research, Spring 2004 and comScore Media Metrix, February 2005.Tuesday, November 27, 12
  49. 49. Yahoo!  Big  Data  –  A  league  of  its  own… GRAND CHALLENGE PROBLEMS OF DATA PROCESSING TRAVEL, CREDIT CARD PROCESSING, STOCK EXCHANGE, RETAIL, INTERNET Y! Data Challenge Exceeds others by 2 orders of magnitudeTuesday, November 27, 12
  50. 50. Behavioral  TargeEng  (BT)Tuesday, November 27, 12
  51. 51. Behavioral  TargeEng  (BT) Targe:ng  your  ads  to  consumers  whose   recent  behaviors  online  indicate  that   your  product  category  is  relevant  to  themTuesday, November 27, 12
  52. 52. Yahoo!  User  DNA • On a per consumer basis: maintain a behavioral/interests profile and profitability (user value and LTV) metricsTuesday, November 27, 12
  53. 53. How  it  works  |  Network  +  Interests  +  ModellingTuesday, November 27, 12
  54. 54. How  it  works  |  Network  +  Interests  +  Modelling Varying Product Purchase Cycles Analyze predictive patterns for purchase cycles in over 100 product categoriesTuesday, November 27, 12
  55. 55. How  it  works  |  Network  +  Interests  +  Modelling Match Users to the Models Analyze predictive patterns for purchase cycles in over 100 product categories In each category, build models to describe behaviour most likely to lead to an ad response (i.e. click).Tuesday, November 27, 12
  56. 56. How  it  works  |  Network  +  Interests  +  Modelling Rewarding Good Behaviour Analyze predictive patterns for purchase cycles in over 100 product categories In each category, build models to describe behaviour most likely to lead to an ad response (i.e. click). Score each user for fit with every category…daily.Tuesday, November 27, 12
  57. 57. How  it  works  |  Network  +  Interests  +  Modelling Identify Most Relevant Users Analyze predictive patterns for purchase cycles in over 100 product categories In each category, build models to describe behaviour most likely to lead to an ad response (i.e. click). Score each user for fit with every category…daily. Target ads to users who get highest ‘relevance’ scores in the targeting categoriesTuesday, November 27, 12
  58. 58. Recency  MaPers,  So  Does  Intensity Active now… …and with feelingTuesday, November 27, 12
  59. 59. DifferenEaEon  |  Category  specific  modellingTuesday, November 27, 12
  60. 60. DifferenEaEon  |  Category  specific  modellingTuesday, November 27, 12
  61. 61. DifferenEaEon  |  Category  specific  modellingTuesday, November 27, 12
  62. 62. DifferenEaEon  |  Category  specific  modellingTuesday, November 27, 12
  63. 63. Automobile  Purchase  Intender  Example • A  test  ad-­‐campaign  with  a  major  Euro  automobile  manufacturer – Designed  a  test  that  served  the  same  ad  creaEve  to  test  and  control  groups  on  Yahoo – Success  metric:  performing  specific  acEons  on  Jaguar  website • Test  results:  900%  conversion  lij  vs.  control  group – Purchase  Intenders  were  9  Emes  more  likely  to  configure  a  vehicle,  request  a  price   quote  or  locate  a  dealer  than  consumers  in  the  control  group – ~3x  higher  click  through  rates  vs.  control  groupTuesday, November 27, 12
  64. 64. Mortgage  Intender  Example Results from a client campaign on Yahoo! Network Example: Mortgages +626% Conv Lift We found: +122% CTR Lift 1,900,000 people looking Y! Finance Audience Mortgage Loans Target Mortgage Loans Target for mortgage loans. Example search terms qualified for this target: Mortgages Home Loans Refinancing Ditech Example Yahoo! Pages visited: Financing section in Real Estate Mortgage Loans area in Finance Real Estate section in Yellow Pages Source: Campaign Click thru Rate lift is determined by Yahoo! Internal research. Conversion is the number of qualified leads from clicks over number of impressions served. Audience size represents the audience within this behavioral interest category that has the highest propensity to engage with a brand or product and to click on an offer. Date: March 2006Tuesday, November 27, 12
  65. 65. Experience summary at Yahoo! • Dealing with the largest data source in the world (25 Terabyte per day) • BT business was grown from $20M to about $500M in 3 years of investment! • Building the largest database systems: – World’s largest Oracle data warehouse – World’s largest single DB – Over 300 data mart data – Analytics with thousands of KPI’s – Over 5000 users of reports – Largest targeting system in the world • Big demands for grid computing (Hadoop)Tuesday, November 27, 12
  66. 66. Social  Network Social  Network  MarkeEng Understanding  Context  for  AdsTuesday, November 27, 12
  67. 67. Case Study: TWITTER Social Marketing? Viacom’s VH1 Twitter campaign on ANVIL (the movie) (week of May 14th , 2009 – see AdAge article) Marketing ANVIL movie Very niche audience, how do you reach them?Tuesday, November 27, 12
  68. 68. Case Study: TWITTER Social Marketing? Viacom’s VH1 Twitter campaign on ANVIL (the movie) (week of May 14th , 2009 – see AdAge article) Marketing ANVIL movie Very niche audience, how do you reach them? Diffusion Give 20 free DVD’s to major related artists/groups, ask them To notify Twitter groups – reached over 2M peopleTuesday, November 27, 12
  69. 69. Case Study: TWITTER Social Marketing? Viacom’s VH1 Twitter campaign on ANVIL (the movie) (week of May 14th , 2009 – see AdAge article) Marketing ANVIL movie Very niche audience, how do you reach them? Diffusion Give 20 free DVD’s to major related artists/groups, ask them To notify Twitter groups – reached over 2M people Social Identity: power of word of mouth... What is the Cost to VH-1? Compare with traditional approach: TV commercials to promote a documentary film?Tuesday, November 27, 12
  70. 70. The  Display  Ads  Challenge  Today Damaging to Brand Irrelevant and Damaging to Brand Mismatch Completely IrrelevantTuesday, November 27, 12
  71. 71. NetSeer:  Intent  for  Display • And  Currently  Processing  4  Billion  Impressions  per  DayTuesday, November 27, 12
  72. 72. NetSeer:  Intent  for  Display • And  Currently  Processing  4  Billion  Impressions  per  DayTuesday, November 27, 12
  73. 73. NetSeer:  Intent  for  Display • And  Currently  Processing  4  Billion  Impressions  per  DayTuesday, November 27, 12
  74. 74. NetSeer:  Intent  for  Display • And  Currently  Processing  4  Billion  Impressions  per  DayTuesday, November 27, 12
  75. 75. Problem:  Hard  to  Understand  User  Intent Contextual  Ad  served  by  GoogleTuesday, November 27, 12
  76. 76. Problem:  Hard  to  Understand  User  Intent Contextual  Ad  served  by  Google What  NetSeer  Sees:  Tuesday, November 27, 12
  77. 77. Specialized  Search  through  Big  Data  AnalyEcs  over   the  Offers  UniverseTuesday, November 27, 12
  78. 78. Chaos  for  Consumers   ConsumerTuesday, November 27, 12
  79. 79. Chaos  for  Consumers   Deep Discount Sites Coupon Flash Sites Sale Sites Loyalty Programs Daily Deals Sites Consumer Online Loyalty Programs Social Search Networks EnginesTuesday, November 27, 12
  80. 80. The  Business  Model  behind  ChoozOn’s  Use  of  Big  DataTuesday, November 27, 12
  81. 81. The  Business  Model  behind  ChoozOn’s  Use  of  Big  Data Consumers • Value from brands they love • Tame the deal chaosTuesday, November 27, 12
  82. 82. The  Business  Model  behind  ChoozOn’s  Use  of  Big  Data Consumers • Value from brands they love • Tame the deal chaos Marketers • Reach targeted consumers • Build loyalty • Create brand evangelistsTuesday, November 27, 12
  83. 83. The  Business  Model  behind  ChoozOn’s  Use  of  Big  Data Consumers • Value from brands they love • Tame the deal chaos Marketers • Reach targeted consumers • Build loyalty • Create brand evangelistsTuesday, November 27, 12
  84. 84. Carol’s  Consumer  NetworkTuesday, November 27, 12
  85. 85. Carol’s  Consumer  Network “Chozen”  BrandsTuesday, November 27, 12
  86. 86. Carol’s  Consumer  Network “Chozen”  Brands Interests  (Intent)Tuesday, November 27, 12
  87. 87. Carol’s  Consumer  Network “Chozen”  Brands Interests  (Intent) Shopping  PalsTuesday, November 27, 12
  88. 88. Carol’s  Consumer  Network “Chozen”  Brands Interests  (Intent) Shopping  Pals Deal  ClubsTuesday, November 27, 12
  89. 89. What  Carol  Gets Carol’s  Consumer  NetworkTuesday, November 27, 12
  90. 90. What  Carol  Gets Carol’s  Consumer  Network The  Universe  of  Deals Affiliate  Deals Loyalty  Programs Daily  Deals Flash  Sales Deep  Discounters Intelligent Matching A  Personal  Shopper  for  DealsTuesday, November 27, 12
  91. 91. What  Carol  Gets Carol’s  Consumer  Network The  Universe  of  Deals Affiliate  Deals Loyalty  Programs 1,600+  brands Daily  Deals Flash  Sales 100,000+  offers Deep  Discounters Intelligent Matching A  Personal  Shopper  for  DealsTuesday, November 27, 12
  92. 92. What  Carol  Gets Carol’s  Consumer  Network The  Universe  of  Deals Affiliate  Deals Loyalty  Programs 1,600+  brands Daily  Deals Flash  Sales 100,000+  offers Deep  Discounters PLUS her Inbox™ Intelligent Matching A  Personal  Shopper  for  DealsTuesday, November 27, 12
  93. 93. Thank  You!   TwiPer:  @choozon nick@choozon.net www.ChoozOn.comTuesday, November 27, 12
  94. 94. Tuesday, November 27, 12

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