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THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME
 

THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME

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Presentation from Nick Weir, ChoozON

Presentation from Nick Weir, ChoozON
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More at http://event.gigaom.com/structuredata/

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  • Great session Nick. Pleased to see the industry finally adopting the '3V's' framework for Big Data that Gartner introduced over 11 years ago. For future reference and proper (professional) attribution, here's a link to that original research note I published in 2001: http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/. --Doug Laney, VP Research, Gartner, @doug_laney
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    THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME Presentation Transcript

    • THE 3V’S OF BIG DATA: VARIETY, VELOCITand VOLUME SPEAKER: Nick Weir CEO ChoozONFriday, July 27, 2012
    • 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,  NYFriday, July 27, 2012
    • Outline • Big  Data  all  around  us • The  role  of  Data  Mining  and  Predic6ve   Analy6cs • On-­‐line  data  and  facts • Case  studies  from  mul6ple  ver6cals: 1. Yahoo!  Big  Data 2. Social  Network  Data 3. ChoozOn  Big  Data  from  offersFriday, July 27, 2012
    • What  MaAers  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  acFons 2.  Prolifera:ng  analy:cs  throughout  your  organiza:on • make  every  part  of  your  business  smarter 3.  Driving  significant  business  value   • embedding  analyFcs  into  every  area  of  your  business   can  help  you  drive  top  line  revenues  and/or  boJom  line   cost  efficiencies  Friday, July 27, 2012
    • Why  Big  Data?Friday, July 27, 2012
    • Why  Big  Data? A  new  term,  with  associated  “Data  Scien:st”  posi:ons:Friday, July 27, 2012
    • 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  tradi:onal  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  opera:onsFriday, July 27, 2012
    • 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  tradi:onal  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  opera:ons • Above  leads  to  “messy”  situa6ons  with  no  standard   recipes  or  architecture:  hence  the  need  for  “data   scien6sts”   –Conduct  “Data  Expedi:ons”  Friday, July 27, 2012
    • What  Makes  Data  “Big  Data”?Friday, July 27, 2012
    • 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 • MulQple  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  Friday, July 27, 2012
    • The  DisQncQon  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  descrip:ons  and  news,  blogs,  etc… – User  and  customer  commentary – Reac:ons  on  social  media:  e.g.  TwiTer  is  a  mix  of  data  anyway • Using  standard  transforms,  enQty  extracQon,  and  new   generaQon  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  compuQngFriday, July 27, 2012
    • 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  operaQons  with  each  being  a  simple  operaQon  but  Friday, July 27, 2012
    • To  Hadoop  or  not  to  Hadoop? When  to  use  techniques  requiring  Map-­‐Reduce  and  grid  compu?ng? • Typically  organizaFons  try  to  use  Map-­‐Reduce  for  everything  to  do  with   Big  Data – Inefficient  and  oIen  irra6onal – Certain  opera6ons  require  specialized  storage • UpdaFng  segment  memberships  over  large  numbers  of  users • Defining  new  segments  on  user  or  usage  data • Map-­‐Reduce  is  useful  when  a  very  simple  operaFon  is  to  be  applied  on   a  large  body  of  unstructured  data – Typically  this  is  during  en6ty  and  aAribute  extrac6on – S6ll  need  Big  Data  analysis  post-­‐Hadoop • Map-­‐Reduce  is  not  efficient  or  effecFve  for  tasks  involving  deeper   staFsFcal  modeling –  Good  for  gathering  counts  and  simple  (sufficient)  sta6s6cs • E.g.  how  many  Fmes  a  keyword  occurs,  quick  aggregaFon  of  simple  facts  in  unstructured   data,  esFmates  of  variances,  density,  etc… – Mostly  pre-­‐processing  for  Data  MiningFriday, July 27, 2012
    • Hadoop  Use  Cases  by  Data  TypeFriday, July 27, 2012
    • Friday, July 27, 2012
    • Friday, July 27, 2012
    • Big  Data  Applica:ons  and  Uses 100%  Capture IT  Log  &  Security   Forensics  &  AnalyFcs Find  New  Signal Predict  Events Product  Planning Automated  Device  Data Failure  Analysis AnalyFcs ProacFve  Fixes RecommendaQon AdverFsing SegmentaQon AnalyFcs Social  Media Hadoop  +    MPP  +  EDW Big  Data Cost  ReducQon Ad  Hoc  Insight Warehouse  AnalyFcs PredicQve  AnalyQcsFriday, July 27, 2012
    • So  this  Internet  thing  is  going  to   be  big! Big  Opportunity,  Big  Data,  Big   Challenges!Friday, July 27, 2012
    • Stats  about  on-­‐line  usageFriday, July 27, 2012
    • Stats  about  on-­‐line  usage • How  many  people  are  on-­‐line  today?   –2.1  Billion  (per  Comscore  es:mates) –30%  of  world  Popula:onFriday, July 27, 2012
    • Stats  about  on-­‐line  usage • How  many  people  are  on-­‐line  today?   –2.1  Billion  (per  Comscore  es:mates) –30%  of  world  Popula:on • How  much  Fme  is  spent  on-­‐line  per  month  by  the   whole  world? –4M  person-­‐years  per  monthFriday, July 27, 2012
    • Stats  about  on-­‐line  usage • How  many  people  are  on-­‐line  today?   –2.1  Billion  (per  Comscore  es:mates) –30%  of  world  Popula:on • How  much  Fme  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)Friday, July 27, 2012
    • Stats  about  on-­‐line  usage • How  many  people  are  on-­‐line  today?   –2.1  Billion  (per  Comscore  es:mates) –30%  of  world  Popula:on • How  much  Fme  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.orgFriday, July 27, 2012
    • Volume,  Velocity,  VarietyFriday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day?Friday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  BillionFriday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day?Friday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250MFriday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day?Friday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day? – More  than  800MFriday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day? – More  than  800M • YouTube:  Views/dayFriday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$er:  How  many  Tweets/day? – More  than  250M • Facebook:  Updates  per  day? – More  than  800M • YouTube:  Views/day – 4  Billion  views – 60  hours  of  video  uploaded  every  minute!Friday, July 27, 2012
    • Volume,  Velocity,  Variety • Google:    How  many  queries  per  day? – More  than  1  Billion • Twi$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%Friday, July 27, 2012
    • 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.orgFriday, July 27, 2012
    • Turning  the  three  Vs  of  Big  Data  into   ValueFriday, July 27, 2012
    • Turning  the  three  Vs  of  Big  Data  into   Value Do  we  understand  what  each  individual  is  trying  to   achieve?Friday, July 27, 2012
    • Turning  the  three  Vs  of  Big  Data  into   Value Do  we  understand  what  each  individual  is  trying  to   achieve? • What  is  user  intent?Friday, July 27, 2012
    • 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…Friday, July 27, 2012
    • 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?Friday, July 27, 2012
    • 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  emoFon?Friday, July 27, 2012
    • 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  emoFon? •  Is  it  negaFve  or  posiFve?Friday, July 27, 2012
    • 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  emoFon? •  Is  it  negaFve  or  posiFve? •  What  is  the  health  of  my  brand  online?Friday, July 27, 2012
    • 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  emoFon? •  Is  it  negaFve  or  posiFve? •  What  is  the  health  of  my  brand  online? Do  we  understand  context  and  content?Friday, July 27, 2012
    • 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  emoFon? •  Is  it  negaFve  or  posiFve? •  What  is  the  health  of  my  brand  online? Do  we  understand  context  and  content? •   What  are  appropriate  ads?Friday, July 27, 2012
    • 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  emoFon? •  Is  it  negaFve  or  posiFve? •  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?Friday, July 27, 2012
    • 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  emoFon? •  Is  it  negaFve  or  posiFve? •  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?,  informaFve?Friday, July 27, 2012
    • Case  Studies Yahoo!  Big  Data 18Friday, July 27, 2012
    • Yahoo!  –  One  of  Largest  Des:na:ons   on  the  Web More people visited Yahoo! in the past month than: 80%  of  the  U.S.  Internet  popula4on  uses  Yahoo!   • Use coupons –  Over  600  million  users  per  month  globally! • Vote • Global  network  of  content,  commerce,  media,  search  and  access   • Recycle products • Exercise regularly • 100+  properFes  including  mail,  TV,  news,  shopping,  finance,  autos,   • Have children travel,  games,  movies,  health,  etc. living at home • Wear sunscreen • 25+  terabytes  of  data  collected  each  day regularly • Represen:ng  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.Friday, July 27, 2012
    • 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 magnitudeFriday, July 27, 2012
    • Behavioral  Targe:ng  (BT)Friday, July 27, 2012
    • Behavioral  Targe:ng  (BT) Targe:ng  your  ads  to  consumers   whose  recent  behaviors  online   indicate  that  your  product   category  is  relevant  to  themFriday, July 27, 2012
    • Yahoo!  User  DNA • On a per consumer basis: maintain a behavioral/interests profile and profitability (user value and LTV) metricsFriday, July 27, 2012
    • How  it  works  |  Network  +  Interests  +  ModellingFriday, July 27, 2012
    • How  it  works  |  Network  +  Interests  +  Modelling Analyze predictive patterns for purchase cycles in over 100 product categories Varying Product Purchase CyclesFriday, July 27, 2012
    • How  it  works  |  Network  +  Interests  +  Modelling Analyze predictive patterns for purchase cycles in over 100 product categories Match Users to the Models In each category, build models to describe behaviour most likely to lead to an ad response (i.e. click).Friday, July 27, 2012
    • How  it  works  |  Network  +  Interests  +  Modelling Analyze predictive patterns for purchase cycles in over 100 product categories Rewarding Good Behaviour 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.Friday, July 27, 2012
    • How  it  works  |  Network  +  Interests  +  Modelling Analyze predictive patterns for purchase cycles in over 100 product categories Identify Most Relevant Users 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 categoriesFriday, July 27, 2012
    • Recency  MaTers,  So  Does  Intensity Active now… …and with feelingFriday, July 27, 2012
    • DifferenQaQon  |  Category  specific  modellingFriday, July 27, 2012
    • DifferenQaQon  |  Category  specific  modellingFriday, July 27, 2012
    • DifferenQaQon  |  Category  specific  modellingFriday, July 27, 2012
    • DifferenQaQon  |  Category  specific  modellingFriday, July 27, 2012
    • Automobile  Purchase  Intender  Example • A  test  ad-­‐campaign  with  a  major  Euro  automobile   manufacturer – Designed  a  test  that  served  the  same  ad  creaQve  to  test  and   control  groups  on  Yahoo – Success  metric:  performing  specific  acQons  on  Jaguar  website • Test  results:  900%  conversion  lij  vs.  control  group – Purchase  Intenders  were  9  Qmes  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  groupFriday, July 27, 2012
    • Mortgage  Intender  Example Results from a client campaign on Yahoo! Example: Mortgages Network +626% Conv Lift We found: +122% CTR Lift 1,900,000 people Y! Finance Audience Mortgage Loans Target Mortgage Loans Target looking 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 Date: March 2006 with a brand or product and to click on an offer.Friday, July 27, 2012
    • 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)Friday, July 27, 2012
    • Social  Network Social  Network  Marke:ng Understanding  Context  for   AdsFriday, July 27, 2012
    • 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?Friday, July 27, 2012
    • 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 peopleFriday, July 27, 2012
    • 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?Friday, July 27, 2012
    • The  Display  Ads  Challenge  Today Damaging to Brand Irrelevant and Damaging to Brand Mismatch Completely IrrelevantFriday, July 27, 2012
    • NetSeer:  Intent  for  Display • And  Currently  Processing  4  Billion  Impressions  per   DayFriday, July 27, 2012
    • NetSeer:  Intent  for  Display • And  Currently  Processing  4  Billion  Impressions  per   DayFriday, July 27, 2012
    • NetSeer:  Intent  for  Display • And  Currently  Processing  4  Billion  Impressions  per   DayFriday, July 27, 2012
    • NetSeer:  Intent  for  Display • And  Currently  Processing  4  Billion  Impressions  per   DayFriday, July 27, 2012
    • Problem:  Hard  to  Understand  User  Intent Contextual  Ad  served  by  GoogleFriday, July 27, 2012
    • Problem:  Hard  to  Understand  User  Intent Contextual  Ad  served  by  Google What  NetSeer  Sees:  Friday, July 27, 2012
    • Specialized  Search  through  Big  Data   AnalyFcs  over  the  Offers  UniverseFriday, July 27, 2012
    • Chaos  for   Consumers   ConsumerFriday, July 27, 2012
    • Chaos  for   Consumers   Deep Discount Sites Coupon Flash Sites Sale Sites Loyalty Program s Daily Deals Sites Consumer Online Loyalty Program Social s Search Network Engines sFriday, July 27, 2012
    • The  Business  Model  behind  ChoozOn’s  Use  of  Big   DataFriday, July 27, 2012
    • The  Business  Model  behind  ChoozOn’s  Use  of  Big   Data Consumers • Value from brands they love • Tame the deal chaosFriday, July 27, 2012
    • 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 evangelistsFriday, July 27, 2012
    • 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 evangelistsFriday, July 27, 2012
    • Carol’s  Consumer  NetworkFriday, July 27, 2012
    • Carol’s  Consumer  Network “Chozen”  BrandsFriday, July 27, 2012
    • Carol’s  Consumer  Network “Chozen”  Brands Interests  (Intent)Friday, July 27, 2012
    • Carol’s  Consumer  Network “Chozen”  Brands Interests  (Intent) Shopping  PalsFriday, July 27, 2012
    • Carol’s  Consumer  Network “Chozen”  Brands Interests  (Intent) Shopping  Pals Deal  ClubsFriday, July 27, 2012
    • What  Carol  Gets Carol’s  Consumer  NetworkFriday, July 27, 2012
    • What  Carol  Gets Carol’s  Consumer  Network The  Universe  of  Deals Affiliate  Deals Loyalty  Programs Daily  Deals Flash  Sales Intelligent Matching A  Personal  Shopper  for  DealsFriday, July 27, 2012
    • What  Carol  Gets Carol’s  Consumer  Network The  Universe  of  Deals Affiliate  Deals 1,600+  brands Loyalty  Programs 100,000+  offers Daily  Deals Flash  Sales Intelligent Matching A  Personal  Shopper  for  DealsFriday, July 27, 2012
    • What  Carol  Gets Carol’s  Consumer  Network The  Universe  of  Deals Affiliate  Deals 1,600+  brands Loyalty  Programs 100,000+  offers Daily  Deals Flash  Sales PLUS her Inbox™ Intelligent Matching A  Personal  Shopper  for  DealsFriday, July 27, 2012
    • Thank  You!   TwiAer:  @choozon nick@choozon.net www.ChoozOn.comFriday, July 27, 2012
    • Friday, July 27, 2012