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Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
Westfield Shopper Data
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Westfield Shopper Data

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The presentation illustrates Westfield consumer data journey.

The presentation illustrates Westfield consumer data journey.

Published in: Technology
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  • 1. [  Wes&ield  shopper  data  ]   From  email  database  to  core  asset,     the  brains  of  the  virtual  mall  
  • 2. [  Increase  revenue  by  10-­‐20%  ]   Capture  internet  traffic   Capture  50-­‐100%  of  fair  market  share  of  traffic   Increase  consumer  engagement   Exceed  50%  of  best  compe@tor’s  engagement  rate     Capture  qualified  leads  and  sell   Convert  10-­‐15%  to  leads  and  of  that  20%  into   sales   Building  consumer  loyalty   Build  60%  loyalty  rate  and  40%  sales  conversion   Increase  online  revenue   Earn  10-­‐20%  incremental  revenue  online  September  2010   ©  Datalicious  Pty  Ltd   2  
  • 3. [  The  consumer  data  journey  ]   To  transacFonal  data   To  retenFon  messages   Individual  data     Wes&ield  data     From  suspect  to   prospect   To  customer   Time   Time   Anonymous  data   3rd  party  data     From  behavioural  data   From  awareness  messages  September  2010   ©  Datalicious  Pty  Ltd   3  
  • 4. [  CoordinaFon  across  channels  ]       GeneraFng   CreaFng   Maximising   awareness   engagement   revenue   TV,  radio,  print,   Retail  stores,  in-­‐store   Outbound  calls,  direct   outdoor,  search   kiosks,  call  centers,   mail,  emails,  social   marke@ng,  display   brochures,  websites,   media,  SMS,  mobile   ads,  performance   mobile  apps,  online   apps,  etc   networks,  affiliates,   chat,  social  media,  etc   social  media,  etc   Off-­‐site   On-­‐site   Profile     targeFng   targeFng   targeFng  September  2010   ©  Datalicious  Pty  Ltd   4  
  • 5. [  Combining  targeFng  pla&orms  ]   Off-­‐site   targe@ng   Profile   On-­‐site   targe@ng   targe@ng  September  2010   ©  Datalicious  Pty  Ltd   5  
  • 6. hPp://ww.wes&ield.com?data=zimbio,promoFon   September  2010   ©  Datalicious  Pty  Ltd   6  
  • 7. cookie:  zimbio,  promoFon,  chrisFne,  fashion  September  2010   ©  Datalicious  Pty  Ltd   7  
  • 8. hPp://ww.wes&ield.com?data=chrisFne,promoFon   September  2010   ©  Datalicious  Pty  Ltd   8  
  • 9. [  Extended  targeFng  pla&orm  ]   Publishers   Partners   Network   Brand  September  2010   ©  Datalicious  Pty  Ltd   9  
  • 10. September  2010   ©  Datalicious  Pty  Ltd   10  
  • 11. September  2010   ©  Datalicious  Pty  Ltd   11  
  • 12. [  Combining  data  sets  ]   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  September  2010   ©  Datalicious  Pty  Ltd   12  
  • 13. [  Combining  Wes&ield  data  sets  ]   Wes&ield  profiles   Social  profiles/comments   Combine     into  single   Survey  responses   database   Social  sharing/likes   for  analysis,   modelling     Campaign  responses   and  to  ID   Reviews/raFngs   targe@ng   variables     Website  behaviour   most  likely     Geo-­‐demographics   to  influence   behaviour   Wes&ield  transacFons   3rd  party  segmentaFon  September  2010   ©  Datalicious  Pty  Ltd   13  
  • 14. [  Behaviours  plus  transacFons  ]   Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collec@on  of  demographical  data     +   browsing,  checkout,  etc   age,  gender,  address,  etc   tracking  of  content  preferences   customer  lifecycle  metrics  and  key  dates   products,  brands,  features,  etc   profitability,  expiraFon,  etc   tracking  of  external  campaign  responses   predic@ve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promo@on  responses   historical  data  from  previous  transac@ons   emails,  internal  search,  etc   average  order  value,  points,  etc   Updated  ConFnuously   Updated  Occasionally  September  2010   ©  Datalicious  Pty  Ltd   14  
  • 15. [  Sample  customer  level  data  ]  September  2010   ©  Datalicious  Pty  Ltd   15  
  • 16. [  Maximise  idenFficaFon  points  ]  160%  140%  120%  100%   80%   60%   −−−  Probability  of  iden@fica@on  through  Cookies   40%   20%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks  September  2010   ©  Datalicious  Pty  Ltd   16  
  • 17. September  2010   ©  Datalicious  Pty  Ltd   17  
  • 18. September  2010   ©  Datalicious  Pty  Ltd   18  
  • 19. September  2010   ©  Datalicious  Pty  Ltd   19  
  • 20. September  2010   ©  Datalicious  Pty  Ltd   20  
  • 21. hPp://www.wes&ield.com?data=digitalforum  September  2010   ©  Datalicious  Pty  Ltd   21  
  • 22. [  Profiling  at  every  touch  point  ]   Using  website  and  email  responses   to  learn  a  li]le  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  September  2010   ©  Datalicious  Pty  Ltd   22  
  • 23. [  Social  media  as  data  source  ]   Facebook  Connect  gives  your   company  the  following  data   and  more  with  just  one  click     Email  address,  first  name,  last  name,   gender,  birthday,  interests,  picture,   affilia@ons,  last  profile  update,  @me  zone,   religion,  poli@cal  interests,  a]racted  to   which  sex,  why  they  want  to  meet   someone,  home  town,  rela@onship   status,  current  loca@on,  ac@vi@es,  music   interests,  tv  show  interests,  educa@on   history,  work  history,  family,  etc   Need  anything  else?  September  2010   ©  Datalicious  Pty  Ltd   23  
  • 24. September  2010   ©  Datalicious  Pty  Ltd   24  
  • 25. September  2010   ©  Datalicious  Pty  Ltd   25  
  • 26. Data  from  September  2010   ©  Datalicious  Pty  Ltd   26  
  • 27. [  Overall  volume  and  influence  ]   Data  from  September  2010   ©  Datalicious  Pty  Ltd   27  
  • 28. [  Influence  and  media  value  ]   US   Data  from   UK   AU/NZ  September  2010   ©  Datalicious  Pty  Ltd   28  
  • 29. Appending  social  data  to  customer  profiles   Name,  age,  gender,  occupaFon,  locaFon,  social     profiles  and  influencer  ranking  based  on  email   (influencers  only)   (all  contacts)  September  2010   ©  Datalicious  Pty  Ltd   29  
  • 30. [  Social  media  data  potenFal  ]  §  Large  Australian  consumer  brand  §  20%  of  customer  emails  had  social  profiles  §  Each  profile  had  an  average  of  8  friends  §  2%  of  profiles  had  an  influencer  score  §  0.5%  of  social  had  a  score  of  over  10  §  For  a  database  of  500,000  that  would  mean  §  Poten@al  addi@onal  reach  of  100,000  friends  §  Includes  2,500  influen@al  individuals  September  2010   ©  Datalicious  Pty  Ltd   30  
  • 31. September  2010   ©  Datalicious  Pty  Ltd   31  
  • 32. September  2010   ©  Datalicious  Pty  Ltd   32  
  • 33. September  2010   ©  Datalicious  Pty  Ltd   33  
  • 34. [  MulFple  stores  with  sales  data  ]   One  backend     with  mulFple     store  fronts    September  2010   ©  Datalicious  Pty  Ltd   34  
  • 35. [  UK  Wes&ield  online  audience  ]  September  2010   ©  Datalicious  Pty  Ltd   35  
  • 36. [  US  Wes&ield  online  audience  ]  September  2010   ©  Datalicious  Pty  Ltd   36  
  • 37. [  Track  offline  sales  driven  by  online  ]   AdverFsing     Phone   Credit  check,   campaign   order   fulfilment   Retail   ConfirmaFon   order   email   Website   Online   Online  order   Virtual  order   research   order   confirmaFon   confirmaFon   Cookie  August  2010   ©  Datalicious  Pty  Ltd   37  
  • 38. September  2010   ©  Datalicious  Pty  Ltd   38  
  • 39. September  2010   ©  Datalicious  Pty  Ltd   39  
  • 40. [  Developing  a  targeFng  matrix  ]   Phase   Fashion   Channels   Data  Points   Awareness   ConsideraFon   Purchase  Intent   Up/Cross-­‐Sell  September  2010   ©  Datalicious  Pty  Ltd   40  
  • 41. [  Developing  a  targeFng  matrix  ]   Phase   Fashion   Channels   Data  Points   Social,  display,   Awareness   Seen  this?   Default   search,  etc   Social,  search,   Download,   ConsideraFon   Great  feature!   website,  etc   product  view   Search,  site,   Cart  add,   Purchase  Intent   Great  value!   email,  etc   checkout,  etc   Mail,  mobile,   Email  click,   Up/Cross-­‐Sell   Add  this!   email,  etc   login,  etc  September  2010   ©  Datalicious  Pty  Ltd   41  
  • 42. [  But  quality  content  is  sFll  key  ]   Avinash  Kaushik:     “The  principle  of  garbage  in,  garbage  out   applies  here.  […]  what  makes  a  behaviour   targe<ng  pla=orm  <ck,  and  produce  results,  is   not  its  intelligence,  it  is  your  ability  to  actually   feed  it  the  right  content  which  it  can  then  target   […].  You  feed  your  BT  system  crap  and  it  will   quickly  and  efficiently  target  crap  to  your   customers.  Faster  then  you  could     ever  have  yourself.”  September  2010   ©  Datalicious  Pty  Ltd   42  
  • 43. [  ImplicaFons  for  Wes&ield  ]  §  Collect  data  to  drive  value  for  customers   –  Not  just  for  the  sake  of  collec@ng  data  §  Use  data  to  coordinate  customer  experience   –  Mul@ple  data  sources  and  targe@ng  plaiorms  §  Iden@fy  customers  wherever  possible   –  Be  crea@ve  about  real  world  transac@on  data  §  KISS  principle  applies:  Keep  it  simple  stupid  September  2010   ©  Datalicious  Pty  Ltd   43  
  • 44. Email  us   cbartens@datalicious.com     Follow  us   twiPer.com/datalicious     Learn  more   blog.datalicious.com    September  2010   ©  Datalicious  Pty  Ltd   44  

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