Data Driven Marketing - Advanced Analytics and Targeting

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The presentation discusses the impact of data driven targeting in marketing campaigns.

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Data Driven Marketing - Advanced Analytics and Targeting

  1. 1. [  Data  driven  marke.ng  ]   Reducing  waste  and  increasing   relevance  through  targe3ng  
  2. 2. [  Quick  company  history  ]  §  Datalicious  was  founded  in  2007  §  Strong  Omniture  web  analy3cs  history  §  1  of  4  global  Omniture  Preferred  Partners  §  Now  360  data  agency  with  specialist  team  §  Combina3on  of  analysts  and  developers  §  Evangelizing  smart  data  driven  marke3ng  §  Making  data  accessible  and  ac3onable  §  Driving  industry  best  prac3ce  (ADMA)  September  2010   ©  Datalicious  Pty  Ltd   2  
  3. 3. [  Wide  range  of  data  services  ]   Data   Insights   Ac.on   Pla=orms   Repor.ng   Applica.ons         Data  collec.on  and  processing   Data  mining  and  modelling   Data  usage  and  applica.on         Web  analy.cs  solu.ons   Customised  dashboards   Marke.ng  automa.on         Omniture,  Google  Analy.cs,  etc   Media  aMribu.on  models   Aprimo,  Trac.on,  Inxmail,  etc         Tag-­‐less  online  data  capture   Market  and  compe.tor  trends   Targe.ng  and  merchandising         End-­‐to-­‐end  data  pla=orms   Social  media  monitoring   Internal  search  op.misa.on         IVR  and  call  center  repor.ng   Online  surveys  and  polls   CRM  strategy  and  execu.on         Single  customer  view   Customer  profiling   Tes.ng  programs    September  2010   ©  Datalicious  Pty  Ltd   3  
  4. 4. [  Clients  across  all  industries  ]  September  2010   ©  Datalicious  Pty  Ltd   4  
  5. 5. [  Using  data  to  reduce  waste  ]   Media  aMribu.on   Op.mising  channel  mix   Targe.ng     Increasing  relevance   Tes.ng   Improving  usability   $$$  September  2010   ©  Datalicious  Pty  Ltd   5  
  6. 6. [  Increase  revenue  by  10-­‐20%  ]   By  coordina.ng  the  consumer’s  end-­‐to-­‐end  experience,   companies  could  enjoy  revenue  increases  of  10-­‐20%.   Google:  “get  more  value  from  digital  marke.ng”     or  hMp://bit.ly/cAtSUN  September  2010   ©  Datalicious  Pty  Ltd   6   Source:  McKinsey  Quarterly,  2010  
  7. 7. [  The  consumer  data  journey  ]   To  transac.onal  data   To  reten.on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages  September  2010   ©  Datalicious  Pty  Ltd   7  
  8. 8. [  Coordina.on  across  channels  ]       Genera.ng   Crea.ng   Maximising   awareness   engagement   revenue   TV,  radio,  print,   Retail  stores,  call   Outbound  calls,  direct   outdoor,  search   centers,  brochures,   mail,  emails,  SMS,  etc   marke3ng,  display   websites,  landing   ads,  performance   pages,  mobile  apps,   networks,  affiliates,   online  chat,  etc   social  media,  etc   Off-­‐site   On-­‐site   Profile     targe.ng   targe.ng   targe.ng  September  2010   ©  Datalicious  Pty  Ltd   8  
  9. 9. [  Combining  targe.ng  pla=orms  ]   Off-­‐site   targe3ng   Profile   On-­‐site   targe3ng   targe3ng  September  2010   ©  Datalicious  Pty  Ltd   9  
  10. 10. September  2010   ©  Datalicious  Pty  Ltd   10  
  11. 11. September  2010   ©  Datalicious  Pty  Ltd   11  
  12. 12. [  Combining  technology  ]   On-­‐site     Off-­‐site   segments   segments  September  2010   ©  Datalicious  Pty  Ltd   12  
  13. 13. [  Datalicious  SuperTag  ]   §  Central  JavaScript  based  container  tag   §  One  tag  for  all  pla^orms  incl.  Omniture   §  Either  hosted  internally  or  externally   §  Faster  tag  implementa3on  and  updates   §  Consistent  network  wide  re-­‐targe3ng   §  Transfer  or  profiling  data  between  sites   §  Iden3fica3on  of  exis3ng  customers   §  Re-­‐targe3ng  by  brand  preferences  September  2010   ©  Datalicious  Pty  Ltd   13  
  14. 14. [  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   14  
  15. 15. [  Behaviours  plus  transac.ons  ]   Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collec3on  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,  expira.on,  etc   tracking  of  external  campaign  responses   predic3ve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promo3on  responses   historical  data  from  previous  transac3ons   emails,  internal  search,  etc   average  order  value,  points,  etc   UPDATED  CONTINUOUSLY   UPDATED  OCCASIONALLY  September  2010   ©  Datalicious  Pty  Ltd   15  
  16. 16. [  Using  Pion  to  enrich  CRM  data  ]   §  Single  point  of  data   capture  and  processing   §  Real-­‐3me  queries  to   enrich  website  data     §  Mul3ple  data  export   op3ons  for  web  analy3cs   §  Enriching  single-­‐customer   view  website  behaviour  September  2010   ©  Datalicious  Pty  Ltd   16  
  17. 17. [  Overes.ma.ng  unique  visitors  ]  The  study  examined  data    from  two  of  the  UK’s  busiest    ecommerce  websites,  ASDA  and  William  Hill.    Given  that  more  than  half    of  all  page  impressions  on    these  sites  are  from  logged-­‐in    users,  they  provided  a  robust    sample  to  compare  IP-­‐based  and  cookie-­‐based  analysis  against.  The  results  were  staggering,  for  example  an  IP-­‐based  approach  overes3mated  visitors  by  up  to  7.6  3mes  whilst  a  cookie-­‐based  approach  overes.mated  visitors  by  up  to  2.3  .mes.    Google:  ”red  eye  cookie  report  pdf”  or  hMp://bit.ly/cszp2o       Source:  White  Paper,  RedEye,  2007  
  18. 18. Datalicious  SuperCookie   Persistent  Flash  cookie  that  cannot  be  deleted  September  2010   ©  Datalicious  Pty  Ltd   18  
  19. 19. [  Maximise  iden.fica.on  points  ]  160%  140%  120%  100%   80%   60%   −−−  Probability  of  iden3fica3on  through  Cookies   40%   20%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks  September  2010   ©  Datalicious  Pty  Ltd   19  
  20. 20. [  Sample  customer  level  data  ]  September  2010   ©  Datalicious  Pty  Ltd   20  
  21. 21. [  Sample  site  visitor  composi.on  ]   30%  new  visitors  with  no   30%  repeat  visitors  with   previous  website  history   referral  data  and  some   aside  from  campaign  or   website  history  allowing   referrer  data  of  which   50%  to  be  segmented  by   maybe  50%  is  useful   content  affinity   30%  exis.ng  customers  with  extensive   10%  serious   profile  including  transac3onal  history  of   prospects   which  maybe  50%  can  actually  be   with  limited   iden3fied  as  individuals     profile  data  September  2010   ©  Datalicious  Pty  Ltd   21  
  22. 22. [  Poten.al  home  page  layout  ]   Customise  content   Branded  header   delivery  on  the  fly   based  on  referrer   data,  past  content   Rule  based  offer   Login   consump3on  or   profile  data  for   exis3ng  customers.   Targeted   Targeted   offer   offer   Popular     links,     FAQs  September  2010   ©  Datalicious  Pty  Ltd   22  
  23. 23. [  Prospect  targe.ng  parameters  ]  September  2010   ©  Datalicious  Pty  Ltd   23  
  24. 24. [  Affinity  targe.ng  in  ac.on  ]   Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe3ng,     response  rates  are     liked  significantly     across  products.   CTR  By  Category  Affinity   Message   Postpay   Prepay   Broadb.   Business   Blackberry  Bold   - - - + Google:  “vodafone   5GB  Mobile  Broadband   - - + - omniture  case  study”     Blackberry  Storm   + - + + or  hMp://bit.ly/de70b7   12  Month  Caps   - + - +September  2010   ©  Datalicious  Pty  Ltd   24  
  25. 25. [  Poten.al  newsleMer  layout  ]   Using  profile  data   Rule  based  branded  header   enhanced  with   website  behaviour   Data  verifica.on   NPS   data  imported  into   the  email  delivery   pla^orm  to  build   Rule  based  offer   business  rules  and   Closest     stores,     customise  content   Profile  based  offer   delivery.   offers     etc  September  2010   ©  Datalicious  Pty  Ltd   25  
  26. 26. [  Customer  profiling  in  ac.on  ]   Using  website  and  email  responses   to  learn  a  lille  bite  more  about   customers  at  every  touch  point  in   order  to  keep  refining  customer   profiles  and  customising  future   communica3ons.  September  2010   ©  Datalicious  Pty  Ltd   26  
  27. 27. [  Poten.al  landing  page  layout  ]   Passing  data  on  user   Rule  based  branded  header   preferences  through   to  the  website  via   parameters  in  email   Campaign  message  match   click-­‐through  URLs     to  customise   content  delivery.   Targeted  offer   Call  to  ac.on  September  2010   ©  Datalicious  Pty  Ltd   27  
  28. 28. Exercise:  Targe.ng  matrix  September  2010   ©  Datalicious  Pty  Ltd   28  
  29. 29. [  Exercise:  Targe.ng  matrix  ]   Phase   Segment  A/B   Channels   Data  Points   Awareness   Considera.on   Purchase  Intent   Up/Cross-­‐Sell  September  2010   ©  Datalicious  Pty  Ltd   29  
  30. 30. [  Exercise:  Targe.ng  matrix  ]   Phase   Segment  A/B   Channels   Data  Points   Social,  display,   Awareness   Seen  this?   Default   search,  etc   Social,  search,   Download,   Considera.on   Great  feature!   website,  etc   product  view   Search,  site,   Cart  add,   Purchase  Intent   Great  value!   emails,  etc   checkout,  etc   Direct  mail,   Email  response,   Up/Cross-­‐Sell   Add  this!   emails,  etc   login,  etc  September  2010   ©  Datalicious  Pty  Ltd   30  
  31. 31. [  Quality  content  key  to  success  ]   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   31  
  32. 32. [  Small  changes  with  big  impact  ]  September  2010   ©  Datalicious  Pty  Ltd   32  
  33. 33. [  Bad  campaign  worse  than  none  ]  September  2010   ©  Datalicious  Pty  Ltd   33  
  34. 34. [  Keys  to  effec.ve  targe.ng  ]   1.  Define  success  metrics   2.  Define  and  validate  segments   3.  Develop  targe3ng  and  message  matrix     4.  Transform  matrix  into  business  rules   5.  Develop  and  test  content   6.  Start  targe3ng  and  automate   7.  Keep  tes3ng  and  refining   8.  Communicate  results  September  2010   ©  Datalicious  Pty  Ltd   34  
  35. 35. ADMA  short  course   “Analyse  to  op.mise”     In  Melbourne  &  Sydney   October/November   By  Datalicious  September  2010   ©  Datalicious  Pty  Ltd   35  
  36. 36. Email  me   cbartens@datalicious.com     Follow  us   twiMer.com/datalicious     Learn  more   blog.datalicious.com    September  2010   ©  Datalicious  Pty  Ltd   36  

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