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Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
Digital Measurement - How to Turn Data into Actionable Insights
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Digital Measurement - How to Turn Data into Actionable Insights

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The presentation discusses the concepts and principles of digital measurement in tracking and measuring marketing performance.

The presentation discusses the concepts and principles of digital measurement in tracking and measuring marketing performance.

Published in: Technology
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  • 1. [  Digital  Measurement  ]   Analy&cs  workshop  on  how  to  turn   data  into  ac&onable  insights  
  • 2. [  Company  history  ]  §  Datalicious  was  founded  in  2007  §  Strong  Omniture  web  analy&cs  history  §  One-­‐stop  data  agency  with  specialist  team  §  Combina&on  of  analysts  and  developers  §  Making  data  accessible  and  ac&onable  §  Driving  industry  best  prac&ce  §  Evangelizing  use  of  data  June  2010   ©  Datalicious  Pty  Ltd   2  
  • 3. [  Challenging  clients  ]  June  2010   ©  Datalicious  Pty  Ltd   3  
  • 4. [  Data  driven  marke:ng  ]     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  aKribu:on  models   Aprimo,  Trac:on,  Inxmail,  etc         Tagless  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    June  2010   ©  Datalicious  Pty  Ltd   4  
  • 5. [  Today  ]  §  Capturing  data   –  Op&ons,  limita&ons,  innova&ons  §  Genera&ng  insights   –  Process,  metrics,  examples  §  Taking  ac&on   –  Media,  targe&ng,  tes&ng  June  2010   ©  Datalicious  Pty  Ltd   5  
  • 6. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Capturing  data  ]  June  2010   ©  Datalicious  Pty  Ltd   6  
  • 7. [  Digital  data  is  cheap  ]  June  2010   ©  Datalicious  Pty  Ltd   7   Source:  Omniture  Summit,  MaS  Belkin,  2007  
  • 8. [  Digital  data  op:ons  ]   +Social  June  2010   ©  Datalicious  Pty  Ltd   8   Source:  Accuracy  Whitepaper  for  web  analy&cs,  Brian  CliWon,  2008  
  • 9. [  On-­‐site  analy:cs  tools  ]   Google:     ”forrester  wave     web  analy:cs  pdf”     or     hKp://bit.ly/aTLAKT  June  2010   ©  Datalicious  Pty  Ltd   9   Source:  Forrester  Wave  Web  Analy&cs,  2009  
  • 10. [  What  pla<orm  to  use  ]   Stage  1:  Data   Stage  2:  Insights   Stage  3:  Ac:on   Data  is  fully  owned       Sophis&ca&on in-­‐house,  advanced   Data  is  being  brought     predic&ve  modelling   in-­‐house,  shiW  towards   and  trigger  based   Third  par&es  control   insights  genera&on  and   marke&ng,  i.e.  what     data  mining,  i.e.  why   will  happen  and     most  data,  ad  hoc   did  it  happen?   making  it  happen!   repor&ng  only,  i.e.     what  happened?   Time,  Control  June  2010   ©  Datalicious  Pty  Ltd   10  
  • 11. [  Governance  and  data  integrity  ]  June  2010   ©  Datalicious  Pty  Ltd   11   Source:  Omniture  Summit,  MaS  Belkin,  2007  
  • 12. [  Free  off-­‐site  analy:cs  tools  ]  §  hSp://www.google.com/trends    §  hSp://www.google.com/sktool  §  hSp://www.google.com/insights/search  §  hSp://www.google.com/webmasters  §  hSp://www.google.com/adplanner  §  hSp://www.google.com/videotarge&ng  §  hSp://www.keywordspy.com    §  hSp://www.compete.com  §  hSp://www.alexa.com    §  hSp://wiki.kenburbary.com    June  2010   ©  Datalicious  Pty  Ltd   12  
  • 13. [  Search  at  all  stages  ]   In  Australia  Google  has  a  market  share     of  almost  90%  of  all  searches,  making     it  a  very  large  and  reliable  data  sample  June  2010   ©  Datalicious  Pty  Ltd   13   Source:  Inside  the  Mind  of  the  Searcher,  Enquiro  2004  
  • 14. [  Search  call  to  ac:on  for  offline  ]  June  2010   ©  Datalicious  Pty  Ltd   14  
  • 15. [  Client  side  tracking  process  ]   What  if:  Someone  deletes  their  cookies?  Or  uses  a  device   that  does  not  support  JavaScript?  Or  uses  two  computers   (work  vs.  home)?  Or  two  people  use  the  same  computer?  June  2010   ©  Datalicious  Pty  Ltd   15   Source:  Google  Analy&cs,  Jus&n  Cutroni,  2007  
  • 16. [  Tag-­‐less  data  capture  ]   Google:  “atomic  labs”       www.atomiclabs.com  June  2010   ©  Datalicious  Pty  Ltd   16  
  • 17. [  Overes:ma:on  of  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  overes&mated  visitors  by  up  to  7.6  &mes  whilst  a  cookie-­‐based  approach  overes:mated  visitors  by  up  to  2.3  :mes.    Google:  ”red  eye  cookie  report  pdf”  or  hKp://bit.ly/cszp2o       2010  June   ©  Datalicious  Pty  Ltd   17   Source:  White  Paper,  RedEye,  2007  
  • 18. [  Maximise  iden:fica:on  points  ]   Probability  of  iden&fica&on  through  cookie  140%  120%  100%   80%   60%   40%   20%   0%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks  June  2010   ©  Datalicious  Pty  Ltd   18  
  • 19. Datalicious  SuperCookie   Persistent  Flash  cookie  that  cannot  be  deleted  June  2010   ©  Datalicious  Pty  Ltd   19  
  • 20. [  Mobile  page  headers  ]   MSISDN  =  Mobile  Number  June  2010   ©  Datalicious  Pty  Ltd   20   Source:  Mobile  Tracking,  Omniture,  2008  
  • 21. [  Single-­‐sign  on  ]   Facebook  Connect  gives  your   company  the  following  data   and  more  with  just  one  click!     ID,  first  name,  last  name,  middle  name,   picture,  affilia&ons,  last  profile  update,   &me  zone,  religion,  poli&cal  interests,   interests,  sex,  birthday,  aSracted  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  and  email     Need  anything  else?  June  2010   ©  Datalicious  Pty  Ltd   21  
  • 22. [  Research  online,  shop  offline  ]  Google:  ”digital  future  report  2009  pdf”  or  hKp://bit.ly/ZkLvr  June  2010   ©  Datalicious  Pty  Ltd   22   Source:  2008  Digital  Future  Report,  Surveying  The  Digital  Future,  Year  Seven,  USC  Annenberg  School  
  • 23. [  Offline  sales  driven  by  online  ]   Tying  offline  conversions  back  to  online  campaign  and  research  behavior  using   standard  cookie  technology  by  triggering  virtual  online  order  confirma&on   pages  for  offline  sales  using  email  receipts.   Website.com   Phone   Virtual  Order   Research   Orders   Credit  Check   Fulfilment   @   Confirma:on   Adver:sing     Website.com   Retail   Virtual  Order   Campaign   Research   Orders   Credit  Check   Fulfilment   @   Confirma:on   Website.com   Online   Online  Order   Virtual  Order   Research   Orders   Confirma:on   Credit  Check   Fulfilment   @   Confirma:on   Cookie   Cookie   Cookie  June  2010   ©  Datalicious  Pty  Ltd   23  
  • 24. [  Summary:  Capturing  data  ]  §  Plenty  of  data  sources  and  plajorms  §  Especially  search  is  great  free  data  source  §  Maintaining  data  integrity  takes  effort  §  Cookie  technology  has  its  limita&ons  §  New  tag-­‐less  technologies  emerging  §  Maximise  iden&fica&on  points  §  Offline  can  be  &ed  to  online  June  2010   ©  Datalicious  Pty  Ltd   24  
  • 25. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Genera:ng  insights  ]  June  2010   ©  Datalicious  Pty  Ltd   25  
  • 26. [  Corporate  data  journey  ]   Stage  1   Stage  2     Stage  3 Data   Insights   Ac:on   Data  is  fully  owned       Sophis&ca&on in-­‐house,  advanced   Data  is  being  brought     predic&ve  modelling   in-­‐house,  shiW  towards   and  trigger  based   Third  par&es  control   insights  genera&on  and   marke&ng,  i.e.  what     data  mining,  i.e.  why   will  happen  and     most  data,  ad  hoc   did  it  happen?   making  it  happen!   repor&ng  only,  i.e.     what  happened?   Time,  Control  June  2010   ©  Datalicious  Pty  Ltd   26  
  • 27. [  The  ideal  analyst  ]  §  Business  minded   –  Semng  realis&c  improvement  goals  §  Technically  savvy   –  Bridging  gap  between  business  and  IT  §  Strong  sales  skills   –  Raising  awareness  for  the  value  of  data  §  Seniority  and  experience   –  Needs  to  be  taken  serious  across  organisa&on  §  Posi&on  within  hierarchy   –  Able  to  analyse  without  loyalty  conflict    June  2010   ©  Datalicious  Pty  Ltd   27  
  • 28. [  Process  is  key  to  success  ]  June  2010   ©  Datalicious  Pty  Ltd   28   Source:  Omniture  Summit,  MaS  Belkin,  2007  
  • 29. [  Defining  metrics  frameworks  ]   Media  and  search  data   Website,  call  center  and  retail  data   Reach   Engagement   Ac:on   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac&on)   (Sa&sfac&on)   Quan&ta&ve  and  qualita&ve  research  data   Social  media  data   Social  media  June  2010   ©  Datalicious  Pty  Ltd   29  
  • 30. [  Key  metrics  by  website  type  ]  June  2010   ©  Datalicious  Pty  Ltd   30   Source:  Omniture  Summit,  MaS  Belkin,  2007  
  • 31. [  Conversion  funnel  1.0  ]   Campaign  responses   Conversion  funnel   Product  page,  add  to  shopping  cart,  view  shopping  cart,   cart  checkout,  payment  details,  shipping  informa&on,   order  confirma&on,  etc   Conversion  event  June  2010   ©  Datalicious  Pty  Ltd   31  
  • 32. [  Conversion  funnel  2.0  ]   Campaign  responses  (inbound  spokes)   Offline  campaigns,  banner  ads,  email  marke&ng,     referrals,  organic  search,  paid  search,     internal  promo&ons,  etc       Landing  page  (hub)       Success  events  (outbound  spokes)   Bounce  rate,  add  to  cart,  cart  checkout,  confirmed  order,     call  back  request,  registra&on,  product  comparison,     product  review,  forward  to  friend,  etc  June  2010   ©  Datalicious  Pty  Ltd   32  
  • 33. [  Addi:onal  success  metrics  ]   Click   Through   $   Click   Add  To   Cart   Through   Cart   Checkout   ?   $   Click   Bounce   Pages  Per   Video   Through   Rate   Visit   Views   $   Click   Call  back   Store   Through   requests   Searches   ?   $  June  2010   ©  Datalicious  Pty  Ltd   33  
  • 34. Exercise:  Metrics  framework  June  2010   ©  Datalicious  Pty  Ltd   34  
  • 35. [  Exercise:  Metrics  framework  ]   Stage   Metrics   Data  Sources   Reach   Engagement   Ac:on   +Buzz  June  2010   ©  Datalicious  Pty  Ltd   35  
  • 36. [  Exercise:  Metrics  framework  ]   Stage   Metrics   Data  Sources   Impressions,   Ad  Server,     Reach   Searches   Google   Video  Views,   Web  Analy:cs   Engagement   Product  Views   Pla<orm   Orders,   Web  Analy:cs,   Ac:on   Store  Searches   Call  Center   Comments,   Social  Analy:cs   +Buzz   Men:ons   Pla<orm  June  2010   ©  Datalicious  Pty  Ltd   36  
  • 37. [  Combining  data  sets  ]   Web  analy:cs  data   Customer  data   +   The  whole  is  greater     than  the  sum  of  its  parts   3rd  party  data  June  2010   ©  Datalicious  Pty  Ltd   37  
  • 38. [  Behaviours  vs.  transac:ons  ]   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,  expira:on,  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  CONTINUOUSLY   UPDATED  OCCASIONALLY  June  2010   ©  Datalicious  Pty  Ltd   38  
  • 39. [  Store  searches  vs.  actual  loca:ons  ]  June  2010   ©  Datalicious  Pty  Ltd   39  
  • 40. [  Enriching  customer  profiles  ]   All  you  need  is  an  address  June  2010   ©  Datalicious  Pty  Ltd   40   Source:  Hitwise,  2006  
  • 41. [  Hitwise  Mosaic  segment  swing  ]  australia.com  vs.  newzealand.com   australia.com  vs.  bulafiji.com    June  2010   ©  Datalicious  Pty  Ltd   41   Source:  Hitwise,  2006  
  • 42. [  Hitwise  Mosaic  segment  swing  ]  australia.com  vs.  newzealand.com   australia.com  vs.  newzealand.com  June  2010   ©  Datalicious  Pty  Ltd   42   Source:  Hitwise,  2006  
  • 43. [  Single  source  of  truth  ]   Insights   Repor:ng  June  2010   ©  Datalicious  Pty  Ltd   43  
  • 44. [  De-­‐duplica:on  across  channels  ]   Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Central   Analy:cs   Pla<orm   Email     Email   Blast   Pla<orm   $   Organic   Google   Search   Analy:cs   $  June  2010   ©  Datalicious  Pty  Ltd   44  
  • 45. Thinking  outside  the  box  June  2010   ©  Datalicious  Pty  Ltd   45  
  • 46. [  Search  and  brand  strength  ]  June  2010   ©  Datalicious  Pty  Ltd   46  
  • 47. [  Search  and  the  product  lifecycle  ]   Nokia  N-­‐Series   www.google.com/trends   Apple  iPhone  June  2010   ©  Datalicious  Pty  Ltd   47  
  • 48. [  Search  and  media  planning  ]   www.google.com/adplanner  June  2010   ©  Datalicious  Pty  Ltd   48  
  • 49. June  2010   ©  Datalicious  Pty  Ltd   49  
  • 50. June  2010   ©  Datalicious  Pty  Ltd   50  
  • 51. Fiat  500:  Online  influencing  offline   June  2010   ©  Datalicious  Pty  Ltd   51   Google:  “slideshare  fiat  500  case  study”  or  hKp://bit.ly/lh7bx  
  • 52. [  Search  driving  offline  crea:ve  ]  June  2010   ©  Datalicious  Pty  Ltd   52  
  • 53. June  2010   ©  Datalicious  Pty  Ltd   53  
  • 54. Sen:ment  analysis:  People  vs.  machine  June  2010   ©  Datalicious  Pty  Ltd   54   Google:  “people  vs  machines  debate”  or  hKp://bit.ly/8VbtB  
  • 55. [  Social  metrics  and  tools  ]   Google:     ”slideshare     al:meter  report”     or     hKp://bit.ly/c8uYXT  June  2010   ©  Datalicious  Pty  Ltd   55   Source:  Social  Marke&ng  Analy&cs,  Al&meter,  2010  
  • 56. Exercise:  Sta:s:cal  significance  June  2010   ©  Datalicious  Pty  Ltd   56  
  • 57. How  many  survey  responses  do  you  need     if  you  have  10,000  customers?   How  many  email  opens  do  you  need  to  test  2  subject  lines   if  your  subscriber  base  is  50,000?   How  many  orders  do  you  need  to  test  6  banner  execu:ons     if  you  serve  1,000,000  banners  June  2010   ©  Datalicious  Pty  Ltd   57  
  • 58. How  many  survey  responses  do  you  need     if  you  have  10,000  customers?   369  for  each  ques:on  or  369  complete  responses   How  many  email  opens  do  you  need  to  test  2  subject  lines   if  your  subscriber  base  is  50,000?   381  per  subject  line  or  381  x  2  =  762  email  opens   How  many  orders  do  you  need  to  test  6  banner  execu:ons     if  you  serve  1,000,000  banners?   383  sales  per  banner  execu:on  or  383  x  6  =  2,298  sales  June  2010   ©  Datalicious  Pty  Ltd   58  
  • 59. [  Summary:  Genera:ng  insights  ]  §  Right  resources  and  processes  are  key  §  Define  a  flexible  metrics  framework  §  Maintain  framework  to  enable  comparison  §  Combine  data  sets  for  hidden  insights    §  Establish  a  single  (data)  source  of  truth  §  Think  outside  the  box  and  across  channels  §  Data  does  not  equal  significance  June  2010   ©  Datalicious  Pty  Ltd   59  
  • 60. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Taking  ac:on  ]  June  2010   ©  Datalicious  Pty  Ltd   60  
  • 61. [  How  to  drive  ROI  ]  §  Increasing  revenue   –  Increasing  overall  amount  of  sales     –  Increasing  the  average  revenue  per  sale  §  Reducing  costs   –  Increasing  media  effec&veness   –  Increasing  website  conversion  rates   –  Increasing  online  self-­‐service  usage  §  Improving  customer  experience   –  Reducing  steps  necessary  to  complete  a  task   –  Perceived  value  or  quality  of  the  final  solu&on  June  2010   ©  Datalicious  Pty  Ltd   61  
  • 62. [  How  to  drive  ROI  ]   Media  or  how  to  op:mise  the  channel  mix   Targe:ng  or  how  to  increasing  relevance   Tes:ng  or  how  to  maximise  conversion  June  2010   ©  Datalicious  Pty  Ltd   62  
  • 63. [  Success  aKribu:on  models  ]   Banner     Paid     Organic   Success   Last  channel   Search   Ad   Search   $100   $100   gets  all  credit   Banner     Paid     Email     Success   First  channel   Ad   $100   Search   Blast   $100   gets  all  credit   Paid     Banner     Affiliate     Success   All  channels  get   Search   Ad   Referral   $100   $100   $100   $100   equal  credit   Print     Social     Paid     Success   All  channels  get   Ad   Media   Search   $33   $33   $33   $100   par:al  credit  June  2010   ©  Datalicious  Pty  Ltd   63  
  • 64. [  First  vs.  last  click  aKribu:on  ]   Chart  shows   percentage  of   channel  touch   points  that  lead   Paid/Organic  Search   to  a  conversion.   Neither  first     Emails/Shopping  Engines   nor  last-­‐click   measurement   would  provide   true  picture    June  2010   ©  Datalicious  Pty  Ltd   64  
  • 65. [  Path  to  purchase  ]   Banner     SEM   Partner   Direct     Click   Generic   Site   Visit   $   Banner     SEO   View   Generic   $   TV   SEO   Banner     Ad   Branded   Click   $   Print     Social     Email   Direct     Ad   Media   Update   Visit   $  June  2010   ©  Datalicious  Pty  Ltd   65  
  • 66. [  Forrester  media  aKribu:on  ]   Google:     ”forrester   aKribu:on   framework  pdf”     or     hKp://bit.ly/ dnbnzY  June  2010   ©  Datalicious  Pty  Ltd   66   Source:  Forrester,  2009  
  • 67. [  Customer  data  journey  ]   To  transac:onal  data   To  reten:on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages  June  2010   ©  Datalicious  Pty  Ltd   67  
  • 68. June  2010   ©  Datalicious  Pty  Ltd   68  
  • 69. June  2010   ©  Datalicious  Pty  Ltd   69  
  • 70. [  Matching  segments  are  key  ]   On-­‐site     Off-­‐site   segments   segments   On  and  off-­‐site  targe:ng  pla<orms  should  use     iden:cal  triggers  to  sort  visitors  into  segments  June  2010   ©  Datalicious  Pty  Ltd   70  
  • 71. [  Off-­‐site  targe:ng  pla<orms  ]  §  Ad  servers   §  Ad  Networks   –  Google/DoubleClick   –  Google   –  Eyeblaster   –  Yahoo   –  Faciliate   –  ValueClick   –  Atlas   –  Adconian   –  Etc   –  Etc   hSp://en.wikipedia.org/wiki/Contextual_adver&sing,  hSp://hubpages.com/hub/101-­‐Google-­‐Adsense-­‐Alterna&ves,     hSp://en.wikipedia.org/wiki/Central_ad_server,  hSp://www.adopera&onsonline.com/2008/05/23/list-­‐of-­‐ad-­‐servers/,     hSp://lists.econsultant.com/top-­‐10-­‐adver&sing-­‐networks.html,  hSp://www.clickz.com/3633599,  hSp://en.wikipedia.org/wiki/ behavioural_targe&ng      June  2010   ©  Datalicious  Pty  Ltd   71  
  • 72. [  On-­‐site  targe:ng  pla<orms  ]  §  Test&Target  (Omniture,  Offerma&ca,  TouchClarity)  §  Memetrics  (Accenture)  §  Op&most  (Autonomy)  §  KeWa  (Acxiom)  §  AudienceScience  §  Maxymiser  §  Amadesa  §  Certona  §  SiteSpect  §  BTBuckets  (free)  §  Google/DoubleClick  Ad  Server  (free)  June  2010   ©  Datalicious  Pty  Ltd   72  
  • 73. [  Prospect  targe:ng  parameters  ]  June  2010   ©  Datalicious  Pty  Ltd   73  
  • 74. [  Vodafone  affinity  targe:ng  ]   Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe&ng,     response  rates  are     liWed  significantly     across  products.   CTR  By  Category  Affinity   Message   Postpay   Prepay   Broadb.   Business   Blackberry  Bold   - - - + 5GB  Mobile  Broadband   - - + - Blackberry  Storm   + - + + 12  Month  Caps   - + - +June  2010   ©  Datalicious  Pty  Ltd   74  
  • 75. [  Affinity  targe:ng  ]    §  Func&on  of  behavioural  targe&ng   –  Grouping  of  visitors  into  major  segments   –  Based  on  content  and  conversion  behaviour   –  Ease  of  use  vs.  reduced  targe&ng  ability  §  Most  common  affini&es  used   –  Brand  affinity   –  Image  preference   –  Price  sensi&vity   –  Product  affinity   –  Content  affinity  June  2010   ©  Datalicious  Pty  Ltd   75  
  • 76. [  Coordinate  the  experience  ]   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  hKp://bit.ly/cAtSUN  June  2010   ©  Datalicious  Pty  Ltd   76   Source:  McKinsey  Quarterly,  2010  
  • 77. [  Quality  content  is  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.”  June  2010   ©  Datalicious  Pty  Ltd   77  
  • 78. Exercise:  Targe:ng  matrix  June  2010   ©  Datalicious  Pty  Ltd   78  
  • 79. [  Exercise:  Targe:ng  matrix  ]   Phase   Segment  A   Segment  B   Awareness   Considera:on   Purchase  Intent   Up/Cross-­‐Sell   Reten:on  June  2010   ©  Datalicious  Pty  Ltd   79  
  • 80. [  Exercise:  Targe:ng  matrix  ]   Phase   Segment  A   Segment  B   Awareness   Seen  this?   Considera:on   Great  feature!   Purchase  Intent   Great  value!   Up/Cross-­‐Sell   Add  this!   Reten:on   Discount?  June  2010   ©  Datalicious  Pty  Ltd   80  
  • 81. [  ClickTale  tes:ng  case  study  ]   Google:  “change  one  word  double  conversion”     or  hKp://bit.ly/bpyqFp  June  2010   ©  Datalicious  Pty  Ltd   81  
  • 82. [  Tes:ng  pla<orms  ]  §  Test&Target  (Omniture,  Offerma&ca,  TouchClarity)  §  Memetrics  (Accenture)  §  Op&most  (Autonomy)  §  KeWa  (Acxiom)  §  Maxymiser  §  Amadesa  §  SiteSpect  §  ClickTale  (cheap)  §  Unbounce  (cheap)  §  Google  Website  Op&miser  (free)  June  2010   ©  Datalicious  Pty  Ltd   82  
  • 83. [  Summary  ]  §  There  is  no  magic  formula  for  ROI  §  Focus  on  the  en&re  conversion  funnel  §  Media  aSribu&on  is  hard  but  necessary  §  Neither  first  nor  last  click  method  works  §  Create  a  coordinated  targeted  experience  §  Content  is  always  king  no  maSer  what  §  Test,  learn  and  refine  con&nuously  June  2010   ©  Datalicious  Pty  Ltd   83  
  • 84. Contact  me   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twiSer.com/datalicious    June  2010   ©  Datalicious  Pty  Ltd   84  

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