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The presentation discusses the concepts, principles and significance of data driven marketing.

The presentation discusses the concepts, principles and significance of data driven marketing.

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  • 1. >  ANZ  Analy*cs  Workshop  <   Smart  Data  Driven  Marke.ng  
  • 2. >  Short  but  sharp  history  §  Datalicious  was  founded  late  2007  §  Strong  Omniture  web  analy.cs  history  §  Now  360  data  agency  with  specialist  team  §  Combina.on  of  analysts  and  developers  §  Carefully  selected  best  of  breed  partners  §  Evangelizing  smart  data  driven  marke.ng  §  Making  data  accessible  and  ac.onable  §  Driving  industry  best  prac.ce  (ADMA)  March  2011   ©  Datalicious  Pty  Ltd   2  
  • 3. >  Clients  across  all  industries  March  2011   ©  Datalicious  Pty  Ltd   3  
  • 4. >  Wide  range  of  data  services   Data   Insights   Ac*on   PlaAorms   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  aNribu*on  models   Alterian,  Trac*on,  Inxmail,  etc         Tag-­‐less  online  data  capture   Market  and  compe*tor  trends   Targe*ng  and  merchandising         End-­‐to-­‐end  data  plaAorms   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    March  2011   ©  Datalicious  Pty  Ltd   4  
  • 5. >  Smart  data  driven  marke*ng     Metrics  Framework Metrics  Framework Media  ANribu*on Benchmarking  and  trending   Benchmarking  and  trending     Op*mise  channel  mix   Targe*ng     Increase  relevance   Tes*ng   Improve  usability     $$$  March  2011   ©  Datalicious  Pty  Ltd   5  
  • 6. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Metrics  framework  March  2011   ©  Datalicious  Pty  Ltd   6  
  • 7. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac*on   Sa*sfac*on   Social  media  March  2011   ©  Datalicious  Pty  Ltd   7  
  • 8. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac.on)   (Sa.sfac.on)  March  2011   ©  Datalicious  Pty  Ltd   8  
  • 9. >  Marke*ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  March  2011   ©  Datalicious  Pty  Ltd   9  
  • 10. >  Unique  visitor  overes*ma*on    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.    March  2011   ©  Datalicious  Pty  Ltd   10   Source:  White  Paper,  RedEye,  2007  
  • 11. >  Maximise  iden*fica*on  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  March  2011   ©  Datalicious  Pty  Ltd   11  
  • 12. >  Maximise  iden*fica*on  points   Mobile   Home   Work   Online   Phone   Branch  March  2011   ©  Datalicious  Pty  Ltd   12  
  • 13. >  Addi*onal  funnel  breakdowns     Brand  vs.  direct  response  campaign   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   New  prospects  vs.  exis.ng  customers  March  2011   ©  Datalicious  Pty  Ltd   13  
  • 14. New  vs.  returning  visitors  
  • 15. AU/NZ  vs.  rest  of  world  
  • 16. Exercise:  Funnel  breakdowns  
  • 17. >  Exercise:  Funnel  breakdowns    §  List  poten.ally  insighcul  funnel  breakdowns   –  Brand  vs.  direct  response  campaign   –  New  prospects  vs.  exis.ng  customers   –  Baseline  vs.  incremental  conversions   –  Compe..ve  ac.vity,  i.e.  none,  a  lot,  etc   –  Segments,  i.e.  age,  loca.on,  influence,  etc   –  Channels,  i.e.  search,  display,  social,  etc   –  Campaigns,  i.e.  this/last  week,  month,  year,  etc   –  Products  and  brands,  i.e.  iphone,  htc,  etc   –  Offers,  i.e.  free  minutes,  free  handset,  etc   –  Devices,  i.e.  home,  office,  mobile,  tablet,  etc  March  2011   ©  Datalicious  Pty  Ltd   17  
  • 18. >  Mul*ple  metrics  data  sources   Media  and  search  data   Website,  call  center  and  retail  data   People   People   People   People   reached   engaged   converted   delighted   Quan.ta.ve  and  qualita.ve  research  data   Social  media  data   Social  media  March  2011   ©  Datalicious  Pty  Ltd   18  
  • 19. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  March  2011   ©  Datalicious  Pty  Ltd   19  
  • 20. Calendar  events  to  add  context  March  2011   ©  Datalicious  Pty  Ltd   20  
  • 21. >  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  March  2011   ©  Datalicious  Pty  Ltd   21  
  • 22. >  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  March  2011   ©  Datalicious  Pty  Ltd   22  
  • 23. >  Addi*onal  success  metrics     Click   Through   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $  March  2011   ©  Datalicious  Pty  Ltd   23  
  • 24. Exercise:  Sta*s*cal  significance  March  2011   ©  Datalicious  Pty  Ltd   24  
  • 25. 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  March  2011   ©  Datalicious  Pty  Ltd   25   Google  “nss  sample  size  calculator”  
  • 26. 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?  And  email  sends?   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  March  2011   ©  Datalicious  Pty  Ltd   26   Google  “nss  sample  size  calculator”  
  • 27. >  Addi*onal  success  metrics     Click   Through   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $  March  2011   ©  Datalicious  Pty  Ltd   27  
  • 28. Exercise:  Metrics  framework  
  • 29. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac*cal   Funnel   breakdowns  March  2011   ©  Datalicious  Pty  Ltd   29  
  • 30. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   People   People   People   People   reached   engaged   converted   delighted   Level  2   Display   Strategic   impressions   ?   ?   ?   Level  3   Interac*on   Tac*cal   rate,  etc   ?   ?   ?   Funnel   Exis*ng  customers  vs.  new  prospects,  products,  etc   Breakdowns  March  2011   ©  Datalicious  Pty  Ltd   30  
  • 31. >  Exercise:  Conversion  Funnel  March  2011   ©  Datalicious  Pty  Ltd   31  
  • 32. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  aNribu*on  March  2011   ©  Datalicious  Pty  Ltd   32  
  • 33. >  Complex  campaign  flows   =  Paid  media   Organic     PR,  WOM,   search   events,  etc   =  Viral  elements   =  Sales  channels   YouTube,     Home  pages,   Paid     TV,  print,     blog,  etc   portals,  etc   search   radio,  etc   Direct  mail,     Landing  pages,   Display  ads,   email,  etc   offers,  etc   affiliates,  etc   CRM   Facebook   program   TwiNer,  etc   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc  March  2011   ©  Datalicious  Pty  Ltd   33  
  • 34. >  Duplica*on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Email     Email   Blast   PlaAorm   $   Organic   Web   Search   Analy*cs   $  March  2011   ©  Datalicious  Pty  Ltd   34  
  • 35. >  Cookie  expira*on  impact   Paid     Bid     Search   Mgmt   $   Banner     Banner     Ad     Ad  Click   Ad  View   Server   $   Email     Email   Expira*on   Blast   PlaAorm   $   Organic   Google   Search   Analy*cs   $  March  2011   ©  Datalicious  Pty  Ltd   35  
  • 36. >  ANZ  repor*ng  plaAorms  March  2011   ©  Datalicious  Pty  Ltd   36  
  • 37. >  De-­‐duplica*on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy*cs   PlaAorm   Email     Blast   $   Organic   Search   $  March  2011   ©  Datalicious  Pty  Ltd   37  
  • 38. Exercise:  Duplica*on  impact  March  2011   ©  Datalicious  Pty  Ltd   38  
  • 39. >  Exercise:  Duplica*on  impact    §  Double-­‐coun.ng  of  conversions  across  channels  can   have  a  significant  impact  on  key  metrics,  especially  CPA  §  Example:  Display  ads  and  paid  search   –  Total  media  budget  of  $10,000  of  which  50%  is  spend  on  paid   search  and  50%  on  display  ads   –  Total  of  100  conversions  across  both  channels  with  a  channel   overlap  of  50%,  i.e.  both  channels  claim  100%  of  conversions   based  on  their  own  repor.ng  but  once  de-­‐duplicated  they   each  only  contributed  50%  of  conversions   –  What  are  the  ini.al  CPA  values  and  what  is  the  true  CPA?  §  Solu.on:  $50  ini.al  CPA  and  $100  true  CPA   –  $5,000  /  100  =  $50  ini.al  CPA  and  $5,000  /  50  =  $100  true   CPA  (which  represents  a  100%  increase)  March  2011   ©  Datalicious  Pty  Ltd   39  
  • 40. >  Reach  and  channel  overlap     TV/Print     audience   Banner   Search   audience   audience  March  2011   ©  Datalicious  Pty  Ltd   40  
  • 41. >  Ad  server  exposure  test   Banner   TV/Print   Search   Impression   Response   Response   $   Banner   Search   Direct   Impression   Response   Response   $   Users  are   segmented   before  1st   ad  is  even   Exposed  group:  90%  of  users  get  branded  message   served     Control  group:  10%  of  users  get  non-­‐branded  message   Banner   Search   Direct   Impression   Response   Response   $  March  2011   ©  Datalicious  Pty  Ltd   41  
  • 42. >  Indirect  display  impact    March  2011   ©  Datalicious  Pty  Ltd   42  
  • 43. >  Indirect  display  impact    March  2011   ©  Datalicious  Pty  Ltd   43  
  • 44. March  2011   ©  Datalicious  Pty  Ltd   44  
  • 45. >  Indirect  display  impact    March  2011   ©  Datalicious  Pty  Ltd   45  
  • 46. >  Success  aNribu*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  March  2011   ©  Datalicious  Pty  Ltd   46  
  • 47. >  First  and  last  click  aNribu*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    March  2011   ©  Datalicious  Pty  Ltd   47  
  • 48. >  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update  March  2011   ©  Datalicious  Pty  Ltd   48  
  • 49. >  Poten*al  calls  to  ac*on    §  Unique  click-­‐through  URLs  §  Unique  vanity  domains  or  URLs  §  Unique  phone  numbers  §  Unique  search  terms  §  Unique  email  addresses  §  Unique  personal  URLs  (PURLs)  §  Unique  SMS  numbers,  QR  codes  §  Unique  promo.onal  codes,  vouchers  §  Geographic  loca.on  (Facebook,  FourSquare)  §  Plus  regression  analysis  of  cause  and  effect  March  2011   ©  Datalicious  Pty  Ltd   49  
  • 50. >  Search  call  to  ac*on  for  offline    March  2011   ©  Datalicious  Pty  Ltd   50  
  • 51. March  2011   ©  Datalicious  Pty  Ltd   51  
  • 52. >  PURLs  boos*ng  DM  response  rates   Text  March  2011   ©  Datalicious  Pty  Ltd   52  
  • 53. >  Jet  Interac*ve  phone  call  data  March  2011   ©  Datalicious  Pty  Ltd   53  
  • 54. >  Unique  phone  numbers  §  1  unique  phone  number     –  Phone  number  is  considered  part  of  the  brand   –  Media  origin  of  calls  cannot  be  established   –  Added  value  of  website  interac.on  unknown  §  2-­‐10  unique  phone  numbers   –  Different  numbers  for  different  media  channels   –  Exclusive  number(s)  reserved  for  website  use   –  Call  origin  data  more  granular  but  not  perfect   –  Difficult  to  rotate  and  pause  numbers  March  2011   ©  Datalicious  Pty  Ltd   54  
  • 55. >  Unique  phone  numbers  §  10+  unique  phone  numbers   –  Different  numbers  for  different  media  channels   –  Different  numbers  for  different  product  categories   –  Different  numbers  for  different  conversion  steps   –  Call  origin  becoming  useful  to  shape  call  script   –  Feasible  to  pause  numbers  to  improve  integrity  §  100+  unique  phone  numbers   –  Different  numbers  for  different  website  visitors   –  Call  origin  and  .me  stamp  enable  individual  match   –  Call  conversions  matched  back  to  search  terms  March  2011   ©  Datalicious  Pty  Ltd   55  
  • 56. >  Cross-­‐channel  impact  March  2011   ©  Datalicious  Pty  Ltd   56  
  • 57. >  Offline  sales  driven  by  online   Adver*sing     Phone   Credit  check,   campaign   order   fulfilment   Retail   Confirma*on   order   email   Website   Online   Online  order   Virtual  order   research   order   confirma*on   confirma*on   Cookie  March  2011   ©  Datalicious  Pty  Ltd   57  
  • 58. >  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update  March  2011   ©  Datalicious  Pty  Ltd   58  
  • 59. Adobe  campaign  stack   does  not  include  organic   channels  or  banner   impressions  and  does  not   expire  on  any  event,  i.e.   con*nues  as  long  as  the   cookie  is  present.  March  2011   ©  Datalicious  Pty  Ltd   59  
  • 60. >  Where  to  collect  the  data     Ad  Server   Web  Analy*cs   Banner  impressions   Referral  visits   Banner  clicks   Social  media  visits   +   Organic  search  visits   Paid  search  clicks   Paid  search  visits   Email  visits,  etc   Lacking  organic  visits   Lacking  ad  impressions   More  granular  &  complex   Less  granular  &  complex  March  2011   ©  Datalicious  Pty  Ltd   60  
  • 61. >  Maximise  iden*fica*on  points   Mobile   Home   Work   Online   Phone   Branch  March  2011   ©  Datalicious  Pty  Ltd   61  
  • 62. >  Combining  data  sources  March  2011   ©  Datalicious  Pty  Ltd   62  
  • 63. >  Single  source  of  truth  repor*ng   Insights   Repor*ng  March  2011   ©  Datalicious  Pty  Ltd   63  
  • 64. >  Understanding  channel  mix  March  2011   ©  Datalicious  Pty  Ltd   64  
  • 65. >  Website  entry  survey     De-­‐duped  Campaign  Report   Greatest  Influencer  on  Branded  Search  /  STS   }   Channel   %  of  Conversions   Channel   %  of  Influence   Straight  to  Site   27%   Word  of  Mouth   32%   SEO  Branded   15%   Blogging  &  Social  Media   24%   SEM  Branded   9%   Newspaper  Adver.sing   9%   SEO  Generic   7%   Display  Adver.sing   14%   SEM  Generic   14%   Email  Marke.ng   7%   Display  Adver.sing   7%   Retail  Promo.ons   14%   Affiliate  Marke.ng   9%   Referrals   5%   Conversions  arributed  to  search  terms   Email  Marke.ng   7%   that  contain  brand  keywords  and  direct   website  visits  are  most  likely  not  the   origina.ng  channel  that  generated  the   awareness  and  as  such  conversion   credits  should  be  re-­‐allocated.    March  2011   ©  Datalicious  Pty  Ltd   66  
  • 66. >  Adjus*ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10  March  2011   ©  Datalicious  Pty  Ltd   67  
  • 67. >  Success  aNribu*on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   ANrib.   Exclusion   33%   33%   33%   0%   ANrib.   PaNern   30%   20%   20%   30%   ANrib.  March  2011   ©  Datalicious  Pty  Ltd   68  
  • 68. >  Path  across  different  segments   Introducer   Influencer   Influencer   Closer   $   Product     Channel  1   Channel  2   Channel  3   Channel  4   A  vs.  B   New   Channel  1   Channel  2   Channel  3   Channel  4   prospects   Exis*ng   Channel  1   Channel  2   Channel  3   Product  4   customers  March  2011   ©  Datalicious  Pty  Ltd   69  
  • 69. Exercise:  ANribu*on  model  March  2011   ©  Datalicious  Pty  Ltd   70  
  • 70. >  Exercise:  ANribu*on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   ANrib.   Exclusion   33%   33%   33%   0%   ANrib.   ?   ?   ?   ?   Custom   ANrib.  March  2011   ©  Datalicious  Pty  Ltd   71  
  • 71. >  Common  aNribu*on  models  §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  strong   baseline  to  s.mulate  repeat  purchases    §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  direct   response  focus  §  Allocate  more  conversion  credits  to  ini.a.ng   touch  points  for  new  and  expensive  brands  and   products  to  insert  them  into  the  mindset  March  2011   ©  Datalicious  Pty  Ltd   72  
  • 72. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Targe*ng  and  tes*ng  March  2011   ©  Datalicious  Pty  Ltd   73  
  • 73. >  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%  to  sales   Building  consumer  loyalty   Build  60%  loyalty  rate  and  40%  sales  conversion   Increase  online  revenue   Earn  10-­‐20%  incremental  revenue  online  March  2011   ©  Datalicious  Pty  Ltd   74  
  • 74. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  March  2011   ©  Datalicious  Pty  Ltd   75  
  • 75. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.   Online  research     Change  increases   the  importance  of   experience  during   research  phase.  March  2011   ©  Datalicious  Pty  Ltd   76  
  • 76. >  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  March  2011   ©  Datalicious  Pty  Ltd   77  
  • 77. >  Coordina*on  across  channels         Genera*ng   Crea*ng   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     targe*ng   targe*ng   targe*ng  March  2011   ©  Datalicious  Pty  Ltd   78  
  • 78. >  Combining  targe*ng  plaAorms     Off-­‐site   targe.ng   Profile   On-­‐site   targe.ng   targe.ng  March  2011   ©  Datalicious  Pty  Ltd   79  
  • 79. ANZ  Low  Rate  MasterCard    
  • 80. ANZ  Business  Debit  Card  
  • 81. >  Combining  technology     On-­‐site     Off-­‐site   segments   segments   CRM  March  2011   ©  Datalicious  Pty  Ltd   84  
  • 82. >  SuperTag  code  architecture     §  Central  JavaScript  container  tag   §  One  tag  for  all  sites  and  placorms   §  Hosted  internally  or  externally   §  Faster  tag  implementa.on/updates   §  Eliminates  JavaScript  caching   §  Enables  code  tes.ng  on  live  site   §  Enables  heat  map  implementa.on   §  Enables  redirects  for  A/B  tes.ng   §  Enables  network  wide  re-­‐targe.ng   §  Enables  live  chat  implementa.on  March  2011   ©  Datalicious  Pty  Ltd   85  
  • 83. >  Combining  data  sets     Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  March  2011   ©  Datalicious  Pty  Ltd   86  
  • 84. >  Behaviours  plus  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  Con*nuously   Updated  Occasionally  March  2011   ©  Datalicious  Pty  Ltd   87  
  • 85. >  Maximise  iden*fica*on  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  March  2011   ©  Datalicious  Pty  Ltd   88  
  • 86. >  Maximise  iden*fica*on  points   Mobile   Home   Work   Online   Phone   Branch  March  2011   ©  Datalicious  Pty  Ltd   89  
  • 87. >  Sample  customer  level  data    March  2011   ©  Datalicious  Pty  Ltd   90  
  • 88. >  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  transac.onal  history  of   prospects   which  maybe  50%  can  actually  be   with  limited   iden.fied  as  individuals     profile  data  March  2011   ©  Datalicious  Pty  Ltd   91  
  • 89. >  Prospect  targe*ng  parameters    March  2011   ©  Datalicious  Pty  Ltd   92  
  • 90. >  Affinity  re-­‐targe*ng  in  ac*on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe.ng,     response  rates  are     lited  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  hNp://bit.ly/de70b7   12  Month  Caps   - + - +March  2011   ©  Datalicious  Pty  Ltd   93  
  • 91. >  Ad-­‐sequencing  in  ac*on   Marke.ng  is  about   telling  stories  and   stories  are  not  sta.c   but  evolve  over  .me   Ad-­‐sequencing  can  help  to   evolve  stories  over  .me  the     more  users  engage  with  ads  March  2011   ©  Datalicious  Pty  Ltd   94  
  • 92. Exercise:  Targe*ng  matrix  
  • 93. >  Exercise:  Targe*ng  matrix   Purchase   Segments:  Colour,  price,   Media   Data     Cycle   product  affinity,  etc   Channels   Points   Default,   awareness   Research,   considera*on   Purchase   intent   Reten*on,   up/cross-­‐sell  March  2011   ©  Datalicious  Pty  Ltd   96  
  • 94. >  Exercise:  Targe*ng  matrix   Purchase   Segments:  Colour,  price,   Media   Data     Cycle   product  affinity,  etc   Channels   Points   Default,   Have  you     Have  you     Display,   Default   awareness   seen  A?   seen  B?   search,  etc   Research,   A  has  great     B  has  great     Search,   Ad  clicks,   considera*on   features!   features!   website,  etc   prod  views   Purchase   A  delivers   B  delivers   Website,   Cart  adds,   intent   great  value!   great  value!   emails,  etc   checkouts   Reten*on,   Why  not   Why  not   Direct  mails,   Email  clicks,   up/cross-­‐sell   buy  B?   buy  A?   emails,  etc   logins,  etc  March  2011   ©  Datalicious  Pty  Ltd   97  
  • 95. >  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.”  March  2011   ©  Datalicious  Pty  Ltd   98  
  • 96. >  ClickTale  tes*ng  case  study    March  2011   ©  Datalicious  Pty  Ltd   99  
  • 97. >  Bad  campaign  worse  than  none    March  2011   ©  Datalicious  Pty  Ltd   100  
  • 98. Exercise:  Tes*ng  matrix  
  • 99. >  Exercise:  Tes*ng  matrix   Test   Segment   Content   KPIs   Poten*al   Results  March  2011   ©  Datalicious  Pty  Ltd   102  
  • 100. >  Exercise:  Tes*ng  matrix   Test   Segment   Content   KPIs   Poten*al   Results   New   Conversion   Next  step,   Test  #1A     prospects   form  A   order,  etc   ?   ?   New   Conversion   Next  step,   Test  #1B   prospects   form  B   order,  etc   ?   ?   New   Conversion   Next  step,   Test  #1N   prospects   form  N   order,  etc   ?   ?   ?   ?   ?   ?   ?   ?  March  2011   ©  Datalicious  Pty  Ltd   103  
  • 101. >  Keys  to  effec*ve  targe*ng     1.  Define  success  metrics   2.  Define  and  validate  segments   3.  Develop  targe.ng  and  message  matrix     4.  Transform  matrix  into  business  rules   5.  Develop  and  test  content   6.  Start  targe.ng  and  automate   7.  Keep  tes.ng  and  refining   8.  Communicate  results  March  2011   ©  Datalicious  Pty  Ltd   104  
  • 102. Contact  us   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twiNer.com/datalicious    March  2011   ©  Datalicious  Pty  Ltd   105  
  • 103. Data  >  Insights  >  Ac*on