Ad Tech Campaign Measurement

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Ad Tech Campaign Measurement

  1. 1. >  Campaign  Measurement  <   Digital  Campaign  Measurement   ad:tech  2011  Workshop  
  2. 2. >  Short  but  sharp  history  §  Datalicious  was  founded  late  2007  §  Strong  Omniture  web  analyBcs  history  §  Now  360  data  agency  with  specialist  team  §  CombinaBon  of  analysts  and  developers  §  Carefully  selected  best  of  breed  partners  §  Evangelizing  smart  data  driven  markeBng  §  Making  data  accessible  and  acBonable  §  Driving  industry  best  pracBce  (ADMA)  March  2011   ©  Datalicious  Pty  Ltd   2  
  3. 3. >  Clients  across  all  industries  March  2011   ©  Datalicious  Pty  Ltd   3  
  4. 4. >  Wide  range  of  data  services   Data   Insights   Ac?on   Pla>orms   Repor?ng   Campaigns         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   Alterian,  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    March  2011   ©  Datalicious  Pty  Ltd   4  
  5. 5. >  Smart  data  driven  marke?ng     Standardised  Metrics Standardised  Metrics Media  AKribu?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. 6. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Metrics  framework    March  2011   ©  Datalicious  Pty  Ltd   6  
  7. 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. 8. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (AcBon)   (SaBsfacBon)  March  2011   ©  Datalicious  Pty  Ltd   8  
  9. 9. >  Marke?ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  March  2011   ©  Datalicious  Pty  Ltd   9  
  10. 10. >  Addi?onal  funnel  breakdowns     Brand  vs.  direct  response  campaign   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   New  prospects  vs.  exisBng  customers  March  2011   ©  Datalicious  Pty  Ltd   10  
  11. 11. New  vs.  returning  visitors  
  12. 12. AU/NZ  vs.  rest  of  world  
  13. 13. Exercise:  Funnel  breakdowns  
  14. 14. >  Exercise:  Funnel  breakdowns    §  List  potenBally  insighXul  funnel  breakdowns   –  Brand  vs.  direct  response  campaign   –  New  prospects  vs.  exisBng  customers   –  Baseline  vs.  incremental  conversions   –  CompeBBve  acBvity,  i.e.  none,  a  lot,  etc   –  Segments,  i.e.  age,  locaBon,  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  March  2011   ©  Datalicious  Pty  Ltd   14  
  15. 15. >  Mul?ple  metrics  data  sources   Media  and  search  data   Website,  call  center  and  retail  data   People   People   People   People   reached   engaged   converted   delighted   QuanBtaBve  and  qualitaBve  research  data   Social  media  data   Social  media  March  2011   ©  Datalicious  Pty  Ltd   15  
  16. 16. >  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   16  
  17. 17. Calendar  events  to  add  context  March  2011   ©  Datalicious  Pty  Ltd   17  
  18. 18. >  Conversion  funnel  1.0     Campaign  responses   Conversion  funnel   Product  page,  add  to  shopping  cart,  view  shopping  cart,   cart  checkout,  payment  details,  shipping  informaBon,   order  confirmaBon,  etc   Conversion  event  March  2011   ©  Datalicious  Pty  Ltd   18  
  19. 19. >  Conversion  funnel  2.0     Campaign  responses  (inbound  spokes)   Offline  campaigns,  banner  ads,  email  markeBng,     referrals,  organic  search,  paid  search,     internal  promoBons,  etc       Landing  page  (hub)       Success  events  (outbound  spokes)   Bounce  rate,  add  to  cart,  cart  checkout,  confirmed  order,     call  back  request,  registraBon,  product  comparison,     product  review,  forward  to  friend,  etc  March  2011   ©  Datalicious  Pty  Ltd   19  
  20. 20. >  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   20  
  21. 21. Exercise:  Sta?s?cal  significance  March  2011   ©  Datalicious  Pty  Ltd   21  
  22. 22. 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   22   Google  “nss  sample  size  calculator”  
  23. 23. 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   23   Google  “nss  sample  size  calculator”  
  24. 24. >  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   24  
  25. 25. Exercise:  Metrics  framework  
  26. 26. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   Level  2   Strategic   Level  3   Tac?cal  March  2011   ©  Datalicious  Pty  Ltd   26  
  27. 27. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   People   People   People   People   reached   engaged   converted   delighted   Search   Level  2   Strategic   impressions,   UBs,  etc   ?   ?   ?   Keyword   Level  3   Tac?cal   rank,  click-­‐ through,  etc   ?   ?   ?  March  2011   ©  Datalicious  Pty  Ltd   27  
  28. 28. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  aKribu?on  March  2011   ©  Datalicious  Pty  Ltd   28  
  29. 29. >  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   TwiKer,  etc   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc  March  2011   ©  Datalicious  Pty  Ltd   29  
  30. 30. >  Duplica?on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Email     Email   Blast   Pla>orm   $   Organic   Google   Search   Analy?cs   $  March  2011   ©  Datalicious  Pty  Ltd   30  
  31. 31. >  Cookie  expira?on  impact   Paid     Bid     Search   Mgmt   $   Banner     Banner     Ad     Ad  Click   Ad  View   Server   $   Email     Email   Expira?on   Blast   Pla>orm   $   Organic   Google   Search   Analy?cs   $  March  2011   ©  Datalicious  Pty  Ltd   31  
  32. 32. >  De-­‐duplica?on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy?cs   Pla>orm   Email     Blast   $   Organic   Search   $  March  2011   ©  Datalicious  Pty  Ltd   32  
  33. 33. Exercise:  Duplica?on  impact  March  2011   ©  Datalicious  Pty  Ltd   33  
  34. 34. >  Exercise:  Duplica?on  impact    §  Double-­‐counBng  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  reporBng  but  once  de-­‐duplicated  they   each  only  contributed  50%  of  conversions   –  What  are  the  iniBal  CPA  values  and  what  is  the  true  CPA?  §  SoluBon:  $50  iniBal  CPA  and  $100  true  CPA   –  $5,000  /  100  =  $50  iniBal  CPA  and  $5,000  /  50  =  $100  true   CPA  (which  represents  a  100%  increase)  March  2011   ©  Datalicious  Pty  Ltd   34  
  35. 35. >  Reach  and  channel  overlap     TV/Print     audience   Banner   Search   audience   audience  March  2011   ©  Datalicious  Pty  Ltd   35  
  36. 36. >  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   36  
  37. 37. >  Indirect  display  impact    March  2011   ©  Datalicious  Pty  Ltd   37  
  38. 38. >  Indirect  display  impact    March  2011   ©  Datalicious  Pty  Ltd   38  
  39. 39. >  Indirect  display  impact    March  2011   ©  Datalicious  Pty  Ltd   39  
  40. 40. >  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  March  2011   ©  Datalicious  Pty  Ltd   40  
  41. 41. >  First  and  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    March  2011   ©  Datalicious  Pty  Ltd   41  
  42. 42. >  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   42  
  43. 43. >  Search  call  to  ac?on  for  offline    March  2011   ©  Datalicious  Pty  Ltd   43  
  44. 44. Offline  response  tracking  and  improved  experience   March  2011   ©  Datalicious  Pty  Ltd   44  
  45. 45. March  2011   ©  Datalicious  Pty  Ltd   45  
  46. 46. hKp://www.suncorp.com.au?campaign=workshop   March  2011   ©  Datalicious  Pty  Ltd   46  
  47. 47. >  PURLs  boos?ng  DM  response  rates   Text  March  2011   ©  Datalicious  Pty  Ltd   47  
  48. 48. >  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  promoBonal  codes,  vouchers  §  Geographic  locaBon  (Facebook,  FourSquare)  §  Plus  regression  analysis  of  cause  and  effect  March  2011   ©  Datalicious  Pty  Ltd   48  
  49. 49. >  Jet  Interac?ve  phone  call  data  March  2011   ©  Datalicious  Pty  Ltd   49  
  50. 50. >  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  interacBon  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   50  
  51. 51. >  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  Bme  stamp  enable  individual  match   –  Call  conversions  matched  back  to  search  terms  March  2011   ©  Datalicious  Pty  Ltd   51  
  52. 52. >  Cross-­‐channel  impact  March  2011   ©  Datalicious  Pty  Ltd   52  
  53. 53. >  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   53  
  54. 54. >  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   54  
  55. 55. >  Adobe  stacking/par?cipa?on   Adobe  can  only  stack   direct  paid  and  organic   responses  that  end  up  on   your  website  proper?es,   mere  banner  impressions   are  missing  from  the  stack   and  cannot  be  included   via  Genesis  ater  the  fact.  March  2011   ©  Datalicious  Pty  Ltd   55  
  56. 56. >  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  banner  impressions   More  granular  &  complex   Less  granular  &  complex  March  2011   ©  Datalicious  Pty  Ltd   56  
  57. 57. >  Combining  data  sources  March  2011   ©  Datalicious  Pty  Ltd   57  
  58. 58. >  Single  source  of  truth  repor?ng   Insights   Repor?ng  March  2011   ©  Datalicious  Pty  Ltd   58  
  59. 59. >  Understanding  channel  mix  March  2011   ©  Datalicious  Pty  Ltd   59  
  60. 60. >  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  AdverBsing   9%   SEO  Generic   7%   Display  AdverBsing   14%   SEM  Generic   14%   Email  MarkeBng   7%   Display  AdverBsing   7%   Retail  PromoBons   14%   Affiliate  MarkeBng   9%   Referrals   5%   Conversions  aoributed  to  search  terms   Email  MarkeBng   7%   that  contain  brand  keywords  and  direct   website  visits  are  most  likely  not  the   originaBng  channel  that  generated  the   awareness  and  as  such  conversion   credits  should  be  re-­‐allocated.    March  2011   ©  Datalicious  Pty  Ltd   61  
  61. 61. >  Adjus?ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10  March  2011   ©  Datalicious  Pty  Ltd   62  
  62. 62. >  Success  aKribu?on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AKrib.   Exclusion   33%   33%   33%   0%   AKrib.   PaKern   30%   20%   20%   30%   AKrib.  March  2011   ©  Datalicious  Pty  Ltd   63  
  63. 63. >  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   64  
  64. 64. Exercise:  AKribu?on  model  March  2011   ©  Datalicious  Pty  Ltd   65  
  65. 65. >  Exercise:  AKribu?on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AKrib.   Exclusion   33%   33%   33%   0%   AKrib.   ?   ?   ?   ?   Custom   AKrib.  March  2011   ©  Datalicious  Pty  Ltd   66  
  66. 66. >  Common  aKribu?on  models  §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  strong   baseline  to  sBmulate  repeat  purchases    §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  direct   response  focus  §  Allocate  more  conversion  credits  to  iniBaBng   touch  points  for  new  and  expensive  brands  and   products  to  insert  them  into  the  mindset  March  2011   ©  Datalicious  Pty  Ltd   67  
  67. 67. >  Media  aKribu?on  phases    §  Phase  1:  De-­‐duplicaBon   –  Conversion  de-­‐duplicaBon  across  all  channels   –  Requires  one  central  reporBng  plaXorm   –  Limited  to  first/last  click  aoribuBon  §  Phase  2:  Direct  response  pathing   –  Response  pathing  across  paid  and  organic  channels   –  Only  covers  clicks  and  not  mere  banner  views   –  Can  be  enabled  in  Google  AnalyBcs  and  Omniture  §  Phase  3:  Full  purchase  path   –  Direct  response  tracking  including  banner  exposure   –  Cannot  be  done  in  Google  AnalyBcs  or  Omniture   –  Easier  to  import  addiBonal  channels  into  ad  server  March  2011   ©  Datalicious  Pty  Ltd   68  
  68. 68. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Targe?ng  March  2011   ©  Datalicious  Pty  Ltd   69  
  69. 69. >  Increase  revenue  by  10-­‐20%     Capture  internet  traffic   Capture  50-­‐100%  of  fair  market  share  of  traffic   Increase  consumer  engagement   Exceed  50%  of  best  compeBtor’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   70  
  70. 70. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  March  2011   ©  Datalicious  Pty  Ltd   71  
  71. 71. >  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   72  
  72. 72. >  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   73  
  73. 73. >  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   markeBng,  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   74  
  74. 74. >  Combining  targe?ng  pla>orms     Off-­‐site   targeBng   Profile   On-­‐site   targeBng   targeBng  March  2011   ©  Datalicious  Pty  Ltd   75  
  75. 75. March  2011   ©  Datalicious  Pty  Ltd   76  
  76. 76. Take  a  closer   look  at  our   cash  flow   solu?ons  March  2011   ©  Datalicious  Pty  Ltd   77  
  77. 77. March  2011   ©  Datalicious  Pty  Ltd   78  
  78. 78. +  Add  website  behaviour  to  submiKed  contact  form  data    March  2011   ©  Datalicious  Pty  Ltd   79  
  79. 79. Take  a  closer   look  at  our   cash  flow   solu?ons  March  2011   ©  Datalicious  Pty  Ltd   80  
  80. 80. Save  ?me  and  get  your   business  insurance  March  2011   ©  Datalicious  Pty  Ltd   online.   81  
  81. 81. Our  Flexi-­‐Premium  car   insurance  can  help  you  March  2011   ©  Datalicious  Pty  Ltd   save.   82  
  82. 82. Save  with  our  combine   Our  Flexi-­‐Premium  car   car  and  life  an  help  you   insurance  c insurance  March  2011   ©  Datalicious  Pty  Ltd   offer.   save.   83  
  83. 83. March  2011   ©  Datalicious  Pty  Ltd   84  
  84. 84. March  2011   ©  Datalicious  Pty  Ltd   85  
  85. 85. It’s  no  accident    March  2011   ©  Datalicious  Pty  Ltd   86   we’re  cheaper  
  86. 86. >  Combining  technology     On-­‐site     Off-­‐site   segments   segments   CRM  March  2011   ©  Datalicious  Pty  Ltd   87  
  87. 87. >  SuperTag  code  architecture     §  Central  JavaScript  container  tag   §  One  tag  for  all  sites  and  plaXorms   §  Hosted  internally  or  externally   §  Faster  tag  implementaBon/updates   §  Eliminates  JavaScript  caching   §  Enables  code  tesBng  on  live  site   §  Enables  heat  map  implementaBon   §  Enables  redirects  for  A/B  tesBng   §  Enables  network  wide  re-­‐targeBng   §  Enables  live  chat  implementaBon  March  2011   ©  Datalicious  Pty  Ltd   88  
  88. 88. >  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   89  
  89. 89. >  Behaviours  plus  transac?ons     Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collecBon  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   predicBve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promoBon  responses   historical  data  from  previous  transacBons   emails,  internal  search,  etc   average  order  value,  points,  etc   Updated  Con?nuously   Updated  Occasionally  March  2011   ©  Datalicious  Pty  Ltd   90  
  90. 90. >  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  overesBmated  visitors  by  up  to  7.6  Bmes  whilst  a  cookie-­‐based  approach  overes?mated  visitors  by  up  to  2.3  ?mes.    March  2011   ©  Datalicious  Pty  Ltd   91   Source:  White  Paper,  RedEye,  2007  
  91. 91. Datalicious  SuperCookie   Persistent  Flash  cookie  that  cannot  be  deleted  March  2011   ©  Datalicious  Pty  Ltd   92  
  92. 92. >  Maximise  iden?fica?on  points    160%  140%  120%  100%   80%   60%   −−−  Probability  of  idenBficaBon  through  Cookies   40%   20%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks  March  2011   ©  Datalicious  Pty  Ltd   93  
  93. 93. >  Maximise  iden?fica?on  points   Mobile   Home   Work   Online   Phone   Branch  March  2011   ©  Datalicious  Pty  Ltd   94  
  94. 94. >  Sample  customer  level  data    March  2011   ©  Datalicious  Pty  Ltd   95  
  95. 95. >  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  transacBonal  history  of   prospects   which  maybe  50%  can  actually  be   with  limited   idenBfied  as  individuals     profile  data  March  2011   ©  Datalicious  Pty  Ltd   96  
  96. 96. >  Poten?al  home  page  layout     Customise  content   Branded  header   delivery  on  the  fly   based  on  referrer   data,  past  content   Rule  based  offer   Login   consumpBon  or   profile  data  for   exisBng  customers.   Targeted   Targeted   offer   offer   Popular     links,     FAQs  March  2011   ©  Datalicious  Pty  Ltd   97  
  97. 97. >  Prospect  targe?ng  parameters    March  2011   ©  Datalicious  Pty  Ltd   98  
  98. 98. >  Affinity  re-­‐targe?ng  in  ac?on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targeBng,     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  hKp://bit.ly/de70b7   12  Month  Caps   - + - +March  2011   ©  Datalicious  Pty  Ltd   99  
  99. 99. >  Ad-­‐sequencing  in  ac?on   MarkeBng  is  about   telling  stories  and   stories  are  not  staBc   but  evolve  over  Bme   Ad-­‐sequencing  can  help  to   evolve  stories  over  Bme  the     more  users  engage  with  ads  March  2011   ©  Datalicious  Pty  Ltd   100  
  100. 100. >  Poten?al  newsleKer  layout     Using  profile  data   Rule  based  branded  header   enhanced  with   website  behaviour   Data  verifica?on   NPS   data  imported  into   the  email  delivery   plaXorm  to  build   Rule  based  offer   business  rules  and   Closest     stores,     customise  content   Profile  based  offer   delivery.   offers     etc  March  2011   ©  Datalicious  Pty  Ltd   101  
  101. 101. >  Customer  profiling  in  ac?on     Using  website  and  email  responses   to  learn  a  liole  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  March  2011   ©  Datalicious  Pty  Ltd   102  
  102. 102. >  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  March  2011   ©  Datalicious  Pty  Ltd   103  
  103. 103. March  2011   ©  Datalicious  Pty  Ltd   104  
  104. 104. >  Poten?al  call  center  interface   Customers  can  also   Call  center  menu  op?ons   be  idenBfied  offline   and  given  most  call   center  plaXorms  are   Customer  contact  history   now  web-­‐based  it   would  be  possible  to   use  online  targeBng   Targeted  offer   Call  script   plaXorms  to  shape   the  call  experience.  March  2011   ©  Datalicious  Pty  Ltd   105  
  105. 105. Exercise:  Targe?ng  matrix  March  2011   ©  Datalicious  Pty  Ltd   106  
  106. 106. Segment  A   Segment  B   Purchase     Media   Data     cycle   channels   points   Default,   awareness   Research,  considera?on   Purchase   intent   Reten?on,  up/Cross-­‐Sell   March  2011   ©  Datalicious  Pty  Ltd   107  
  107. 107. Segment  A   Segment  B   Purchase     Media   Data     cycle   channels   points   Colour,  price,     product  affinity,  etc   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   product  views   Purchase   A  delivers   B  delivers   Website,   Cart  adds,   intent   great  value!   great  value!   emails,  etc   checkouts,  etc   Reten?on,   Why  not   Why  not   Direct  mails,   Email  clicks,  up/Cross-­‐Sell   March  2011   buy  B?   buy  A?   ©  Datalicious  Pty  Ltd   emails,  etc   logins,  108   etc  
  108. 108. >  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   109  
  109. 109. >  ClickTale  tes?ng  case  study    March  2011   ©  Datalicious  Pty  Ltd   110  
  110. 110. >  Bad  campaign  worse  than  none    March  2011   ©  Datalicious  Pty  Ltd   111  
  111. 111. >  Keys  to  effec?ve  targe?ng     1.  Define  success  metrics   2.  Define  and  validate  segments   3.  Develop  targeBng  and  message  matrix     4.  Transform  matrix  into  business  rules   5.  Develop  and  test  content   6.  Start  targeBng  and  automate   7.  Keep  tesBng  and  refining   8.  Communicate  results  March  2011   ©  Datalicious  Pty  Ltd   112  
  112. 112. Contact  us   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twiKer.com/datalicious    March  2011   ©  Datalicious  Pty  Ltd   113  
  113. 113. Data  >  Insights  >  Ac?on  

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