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ADMA Digital Analytics
ADMA Digital Analytics
ADMA Digital Analytics
ADMA Digital Analytics
ADMA Digital Analytics
ADMA Digital Analytics
ADMA Digital Analytics
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ADMA Digital Analytics

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

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

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  • 1. >  ADMA  Digital  Analy-cs  <   Measuring  and  op.mising  digital  
  • 2. >  Digital  analy-cs  course  overview  9  am  start   12.30  pm  30  min  lunch  §  Metrics  framework   §  Channel  integra.on  §  Campaign  tracking   §  Re-­‐marke.ng  15  min  coffee  break   15  min  coffee  break  §  Measuring  brand   §  Landing  pages  §  Media  a8ribu.on   4.30  pm  finish    October  2012   ©  Datalicious  Pty  Ltd   2  
  • 3. >  Digital  analy-cs  course  rules  §  Get  involved  and  be  informal!  §  Ask  ques.ons,  share  experiences  §  Try  to  leave  work  outside  the  door  §  Phones  off  or  on  mute  please  §  Toilet  break  whenever  you  like  §  Different  levels  of  experience  §  Be  open-­‐minded  and  accept  feedback  §  I’m  here  to  cri.cize,  point  out  opportuni.es      October  2012   ©  Datalicious  Pty  Ltd   3  
  • 4. >  Maximising  course  outcome  §  Share  your  expecta.ons  so  I  can  adjust  §  Start  an  ac.on  sheet  to  collect  ideas    §  Main  digital  analy.cs  course  outcomes     –  Define  a  metrics  framework   –  Enable  benchmarking  across  campaigns     –  Effec.vely  incorporate  analy.cs  into  planning   –  Understand  digital  data  sources  and  their  limita.ons   –  Accurately  a8ribute  conversions  across  channels   –  Develop  strategies  to  extend  op.misa.on  past  media   –  Pull  and  interpret  key  reports  in  Google  Analy.cs   –  Impress  with  insights  instead  of  spreadsheets  October  2012   ©  Datalicious  Pty  Ltd   4  
  • 5. >  Introduc-ons  &  expecta-ons  §  Your  name  §  Your  company  §  Your  roles  &  responsibili.es  §  Knowledge  gaps  you’re  hoping  to  fill  §  Something  else  about  yourself   –  Ideal  job   –  Hobbies  October  2012   ©  Datalicious  Pty  Ltd   5  
  • 6. >  About  Datalicious  §  Datalicious  was  founded  in  November  2007  §  Official  Adobe  &  Google  Analy.cs  partner  §  360  data  agency  with  team  of  data  specialists  §  Combina.on  of  analysts  and  developers  §  Blue  chip  clients  across  all  industry  ver.cals  §  Carefully  selected  best  of  breed  partners  §  Driving  industry  best  prac.ce  with  ADMA  §  Turning  data  into  ac.onable  insights  §  Execu.ng  smart  data  driven  campaigns      October  2012   ©  Datalicious  Pty  Ltd   6  
  • 7. >  Smart  data  driven  marke-ng   “Using  data  to  widen  the  funnel”   Media  AKribu-on  &  Modeling   Op-mise  channel  mix,  predict  sales   Targe-ng  &  Merchandising   Increase  relevance,  reduce  churn   Tes-ng  &  Op-misa-on   Remove  barriers,  drive  sales   Boos-ng  ROMI  November  2012   ©  Datalicious  Pty  Ltd   7  
  • 8. >  Wide  range  of  data  services   Data   Insights   Ac-on   PlaTorms   Analy-cs   Campaigns         Data  collec-on  and  processing   Data  mining  and  modelling   Data  usage  and  applica-on         Adobe,  Google  Analy-cs,  etc   Tableau,  Splunk,  SPSS,  R,  etc   SiteCore,  ExactTarget,  etc         Web  and  mobile  analy-cs   Customised  dashboards   Targe-ng  and  merchandising         Tag-­‐less  online  data  capture   Media  aKribu-on  analysis   Marke-ng  automa-on         Retail  and  call  center  analy-cs   Marke-ng  mix  modelling   CRM  strategy  and  execu-on         Big  data  &  data  warehousing   Social  media  monitoring   Data  driven  websites         Single  customer  view   Customer  segmenta-on   Tes-ng  programs  November  2012   ©  Datalicious  Pty  Ltd   8  
  • 9. >  Best  of  breed  partners  November  2012   ©  Datalicious  Pty  Ltd   9  
  • 10. >  Internal  product  development   1  Customer  rela.onship  management  plaform  containing  all  data  necessary  to  manage  campaigns   2  Single  customer  view  plaform  containing  all  data  across  all  (customer)  touch  points     Mass  media   Surveys   Social  media   Campaigns   Digital  media   CRM1   Promo.ons       Measure   Engage       Demographics   Website/apps   Transac.ons   SCV2   Social  media   Campaigns   eDMs/DMs  November  2012   ©  Datalicious  Pty  Ltd   10  
  • 11. >  Clients  across  all  industries  November  2012   ©  Datalicious  Pty  Ltd   11  
  • 12. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Metrics  framework  October  2012   ©  Datalicious  Pty  Ltd   12  
  • 13. October  2012   ©  Datalicious  Pty  Ltd   13  
  • 14. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac-on   Sa-sfac-on   Social  media  October  2012   ©  Datalicious  Pty  Ltd   14  
  • 15. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac.on)   (Delight)  October  2012   ©  Datalicious  Pty  Ltd   15  
  • 16. >  Marke-ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  October  2012   ©  Datalicious  Pty  Ltd   16  
  • 17. >  Standardised  roll-­‐up  metrics   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   Unique  browsers,   Unique  visitors,   Online  sales,   Facebook     search  impressions,   site  engagements,   online  leads,  store     comments,  Tweets,     TV  circula-on,  etc   video  views,  etc   locator  searches,  etc   ra-ngs,  support  calls,  etc   Response  rate,     Conversion  rate,   Review  rate,     Search  response  rate,   engagement  rate,     ra-ng  rate,  comment   TV  response  rate,  etc   checkout  rate,  etc   rate,  NPS  rate,  etc  October  2012   ©  Datalicious  Pty  Ltd   17  
  • 18. >  Provide  context  with  figures   Brand  vs.  direct  response  campaign   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   New  prospects  vs.  exis.ng  customers  October  2012   ©  Datalicious  Pty  Ltd   18  
  • 19. October  2012   ©  Datalicious  Pty  Ltd   19  
  • 20. >  Provide  context  with  figures  §  Brand  vs.  direct  response  campaign  §  New  prospects  vs.  exis.ng  customers  §  Compe..ve  ac.vity,  i.e.  none,  a  lot,  etc  §  Market  share,  i.e.  small,  medium,  large,  et  §  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      October  2012   ©  Datalicious  Pty  Ltd   20  
  • 21. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   21  
  • 22. Exercise:  Internal  traffic  October  2012   ©  Datalicious  Pty  Ltd   22  
  • 23. Exercise:  Custom  segments  October  2012   ©  Datalicious  Pty  Ltd   23  
  • 24. Google:  “google  analy-cs  custom  variables”   October  2012   ©  Datalicious  Pty  Ltd   24  
  • 25. >  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  October  2012   ©  Datalicious  Pty  Ltd   25  
  • 26. >  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  October  2012   ©  Datalicious  Pty  Ltd   26  
  • 27. >  Addi-onal  success  metrics     Click   Through   Use  addi-onal  metrics  closer  to  the  campaign  origin   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $  October  2012   ©  Datalicious  Pty  Ltd   27  
  • 28. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   28  
  • 29. Exercise:  Conversion  goals  October  2012   ©  Datalicious  Pty  Ltd   29  
  • 30. Exercise:  Sta-s-cal  significance  October  2012   ©  Datalicious  Pty  Ltd   30  
  • 31. 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  October  2012   ©  Datalicious  Pty  Ltd   31   Google  “nss  sample  size  calculator”  
  • 32. 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  October  2012   ©  Datalicious  Pty  Ltd   32   Google  “nss  sample  size  calculator”  
  • 33. >  Conversion  metrics  by  category  October  2012   ©  Datalicious  Pty  Ltd   33   Source:  Omniture  Summit,  Ma8  Belkin,  2007  
  • 34. >  Rela-ve  or  calculated  metrics  §  Bounce  rate  §  Conversion  rate  §  Cost  per  acquisi.on  §  Pages  views  per  visit  §  Product  views  per  visit  §  Cart  abandonment  rate  §  Average  order  value  October  2012   ©  Datalicious  Pty  Ltd   34  
  • 35. >  Align  metrics  across  channels  §  Paid  search  response  rate   =  website  visits  /  paid  search  impressions  §  Organic  search  response  rate   =  website  visits  /  organic  search  impressions  §  Display  response  rate     =  website  visits  /  display  ad  impressions  §  Email  response  rate     =  website  visits  /  emails  sent  §  Direct  mail  response  rate     =  (website  visits  +  phone  calls)  /  direct  mail  pieces  sent  §  TV  response  rate     =  (website  visits  +  phone  calls)  /  (TV  ad  reach  x  frequency)  October  2012   ©  Datalicious  Pty  Ltd   35  
  • 36. Exercise:  Metrics  framework  October  2012   ©  Datalicious  Pty  Ltd   36  
  • 37. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac-cal   Funnel   breakdowns  October  2012   ©  Datalicious  Pty  Ltd   37  
  • 38. >  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  October  2012   ©  Datalicious  Pty  Ltd   38  
  • 39. >  NPS  survey  and  page  ra-ngs   Page  ra.ngs  October  2012   ©  Datalicious  Pty  Ltd   39  
  • 40. Google:  “google  analy-cs  custom  events”  October  2012   ©  Datalicious  Pty  Ltd   40  
  • 41. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  October  2012   ©  Datalicious  Pty  Ltd   41  
  • 42. October  2012   ©  Datalicious  Pty  Ltd   42  
  • 43. >  Poten-al  calendar  events  §  Press  releases  §  Sponsored  events  §  Campaign  launches  §  Campaign  changes  §  Crea.ve  changes  §  Price  changes  §  Website  changes  §  Technical  difficul.es  October  2012   ©  Datalicious  Pty  Ltd   43  
  • 44. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   44  
  • 45. Exercise:  Calendar  events  October  2012   ©  Datalicious  Pty  Ltd   45  
  • 46. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Campaign  tracking  October  2012   ©  Datalicious  Pty  Ltd   46  
  • 47. October  2012   ©  Datalicious  Pty  Ltd   47  
  • 48. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   48  
  • 49. Exercise:  Track  campaigns  October  2012   ©  Datalicious  Pty  Ltd   49  
  • 50. Google:  “google  analy-cs  url  builder”  October  2012   ©  Datalicious  Pty  Ltd   50  
  • 51. >  Email  click-­‐through  iden-fica-on   h8p://www.company.com/email-­‐landing-­‐page.html?     utm_id=neNCu&   CustomerID=12345&   Demographics=M|35&   CustomerSegment=A1&   CustomerValue=High&   ProductHistory=A6&   NextBestOffer=A7&   ChurnRisk=Low   [...]  October  2012   ©  Datalicious  Pty  Ltd   51  
  • 52. >  Personalised  URLs  for  direct  mail   ChrisBartens.company.com  >  redirect  to  >  company.com?     utm_id=neND&   CustomerID=12345&   Demographics=M|35&   CustomerSegment=A1&   CustomerValue=High&   ProductHistory=A6&   NextBestOffer=A7&   ChurnRisk=Low   [...]  October  2012   ©  Datalicious  Pty  Ltd   52  
  • 53. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   53  
  • 54. >  Exercise:  Naming  conven-on   Source   Medium   Term   Content   Campaign   Referrer   Medium   Keyword   Crea-ve   Promo-on   google   cpc   search  term  a   red  banner   promo  a   newsleKer   banner   search  term  b   black  banner   promo  b   ?   ?   ?   ?   ?  October  2012   ©  Datalicious  Pty  Ltd   54  
  • 55. October  2012   ©  Datalicious  Pty  Ltd   55  
  • 56. Google:  “link  google  analy-cs  webmaster  tools”   October  2012   ©  Datalicious  Pty  Ltd   56  
  • 57. October  2012   ©  Datalicious  Pty  Ltd   57  
  • 58. Google:  “link  google  analy-cs  google  adwords”  October  2012   ©  Datalicious  Pty  Ltd   58  
  • 59. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   59  
  • 60. Exercise:  Organic  op-misa-on  October  2012   ©  Datalicious  Pty  Ltd   60  
  • 61. October  2012   ©  Datalicious  Pty  Ltd   61  
  • 62. October  2012   ©  Datalicious  Pty  Ltd   62  
  • 63. >  Importance  of  social  media   Search   Company   Promo-on   Consumer   WOM,  blogs,  reviews,   ra-ngs,  communi-es,   social  networks,  photo   sharing,  video  sharing  October  2012   ©  Datalicious  Pty  Ltd   63  
  • 64. >  Social  as  the  new  search  October  2012   ©  Datalicious  Pty  Ltd   64  
  • 65. October  2012   ©  Datalicious  Pty  Ltd   65  
  • 66. October  2012   ©  Datalicious  Pty  Ltd   66  
  • 67. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Measuring  brand  October  2012   ©  Datalicious  Pty  Ltd   67  
  • 68. October  2012   ©  Datalicious  Pty  Ltd   68  
  • 69. October  2012   ©  Datalicious  Pty  Ltd   69  
  • 70. October  2012   ©  Datalicious  Pty  Ltd   70  
  • 71. October  2012   ©  Datalicious  Pty  Ltd   71  
  • 72. October  2012   ©  Datalicious  Pty  Ltd   72  
  • 73. October  2012   ©  Datalicious  Pty  Ltd   73  
  • 74. >  Measuring  brand:  Search  vs.  social   Search   Social   Quan-ty   Quality  October  2012   ©  Datalicious  Pty  Ltd   74  
  • 75. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  aKribu-on  October  2012   ©  Datalicious  Pty  Ltd   75  
  • 76. >  Duplica-on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Email     Email   Blast   PlaTorm   $   Organic   Google   Search   Analy-cs   $  October  2012   ©  Datalicious  Pty  Ltd   76  
  • 77. >  Duplica-on  across  channels     Display   Paid     Display   Organic   impression   search   click   search   $   Ad  server   Ad  server   Ad     cookie   cookie   Server   Bid  mgmt.   Bid     cookie   mgmt.   Analy-cs   Analy-cs   Analy-cs   Web   cookie   cookie   cookie   analy-cs  October  2012   ©  Datalicious  Pty  Ltd   77  
  • 78. >  De-­‐duplica-on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy-cs   PlaTorm   Email     Blast   $   Organic   Search   $  October  2012   ©  Datalicious  Pty  Ltd   78  
  • 79. >  Campaign  flows  are  complex   =  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  October  2012   ©  Datalicious  Pty  Ltd   79  
  • 80. Exercise:  Campaign  flow  October  2012   ©  Datalicious  Pty  Ltd   80  
  • 81. >  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  October  2012   ©  Datalicious  Pty  Ltd   81  
  • 82. >  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    October  2012   ©  Datalicious  Pty  Ltd   82  
  • 83. >  Ad  clicks  inadequate  measure   Only  a  small  minority  of  people  actually  click  on  ads,  the  majority   merely  processes  them  (if  at  all)  like  any  other  adver.sing  without  an   immediate  response  so  adver.sers  cannot  rely  on  clicks  as  the  sole   success  measure  but  should  instead  focus  on  impressions  delivered  October  2012   ©  Datalicious  Pty  Ltd   83  
  • 84. >  Indirect  display  impact    October  2012   ©  Datalicious  Pty  Ltd   84  
  • 85. >  Indirect  display  impact    October  2012   ©  Datalicious  Pty  Ltd   85  
  • 86. >  Indirect  display  impact    October  2012   ©  Datalicious  Pty  Ltd   86  
  • 87. >  Full  purchase  path  tracking   Introducer   Influencer   Influencer   Closer   $   Paid     Display     Organic   Direct     Online   search   ad  clicks   search   site  visits   leads   Display     Affiliate   Social   Emails,   Offline   ad  views   clicks   referrals   direct  mail   sales   TV/print     Organic   Social     Retail     Life-me   responses   search   buzz   visits   profit  October  2012   ©  Datalicious  Pty  Ltd   87  
  • 88. >  Full  purchase  path  tracking   Introducer   Influencer   Influencer   Closer   $   Paid     Display     Organic   Direct     Online   search   ad  clicks   search   site  visits   leads   Display     Affiliate   Social   Emails,   Offline   ad  views   clicks   referrals   direct  mail   sales   TV/print     Organic   Social     Retail     Life-me   responses   search   buzz   visits   profit  October  2012   ©  Datalicious  Pty  Ltd   88  
  • 89. >  Purchase  path  example  October  2012   ©  Datalicious  Pty  Ltd   89  
  • 90. October  2012   ©  Datalicious  Pty  Ltd   90  
  • 91. >  Path  across  different  segments   Introducer   Influencer   Influencer   Closer   $   Product     Channel  1   Channel  2   Channel  3   Channel  4   A  vs.  B   Clients  vs.   Channel  1   Channel  2   Channel  3   Channel  4   prospects   Brand  vs.   Channel  1   Channel  2   Channel  3   Product  4   direct  resp.  October  2012   ©  Datalicious  Pty  Ltd   91  
  • 92. >  Understanding  channel  mix  October  2012   ©  Datalicious  Pty  Ltd   92  
  • 93. October  2012   ©  Datalicious  Pty  Ltd   93  
  • 94. What  promoted  your  visit  today?   q  Recent  branch  visit   q  Saw  an  ad  on  television   q  Saw  an  ad  in  the  newspaper   q  Recommenda.on  from  family/friends   q  […]     How  likely  are  you  to  apply  for  a  loan?   q  Within  the  next  few  weeks   q  Within  the  next  few  months   q  I  am  a  customer  already  October  2012   q  […]   ©  Datalicious  Pty  Ltd   94  
  • 95. >  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  a8ributed  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.    October  2012   ©  Datalicious  Pty  Ltd   95  
  • 96. October  2012   ©  Datalicious  Pty  Ltd   96  
  • 97. >  Website  entry  survey  example   In  this  retail  example,  the   exposure  to  retail  display  ads   was  the  biggest  website  traffic   driver  for  direct  visits  as  well  as   visits  origina.ng  from  search   terms  that  included  branded   keywords  –  before  TV,  word  of   mouth  and  print  ads.  October  2012   ©  Datalicious  Pty  Ltd   97  
  • 98. >  Adjus-ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10  October  2012   ©  Datalicious  Pty  Ltd   98  
  • 99. >  Purchase  path  vs.  aKribu-on  §  Important  to  make  a  dis.nc.on  between  media   a8ribu.on  and  purchase  path  tracking   –  Not  the  same,  one  is  necessary  to  enable  the  other  §  Tracking  the  complete  purchase  path,  i.e.  every  paid   and  organic  campaign  touch  point  leading  up  to  a   conversion  is  a  necessary  requirement  to  be  able  to   actually  do  media  a8ribu.on  or  the  alloca.on  or   conversion  credits  back  to  campaign  touch  points     –  Purchase  path  tracking  is  the  data  collec.on  and     media  a8ribu.on  is  the  actual  analysis  or  modelling      October  2012   ©  Datalicious  Pty  Ltd   99  
  • 100. >  Where  to  track  purchase  path   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  October  2012   ©  Datalicious  Pty  Ltd   100  
  • 101. >  Purchase  path  data  samples  Web  Analy-cs  data  sample  LAST  AD  IMPRESSION  >  SEARCH  >  $$$|  PV  $$$  AD  IMPRESSION  >  AD  IMPRESSION  >  SEARCH  >  $$$    Ad  Server  data  sample  01/01/2012  11:45  AD  IMP  YAHOO  HOME  $33  01/01/2012  12:00  AD  IMP  SMH  FINANCE  $33  01/01/2012  12:05  SEARCH  KEYWORD    -­‐  07/01/2012  17:00  DIRECT        $33  08/01/2012  15:00  $$$        $100  October  2012   ©  Datalicious  Pty  Ltd   101  
  • 102. >  Media  aKribu-on  models   Introducer   Influencer   Influencer   Closer   $   Product     ?%   ?%   ?%   ?%   A  vs.  B   Prospects   ?%   ?%   ?%   ?%   vs.  clients   Brand  vs.   ?%   ?%   ?%   ?%   direct  resp.  October  2012   ©  Datalicious  Pty  Ltd   102  
  • 103. October  2012   ©  Datalicious  Pty  Ltd   103  
  • 104. >  Full  vs.  par-al  purchase  path  data   Display     Display     Email   Search   impression   impression   response   response   $   ✖   ✔   ✔   ✔   Display     Display     Display     Direct     impression   impression   impression   visit   $   ✖   ✖   ✔   ✔   Display     Display     Display     Display     impression   impression   impression   response   $   ✖   ✖   ✔   ✔   Display     Display     Search   Search   impression   impression   response   response   $   ✖   ✔   ✔   ✔  October  2012   ©  Datalicious  Pty  Ltd   104  
  • 105. >  Full  vs.  par-al  purchase  path  data   Display     Display     Email   Search   impression   impression   response   response   $   ✖   ✔   ✔   ✔   Display     impression   5%  to  65%  variance     Display     impression   Display     impression   Direct     visit   $   ✖   in  conversion  aKribu-on     ✖   ✔   ✔   Display     for  different  channels  due  to     Display     Display     Display     $   impression   par-al  purchase  path  data   impression   impression   response   ✖   ✖   ✔   ✔   Display     Display     Search   Search   impression   impression   response   response   $   ✖   ✔   ✔   ✔  October  2012   ©  Datalicious  Pty  Ltd   105  
  • 106. >  Purchase  path  for  each  cookie   Mobile   Home   Work   Tablet   Media   Etc  October  2012   ©  Datalicious  Pty  Ltd   106  
  • 107. >  Media  aKribu-on  models     Display     Display     Display   Search   impression   impression   response   response   $100   Last  click   0%   0%   0%   100%   aKribu-on   Even     25%   25%   25%   25%   aKribu-on   Weighted   X%   X%   Y%   Z%   aKribu-on  October  2012   ©  Datalicious  Pty  Ltd   107  
  • 108. >  Google  Analy-cs  models  §  The  First/Last  Interac-on  model  plus  …  §  The  Linear  model  might  be  used  if  your   campaigns  are  designed  to  maintain   awareness  with  the  customer   throughout  the  en.re  sales  cycle.  §  The  Posi-on  Based  model  can  be  used   to  adjust  credit  for  different  parts  of  the   customer  journey,  such  as  early   interac.ons  that  create  awareness  and   late  interac.ons  that  close  sales.  §  The  Time  Decay  model  assigns  the  most   credit  to  touch  points  that  occurred   nearest  to  the  .me  of  conversion.  It  can   be  useful  for  campaigns  with  short  sales   cycles,  such  as  promo.ons.  October  2012   ©  Datalicious  Pty  Ltd   108  
  • 109. Exercise:  AKribu-on  models  October  2012   ©  Datalicious  Pty  Ltd   109  
  • 110. >  Media  aKribu-on  models   Introducer   Influencer   Influencer   Closer   $   Product     ?%   ?%   ?%   ?%   A  vs.  B   Prospects   ?%   ?%   ?%   ?%   vs.  clients   Brand  vs.   ?%   ?%   ?%   ?%   direct  resp.  October  2012   ©  Datalicious  Pty  Ltd   110  
  • 111. >  Media  aKribu-on  example   Even/weighted   a8ribu.on   Last  click   a8ribu.on   COST  PER  CONVERSION  October  2012   ©  Datalicious  Pty  Ltd   111  
  • 112. >  Media  aKribu-on  example   ?   TV/Print   Even/weighted   a8ribu.on   ?   Internal   ?   ads   Website   content   ?   Email   ?   Last  click   a8ribu.on   Direct   mail   COST  PER  CONVERSION  October  2012   ©  Datalicious  Pty  Ltd   112  
  • 113. >  Media  aKribu-on  example   TOTAL  CONVERSION  VALUE   Increase     spend   Reduce   Increase     spend   spend   ROI  FULL  PURCHASE  PATH  October  2012   ©  Datalicious  Pty  Ltd   113  
  • 114. October  2012   ©  Datalicious  Pty  Ltd   114  
  • 115. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   115  
  • 116. Exercise:  Neglected  keywords  October  2012   ©  Datalicious  Pty  Ltd   116  
  • 117. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Channel  integra-on  October  2012   ©  Datalicious  Pty  Ltd   117  
  • 118. >  Tracking  offline  responses  online  §  Search  calls  to  ac.on  for  TV,  radio,  print   –  Unique  search  term  only  adver.sed  in  print  so  all     responses  from  that  term  must  have  come  from  print  §  PURLs  (personalised  URLs)  for  direct  mail   –  Brand.com/customer-­‐name  redirects  to  new  URL  that   includes    tracking  parameter  iden.fying  response  as  DM  §  Website  entry  survey  for  direct/branded  visits   –  Survey  website  visitors  that  have  come  to  site  directly     or  via  branded  search  about  their  media  habits,  etc  §  Combine  data  sets  into  media  a8ribu.on  model   –  Combine  raw  data  from  online  purchase  path,  website  entry   survey  and  offline  sales  with  offline  media  placement  data  in   tradi.onal  (econometric)  media  a8ribu.on  model  October  2012   ©  Datalicious  Pty  Ltd   118  
  • 119. >  Personalised  URLs  for  direct  mail   ChrisBartens.company.com  >  redirect  to  >  company.com?     utm_id=neND&   Demographics=M|35&   CustomerSegment=A1&   CustomerValue=High&   CustomerSince=2001&   ProductHistory=A6&   NextBestOffer=A7&   ChurnRisk=Low   [...]  October  2012   ©  Datalicious  Pty  Ltd   119  
  • 120. >  Search  call  to  ac-on  for  offline    October  2012   ©  Datalicious  Pty  Ltd   120  
  • 121. >  Econometric  media  modelling   Use  of  tradi.onal  econometric   modelling  to  measure  the   impact  of  communica.ons  on   sales  for  offline  channels  where   it  cannot  be  measured  directly   through  smart  calls  to  ac.on   online  (and  thus  cookie  level   purchase  path  data).  October  2012   ©  Datalicious  Pty  Ltd   121  
  • 122. >  Tracking  offline  sales  online  §  Email  click-­‐through   –  Include  offline  sales  flag  in  1st  email  click-­‐through  URL  a{er   offline  sale  to  track  an  ‘assisted  offline  sales’  conversion  §  First  login  a{er  purchase   –  Similar  to  the  above  method,  however  offline  sales  flag   happens  via  JavaScript  parameter  defined  on  1st  login  §  Unique  phone  numbers   –  Assign  unique  website  numbers  to  responses  from  specific   channels,  search  terms  or  even  individual  visitors  to  match   offline  call  center  results  back  to  online  ac.vity  §  Website  entry  survey  for  purchase  intent   –  Survey  website  visitors  to  at  least  measure  purchase     intent  in  case  actual  offline  sales  cannot  be  tracked  October  2012   ©  Datalicious  Pty  Ltd   122  
  • 123. >  Offline  sales  driven  by  online   Adver-sing     Phone   Fulfilment,   campaign   sales   CRM,  etc   Retail   Confirma-on   sales   email,  1st  login   Website   Online   Online  sales   Virtual  sales     research   sales   confirma-on   confirma-on   Cookie  October  2012   ©  Datalicious  Pty  Ltd   123  
  • 124. >  Email  click-­‐through  iden-fica-on   h8p://www.company.com/email-­‐landing-­‐page.html?     utm_id=neNCu&   CustomerID=12345&   Demographics=M|35&   CustomerSegment=A1&   CustomerValue=High&   ProductHistory=A6&   NextBestOffer=A7&   ChurnRisk=Low   [...]  October  2012   ©  Datalicious  Pty  Ltd   124  
  • 125. >  Login  landing  and  exit  pages   Customer  data  exposed  in  page  or  URL  on  login  or  logout       CustomerID=12345&   Demographics=M|35&   CustomerSegment=A1&   CustomerValue=High&   ProductHistory=A6&   NextBestOffer=A7&   ChurnRisk=Low   [...]  October  2012   ©  Datalicious  Pty  Ltd   125  
  • 126. >  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  October  2012   ©  Datalicious  Pty  Ltd   126  
  • 127. >  Transac-ons  plus  behaviours   CRM  Profile   Site  Behaviour   one-­‐off  collec.on  of  demographical  data     tracking  of  purchase  funnel  stage   +   age,  gender,  address,  etc   browsing,  checkout,  etc   customer  lifecycle  metrics  and  key  dates   tracking  of  content  preferences   profitability,  expira-on,  etc   products,  brands,  features,  etc   predic.ve  models  based  on  data  mining   tracking  of  external  campaign  responses   propensity  to  buy,  churn,  etc   search  terms,  referrers,  etc   historical  data  from  previous  transac.ons   tracking  of  internal  promo.on  responses   average  order  value,  points,  etc   emails,  internal  search,  etc   Updated  Occasionally   Updated  Con-nuously  October  2012   ©  Datalicious  Pty  Ltd   127  
  • 128. >  Customer  profiling  in  ac-on     Using  website  and  email  responses   to  learn  a  li8le  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  October  2012   ©  Datalicious  Pty  Ltd   128  
  • 129. >  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.    October  2012   ©  Datalicious  Pty  Ltd   129   Source:  White  Paper,  RedEye,  2007  
  • 130. >  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  October  2012   ©  Datalicious  Pty  Ltd   130  
  • 131. >  Combining  targe-ng  plaTorms   On-­‐site     Off-­‐site   targe.ng   targe.ng   CRM  October  2012   ©  Datalicious  Pty  Ltd   131  
  • 132. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Re-­‐marke-ng  October  2012   ©  Datalicious  Pty  Ltd   132  
  • 133. >  Importance  of  online  experience   The  consumer  decision  process  is  changing  from  linear  to  circular.   Considera-on     set  now  grows   during  online   Online  research     research  phase   which  increases   importance  of   user  experience   during  that  phase  October  2012   ©  Datalicious  Pty  Ltd   133  
  • 134. October  2012   ©  Datalicious  Pty  Ltd   134  
  • 135. >  Increase  revenue  by  10-­‐20%    October  2012   ©  Datalicious  Pty  Ltd   135  
  • 136. October  2012   ©  Datalicious  Pty  Ltd   136  
  • 137. APPLY  NOW  October  2012   ©  Datalicious  Pty  Ltd   137  
  • 138. >  Network  wide  re-­‐targe-ng   Product  A   Product  B   Product  C   Product  A   Product  B   Product  C   prospect   prospect   prospect   Product  B   Product  C   Product  A   prospect   prospect   prospect   Product  A   Product  B   Product  C   customer   customer   customer  October  2012   ©  Datalicious  Pty  Ltd   138  
  • 139. >  Network  wide  re-­‐targe-ng   Group  wide  campaign  with  approximate  impression  targets  by  product  rather  than  hard  budget  limita-ons   Product  A   Product  B   Product  C   prospect   prospect   prospect   Product  B   Product  C   Product  A   prospect   prospect   prospect   Product  A   Product  B   Product  C   customer   customer   customer  October  2012   ©  Datalicious  Pty  Ltd   139  
  • 140. >  Story  telling  or  ad-­‐sequencing   Introducer   Influencer   Influencer   Closer   $   Message  1   Message  2   Message  3   Message  4   Product  A   Message  1   Message  2   Message  3   Message  4   Product  B   Message  1   Message  2   Message  3   Message  4   Product  C  October  2012   ©  Datalicious  Pty  Ltd   140  
  • 141. >  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  October  2012   ©  Datalicious  Pty  Ltd   141  
  • 142. >  Targe-ng:  Quality  vs.  quan-ty   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  October  2012   ©  Datalicious  Pty  Ltd   142  
  • 143. >  ANZ  home  page  targe-ng   ANZ  home  page   re-­‐targe.ng  and   merchandising   combined  with   landing  page   op.misa.on   delivered  an   increase  in  offer   response  and   conversion  rates   with  an  overall   project  ROI  of   578%  October  2012   ©  Datalicious  Pty  Ltd   143  
  • 144. Exercise:  Re-­‐targe-ng  matrix   October  2012   ©  Datalicious  Pty  Ltd   144  
  • 145. >  Exercise:  Re-­‐targe-ng  matrix   Segmenta-on  based  on:  Search  keywords,   Purchase   display  ad  clicks  and  website  behaviour   Data     Cycle   Points   Default,   Default   awareness   Research,   Product     considera-on   view,  etc   Purchase   Checkout,   intent   chat,  etc   Exis-ng   Login,  email   customer   click,  etc  October  2012   ©  Datalicious  Pty  Ltd   145  
  • 146. >  Exercise:  Re-­‐targe-ng  matrix   Segmenta-on  based  on:  Search  keywords,   Purchase   display  ad  clicks  and  website  behaviour   Data     Cycle   Points   Default   Product  A   Product  B   Default,   Acquisi-on   Acquisi-on   Acquisi-on   Default   awareness   message  D1   message  A1   message  B1   Research,   Acquisi-on   Acquisi-on   Acquisi-on   Product     considera-on   message  D2   message  A2   message  B2   view,  etc   Purchase   Acquisi-on   Acquisi-on   Acquisi-on   Checkout,   intent   message  D3   message  A3   message  B3   chat,  etc   Exis-ng   Cross-­‐sell   Cross-­‐sell   Cross-­‐sell   Login,  email   customer   message  D4   message  A4   message  B4   click,  etc  October  2012   ©  Datalicious  Pty  Ltd   146  
  • 147. Google:  “enable  remarke-ng  google  analy-cs”  October  2012   ©  Datalicious  Pty  Ltd   147  
  • 148. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   148  
  • 149. Exercise:  Remarke-ng  lists  October  2012   ©  Datalicious  Pty  Ltd   149  
  • 150. >  Unique  phone  numbers   2  out  of  3  callers   hang  up  as  they   cannot  get  their     informa.on  fast   enough.     Unique  phone   numbers  can   help  improve   call  experience.  October  2012   ©  Datalicious  Pty  Ltd   150  
  • 151. >  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  October  2012   ©  Datalicious  Pty  Ltd   151  
  • 152. >  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  October  2012   ©  Datalicious  Pty  Ltd   152  
  • 153. >  Website  call  center  integra-on   Segmenta-on  based  on:  Search  keywords,   Purchase   display  ad  clicks  and  website  behaviour   Data     Cycle   Points   Default   Product  A   Product  B   Default,   1300  000  001   1300  000  005   1300  000  009   Default   awareness   Research,   Product     1300  000  002   1300  000  006   1300  000  010   considera-on   view,  etc   Purchase   Checkout,   1300  000  003   1300  000  007   1300  000  011   intent   chat,  etc   Exis-ng   Login,  email   1300  000  004   1300  000  008   1300  000  012   customer   click,  etc  October  2012   ©  Datalicious  Pty  Ltd   153  
  • 154. October  2012   ©  Datalicious  Pty  Ltd   154  
  • 155. October  2012   ©  Datalicious  Pty  Ltd   155  
  • 156. October  2012   ©  Datalicious  Pty  Ltd   156  
  • 157. October  2012   ©  Datalicious  Pty  Ltd   157  
  • 158. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Landing  pages  October  2012   ©  Datalicious  Pty  Ltd   158  
  • 159. Don’t  reinvent  the  wheel  October  2012   ©  Datalicious  Pty  Ltd   159  
  • 160. October  2012   ©  Datalicious  Pty  Ltd   160  
  • 161. >  Anatomy  of  a  perfect  landing  page  1.  Page  headline  and  ad  copy  2.  Clear  and  concise  headlines  3.  Impeccable  grammar  4.  Taking  advantage  of  trust  indicators  5.  Using  a  strong  call  to  ac.on  6.  Bu8ons  and  call  to  ac.on  should  stand  out  7.  Go  easy  on  the  number  of  links  8.  Use  images  and  video  that  relate  to  copy  9.  Keep  it  above  the  fold  at  all  .mes  October  2012   ©  Datalicious  Pty  Ltd   161  
  • 162. October  2012   ©  Datalicious  Pty  Ltd   162  
  • 163. October  2012   ©  Datalicious  Pty  Ltd   163  
  • 164. >  The  holy  trinity  of  tes-ng  1.  The  headline   –  Have  a  headline!   –  Headline  should  be  concrete   –  Headline  should  be  first  thing  visitors  look  at  2.  Call  to  ac-on   –  Don’t  have  too  many  calls  to  ac.on   –  Have  an  ac.onable  call  to  ac.on   –  Have  a  big,  prominent,  visible  call  to  ac.on  3.  Social  proof   –  Logos,  number  of  users,  tes.monials,     case  studies,  media  coverage,  etc    October  2012   ©  Datalicious  Pty  Ltd   164  
  • 165. >  Best  prac-ce  tes-ng  roadmap  §  Phase  1:  A/B  test   –  Test  same  landing   Element  #1:  Prominent  headline   page  content  in   different  layouts  §  Phase  2:  MV  test   Suppor.ng     Element  #2:     –  Test  different  content   content   Call  to  ac.on   element  combina.ons   within  winning  layout   Element  #3:  Social  proof  /  trust  §  Phase  3:  Repeat   –  Hero  vs.  challengers   Terms  and  condi.ons  §  Phase  4:  Re-­‐targe.ng  October  2012   ©  Datalicious  Pty  Ltd   165  
  • 166. >  G&E  Capital  landing  pages   Project  plaforms  used:  Adobe     SiteCatalyst  and  Test&Target   Before   Removal  of  distrac.ons   such  as  naviga.on  and   search  op.ons  resulted     in  increased  response   A{er   rates  with  ROI  of  492%  October  2012   ©  Datalicious  Pty  Ltd   166  
  • 167. >  Macquarie  landing  pages   Project  plaforms  used:  Adobe     Before   SiteCatalyst  and  Test&Target   A{er   The  small  things  count:   Simplifica.on  down  to  1   set  of  bu8ons  resulted  in   increased  response  rate   and  project  ROI  of  547%  October  2012   ©  Datalicious  Pty  Ltd   167  
  • 168. >  A/B  vs.  MV  (Taguchi)  method   Rather  than  tes.ng  all  combina.ons  of  alterna.ve  page  content  (i.e.  A/B   tes.ng),  the  Taguchi  Method  (i.e.  mul.variate  MV  tes.ng)  is  a  way  of   reducing  the  number  of  different  test  scenarios  (recipes)  but  s.ll  yield   useful  test  results.  Essen.ally,  the  op.mal  page  design  is  ‘predicted’     from  the  test  results  by  analysing  which  page  elements  and  element   combina.ons  were  most  influen.al  overall.     Test  elements     Test  alterna-ves     Full  set  of  test   Reduced  Taguchi     (i.e.  parts  of  page)   (i.e.  test  content)   combina-ons  (A/B)   test  scenarios  (MV)   3   2   8   4   7   2   128   8   4   3   81   9   5   4   1024   16  October  2012   ©  Datalicious  Pty  Ltd   168  
  • 169. >  Sufficient  sample  size  for  tests  §  MV  tes.ng  requires  a  greater  volume  of  visitors  than   A/B  tes.ng.  The  volume  required  is  dependent  on:   –  The  number  of  elements  on  the  page  (and  how  many   alterna.ves  for  each  element)   –  Whether  targe.ng  specific  segments  is  part  of  the  test   or  whether  you  want  to  examine  success  by  different   segments  of  traffic   –  Expected  control  page  conversion  rates   –  How  long  you  can  afford  to  have  the  test  in  market   without  viola.ng  the  test  condi.ons   –  Whether  you  can  afford  to  present  the  test  to  all  traffic  October  2012   ©  Datalicious  Pty  Ltd   169  
  • 170. Exercise:  Sta-s-cal  significance  October  2012   ©  Datalicious  Pty  Ltd   170  
  • 171. How  many  click-­‐throughs  do  you  need  to  test  3     landing  pages  if  you  have  30,000  visitors?   How  many  conversions  do  you  need  to  test  3     landing  pages  if  you  have  30,000  visitors?  How  many  click-­‐throughs  do  you  need  to  test  3  landing  pages     if  you  have  30,000  visitors  but  only  expose  10%  to  the  test?  October  2012   ©  Datalicious  Pty  Ltd   171   Google  “nss  sample  size  calculator”  
  • 172. How  many  click-­‐throughs  do  you  need  to  test  3     landing  pages  if  you  have  30,000  visitors?   369  per  test  or  1,107  clicks  in  total   How  many  conversions  do  you  need  to  test  3     landing  pages  if  you  have  30,000  visitors?   369  per  test  or  1,107  conversions  in  total  How  many  click-­‐throughs  do  you  need  to  test  3  landing  pages     if  you  have  30,000  visitors  but  only  expose  10%  to  the  test?   277  per  test  or  831  clicks  in  total  October  2012   ©  Datalicious  Pty  Ltd   172   Google  “nss  sample  size  calculator”  
  • 173. >  Telstra  bundles  pages   Telstra  bundles  page  op.misa.on  combined  call  center  data  (each  page   had  a  unique  phone  number)  with  Adobe  Test&Target  online  data  and   delivered  a  cross-­‐channel  conversion  rate  increase  with  an  ROI  of  647%  October  2012   ©  Datalicious  Pty  Ltd   173  
  • 174. >  Other  tes-ng  considera-ons  §  Avoiding  ‘no  results’  by  making  test  execu.ons   as  obviously  different  as  possible  to  consumers  §  Limit  poten.al  ‘nega.ve’  test  impact  on   conversions  by  limi.ng  the  test  to  a  smaller   sample  size  ini.ally  §  Avoid  launching  tests  during  major  above  the   line  campaign  ac.vity  as  this  might  magnify  any   incremental  gains  of  tested  scenarios  and  the   test  results  can’t  then  be  replicated  in  a  non-­‐ campaign  period  October  2012   ©  Datalicious  Pty  Ltd   174  
  • 175. >  Introducing  hero  vs.  challengers   Hero  #1   New  hero  #2     CTR  =  1%   =  Challenger  #2   Challenger  #1   Challenger  #2   Challenger  #3   Challenger  #4   CTR  =  0.5%   CTR  =  1.5%   CTR  =  1%   CTR  =  1%  October  2012   ©  Datalicious  Pty  Ltd   175  
  • 176. October  2012   ©  Datalicious  Pty  Ltd   176  
  • 177. Exercise:  Op-misa-on  ideas  October  2012   ©  Datalicious  Pty  Ltd   177  
  • 178. October  2012   ©  Datalicious  Pty  Ltd   178  
  • 179. October  2012   ©  Datalicious  Pty  Ltd   179  
  • 180. October  2012   ©  Datalicious  Pty  Ltd   180  
  • 181. October  2012   ©  Datalicious  Pty  Ltd   181  
  • 182. October  2012   ©  Datalicious  Pty  Ltd   182  
  • 183. October  2012   ©  Datalicious  Pty  Ltd   183  
  • 184. October  2012   ©  Datalicious  Pty  Ltd   184  
  • 185. October  2012   ©  Datalicious  Pty  Ltd   185  
  • 186. October  2012   ©  Datalicious  Pty  Ltd   186  
  • 187. October  2012   ©  Datalicious  Pty  Ltd   187  
  • 188. >  Eye  tracking  vs.  mouse  tracking  §  Eye  tracking  pros   §  Mouse  tracking  pros   –  100%  accurate   –  Natural  environment   –  Controlled   –  No  observer  effect   environment   –  Global  par.cipa.on   –  Open  dialogue   –  Low  cost  §  Eye  tracking  cons   §  Mouse  tracking  cons   –  High  costs   –  No  pre-­‐defined  tests   –  Limited  scope   –  No  research  control   –  Observer  effect   –  No  visitor  feedback  October  2012   ©  Datalicious  Pty  Ltd   188  
  • 189. >  Segmented  heat  maps  are  key   Heat  map  for  new  visitors  vs.  exis-ng  customers   Independent  research  shows  84-­‐88%  correla.on  between  mouse  and  eye  movements*  October  2012   ©  Datalicious  Pty  Ltd   189  
  • 190. October  2012   ©  Datalicious  Pty  Ltd   190  
  • 191. >  New  approach  to  web  design  §  Standard  approach   §  Try  something  new   –  Analyst  iden.fies  issue   –  Analyst  iden.fies  issue   and  briefs  agency   and  briefs  agency  (incl.   –  Agency  develops  new   current  heat  maps)   designs,  trashes  some   –  Agency  develops  new   –  Agency  or  developers   designs  and  tests  them   implement  new  design   (predic.ve  heat  maps)   –  Some.mes  mul.ple   –  Winning  designs  are   designs  are  tested     developed  and  tested   (incl.  new  heat  maps)   –  Top  performing  design   is  implemented  October  2012   ©  Datalicious  Pty  Ltd   191  
  • 192. >  New  approach  to  web  design  §  Step  1:  Iden.fy  problem  pages  §  Step  2:  Priori.se  pages  for  tes.ng  §  Step  3:  Pick  page  for  tes.ng  and  op.misa.on  §  Step  4:  Implement  and  analyse  heat-­‐map  §  Step  5:  Design  test  and  brief  crea.ve  agencies  §  Step  6:  Pick  best  designs  with  predic.ve  heat-­‐maps  §  Step  7:  Develop  different  page  execu.ons  §  Step  8:  Execute,  monitor  (and  refine)  test  §  Step  9:  Analyse  test  and  verify  predic.ve  heat-­‐maps  §  Step  10:  Implement  winning  test  design  §  Step  11:  Pick  next  page  &  repeat  steps  3-­‐10  October  2012   ©  Datalicious  Pty  Ltd   192  
  • 193. Targe-ng  before  tes-ng  October  2012   ©  Datalicious  Pty  Ltd   193  
  • 194. Exercise:  Tes-ng  matrix  October  2012   ©  Datalicious  Pty  Ltd   194  
  • 195. >  Exercise:  Tes-ng  matrix   Test   Segment   Content   Success   Difficulty   Poten-al  October  2012   ©  Datalicious  Pty  Ltd   195  
  • 196. >  Exercise:  Tes-ng  matrix   Test   Segment   Content   Success   Difficulty   Poten-al   Offer  1A   Test  1   Product  1   Offer  1B   Clicks   Low   $100k   Offer  1C   Offer  2A   Test  2   Product  2   Offer  2B   Clicks   High   $100k   Offer  2C  October  2012   ©  Datalicious  Pty  Ltd   196  
  • 197. >  Response  website  design   Through  fluid  grids  and  media  query  adjustments,   responsive  design  enables  web  page  layouts  to  adapt   to  a  variety  of  screen  sizes.  The  content  of  the  page   does  not  change,  just  the  way  it  is  displayed  for  each   screen  size.  October  2012   ©  Datalicious  Pty  Ltd   197  
  • 198. October  2012   ©  Datalicious  Pty  Ltd   198  
  • 199. October  2012   ©  Datalicious  Pty  Ltd   199  
  • 200. >  Online  form  best  prac-ce   Maximise  data  integrity   Age  vs.  year  of  birth   Free  text  vs.  op.ons   Use  auto-­‐complete     wherever  possible  October  2012   ©  Datalicious  Pty  Ltd   200  
  • 201. >  Social  single-­‐sign  on  services   h8p://vimeo.com/16469480     Gigya.com   Janrain.com  October  2012   ©  Datalicious  Pty  Ltd   201  
  • 202. >  Garbage  in,  garbage  out   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.”  October  2012   ©  Datalicious  Pty  Ltd   202  
  • 203. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  About  Datalicious  October  2012   ©  Datalicious  Pty  Ltd   203  
  • 204. >  Short  but  sharp  history  §  Datalicious  was  founded  in  November  2007  §  Official  Adobe  &  Google  Analy.cs  partner  §  360  data  agency  with  team  of  data  specialists  §  Combina.on  of  analysts  and  developers  §  Blue  chip  clients  across  all  industry  ver.cals  §  Carefully  selected  best  of  breed  partners  §  Driving  industry  best  prac.ce  with  ADMA  §  Turning  data  into  ac.onable  insights  §  Execu.ng  smart  data  driven  campaigns      October  2012   ©  Datalicious  Pty  Ltd   204  
  • 205. >  Smart  data  driven  marke-ng   “Using  data  to  widen  the  funnel”   Media  AKribu-on  &  Modeling   Op-mise  channel  mix,  predict  sales   Targe-ng  &  Merchandising   Increase  relevance,  reduce  churn   Tes-ng  &  Op-misa-on   Remove  barriers,  drive  sales   Boos-ng  ROMI  October  2012   ©  Datalicious  Pty  Ltd   205  
  • 206. >  Wide  range  of  data  services   Data   Insights   Ac-on   PlaTorms   Analy-cs   Campaigns         Data  collec-on  and  processing   Data  mining  and  modelling   Data  usage  and  applica-on         Adobe,  Google  Analy-cs,  etc   Tableau,  Splunk,  SPSS,  etc   Alterian,  SiteCore,  Inxmail,  etc         Web  and  mobile  analy-cs   Customised  dashboards   Targe-ng  and  merchandising         Tag-­‐less  online  data  capture   Media  aKribu-on  analysis   Marke-ng  automa-on         Retail  and  call  center  analy-cs   Media  mix  modelling   CRM  strategy  and  execu-on         Data  warehouse  solu-ons   Social  media  monitoring   Data  driven  websites         Single  customer  view   Customer  segmenta-on   Tes-ng  programs  October  2012   ©  Datalicious  Pty  Ltd   206  
  • 207. >  Over  50  years  of  experience   Chris.an  Bartens   Elly  Gillis   Michael  Savio   Chaoming  Li   Founder  &  Director   General  Manager   Head  of  Insights   Head  of  Data           §  Bachelor  of  Business   §  Bachelor  of   §  Bachelor  of  Arts  &   §  Bachelor  of     Management  with   Communica.ons  with   Science  with  applied   Technology  with   marke.ng  focus   print  and  digital  focus   mathema.cs  focus   microelectronics  focus   §  Web  analy.cs  and   §  Digital  marke.ng  and   §  CRM  and  marke.ng   §  So{ware  and  website   digital  marke.ng     project  management   research  and  analy.cs   development  work   work  experience   work  experience   work  experience   experience   §  Space2go,  E-­‐Lo{,   §  M&C  Saatchi,  Mark,   §  ANZ  Bank,  Australian   §  Standards  Australia,     Tourism  Australia   Holler,  Tequila,  IAG,     Bureau  of  Sta.s.c,   DF  Securi.es,  Globiz,   §  SuperTag  founder,   OneDigital,  Telstra   DBM  Consultants   Etang   ADMA  Analy.cs  Chair,   §  Australian  gold  medal   §  ADMA  lecturer  on   §  Developing  his  own   I-­‐COM  Board  Member   in  surf  boat  rowing   marke.ng  tes.ng   CMS  plaform           LinkedIn  profile   LinkedIn  profile   LinkedIn  profile   LinkedIn  profile  October  2012   ©  Datalicious  Pty  Ltd   207  
  • 208. >  Best  of  breed  partners  October  2012   ©  Datalicious  Pty  Ltd   208  
  • 209. >  Clients  across  all  industries  October  2012   ©  Datalicious  Pty  Ltd   209  
  • 210. >  Great  customer  feedback  “[…]  Datalicious  quickly  earned  our  respect  and  confidence  […]  understand  our  business  needs,  deliver  value,  push  our  thinking  […].  Likeable,  transparent  and  trustworthy.  I  would  be  happy  to  recommend  Datalicious  to  anyone.”  Murray  Howe,  Execu.ve  Manager,  Suncorp  Group    "[…]  Datalicious  brought  with  them  best  prac@ce  analy@cs  to  demonstrate  the  true  value  of  our  marke@ng  dollars  […]  have  become  a  cri;cal  business  partner  […]  provided  great  insights  which  have  driven  key  business  decisions.”  Trang  Young,  Senior  Marke.ng  Manager,  E*Trade  Australia      “The  Datalicious  guys  are  great  to  work  along  side  […]  no  stone  unturned  approach  to  finding  solu@ons  to  challenges  […]  knowledge  and  passion  for  web  analy@cs  and  best  of  breed  web  op;miza;on  was  second  to  none”  Steve  Brown,  Senior  Business  Analyst,  Vodafone      “[…]  The  Vodafone  implementa@on  of  SiteCatalyst  is  one  of  the  most  impressive    I  have  seen  and  ranks  in  the  top  10  […].  It  is  an  amazing  founda@on  for  taking  ac@on  on  the  data  and  improving  ROI.”  Adam  Greco,  Consul.ng  Lead,  Omniture  October  2012   ©  Datalicious  Pty  Ltd   210  
  • 211. >  Great  customer  feedback  "[…]  Datalicious  understand  the  value  of  informa@on  and  how  to  leverage  it  using  best  of  breed  soFware.  I  would  recommend  the  team  without  hesita@on  [...]."  James  Fleet,  Marke.ng  Director,  Appliances  Online    "[...]  Datalicious  have  been  in;mately  involved  in  building  our  analy;cs  solu;on.  Most  importantly  their  knowledge  of  best  prac@ce  combined  with  innova@ve  solu@ons  has  allowed  our  business  to  remain  nimble  and  current.  They  are  also  nice  guys."  Tzvi  Balbin,  Group  Digital  Marke.ng  Lead,  Catch  of  the  Day    "[...]  Datalicious  are  helping  us  to  move  from  a  last  click  campaign  measurement  model  to  a  more  accurate  media  aGribu@on  approach.  [...]  poten;al  to  significantly  change  our  media  planning  [...].  Highly  recommended."  Keith  Mirgis,  Senior  Digital  &  Social  Media  Marke.ng  Manager,  Telstra    "We  engaged  Datalicious  to  support  a  strategic  change  in  our  business  [...]  understand  our  customers  [and  their  transac@ons]  beGer  to  ensure  we  retained  as  many  as  possible  [...]"  Natalie  Farrell,  Direct  Marke.ng  Manager,  Luxoƒca  October  2012   ©  Datalicious  Pty  Ltd   211  
  • 212. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  About  SuperTag  October  2012   ©  Datalicious  Pty  Ltd   212  
  • 213. >  The  Datalicious  SuperTag   Easily  implement  and  update   Conversion   any  tag  on  any  websites  without   Tracking   or  limited  IT  involvement   Any   Conversion   JavaScript   De-­‐duping     De-­‐duplicate  conversions  for   CPA  deals  and  align  repor.ng   figures  across  plaforms   Web   Media   Analy-cs   SuperTag   AKribu-on       Collect  accurate  mul.-­‐channel   media  a8ribu.on  data  to   provide  advanced  insights   Live     Behavioral   Chat   Targe-ng     A/B  Tes-ng   Enable  advanced  features  such   Heat  Maps   as  targe.ng,  tes.ng  and  chat  to   op.mise  user  experience  October  2012   ©  Datalicious  Pty  Ltd   213  
  • 214. >  Unique  SuperTag  architecture   T   Injec.ng  JavaScript  tags  into  the   page  based  on  business  rules  using   SuperTag   the  SuperTag  top  and  bo8om  containers.       The  SuperTag  top  and  bo8om  containers  are   JavaScript  func.ons  called  in  the  page  code   just  a{er  the  opening  <body>  tag  and  just   before  the  closing  </body>  tag  on  all     page  across  all  domains.     §  One  tag  for  all  sites  and  plaforms   B   §  Hosted  internally  or  externally   §  Fast  tag  implementa.on/updates   §  Increase  analy.cs  data  accuracy   superT.t()   T   T   §  Enables  code  tes.ng  on  live  site   §  Enables  heat  map  implementa.on   §  Enables  A/B  and  MV  test  execu.on   §  Enables  cross-­‐channel  re-­‐targe.ng   superT.b()   B   B   §  Enables  phone  number  targe.ng  October  2012   ©  Datalicious  Pty  Ltd   214  
  • 215. >  Overcoming  team  barriers   Marke-ng   SuperTag   Technology   Easy  to  use  online  user  interface  enabling  marketers  to     manage  tags  without  intensive  technology  support  October  2012   ©  Datalicious  Pty  Ltd   215  
  • 216. >  Cross-­‐plaTorm  integra-on   Web  Analy-cs   CRM/eDMs   Heat  Maps   Paid  Search   Targe-ng   SuperTag   Ad  Servers   Live  Chat   Affiliates   Tes-ng   DFPs   Centralised  uniform  business  rules  to  trigger  conversions   and  segment  visitors  across  mul.ple  marke.ng  plaforms  October  2012   ©  Datalicious  Pty  Ltd   216  
  • 217. >  Conversion  de-­‐duplica-on   Paid     Bid     search   Mgmt   $   $   Display   Ad     ads   server   $   SuperTag   $   Affiliate   Affiliate   referral   system   $   $   Centralised  business  rules  to  enable  accurate  conversion     de-­‐duplica.on  across  mul.ple  marke.ng  plaforms  October  2012   ©  Datalicious  Pty  Ltd   217  
  • 218. Easy  to  use  drag  &  drop  interface  to  manage  tags   October  2012   ©  Datalicious  Pty  Ltd   218  
  • 219. Flexible  business  rule  builder  to  suit  all  scenarios   October  2012   ©  Datalicious  Pty  Ltd   219  
  • 220. Implement  &  maintain  web  analy-cs  without  IT   October  2012   ©  Datalicious  Pty  Ltd   220  
  • 221. New  more  powerful  re-­‐targe-ng  segment  builder   October  2012   ©  Datalicious  Pty  Ltd   221  
  • 222. October  2012   ©  Datalicious  Pty  Ltd   222  
  • 223. Turn  any  page  element  into  data  or  tes-ng  areas   October  2012   ©  Datalicious  Pty  Ltd   223  
  • 224. >  SuperTag  deployment  op-ons   Manual     Email/FTP   JavaScript     Client     JavaScript   JavaScript   hos-ng  on   website   management   publishing   client  server   Dedicated     JavaScript     CDN  =  Content   Client     Github  client     hos-ng  on   delivery  network   website   code  archive   client  CDN   SuperTag   Real-­‐-me   JavaScript     Client     JavaScript   JavaScript   hos-ng  on   website   management   publishing   SuperTag  CDN  October  2012   ©  Datalicious  Pty  Ltd   224  
  • 225. >  Unique  selling  points  (USPs)  §  Superior  plaform  architecture  for  more  flexibility   –  Turn  any  page  element  into  variables  for  data     collec.on  or  business  rules  for  tag  execu.on   –  Cross-­‐plaform  integra.on  and  data  exchange     –  Splunk  integra.on  for  advanced  data  mining  §  Superior  tes.ng,  deployment  and  audit  features   –  Tes.ng  of  tags  &  business  rules  on  the  live  website   –  Complete  audit  trail  of  all  tag  changes  and  tests  §  No  lock-­‐in,  stop  using  the  SuperTag  at  any  .me   –  External  and  internal  JavaScript  hos.ng  available   –  Perpetual  JavaScript  usage  rights  &  Github  archive  §  All  inclusive  pricing  structure  incen.vizing  use  October  2012   ©  Datalicious  Pty  Ltd   225  
  • 226. >  Blue  chip  SuperTag  clients  October  2012   ©  Datalicious  Pty  Ltd   226  
  • 227. >  Great  customer  feedback  "Managing  third  party  tags  has  never  been  easier  [...]  simplicity  of  seSng  business  rules  [...]  reduc;on  in  CPA  [...].“  Jason  Lima,  Online  Marke.ng,  IMB    "[...]  SuperTag  tool  is  so  easy  to  use  [...].  Live  tes@ng  is  par@cularly  useful  [...]  highly  recommended  [...]."  Helene  Cameron-­‐Heslop,  Analyst,  Appliances  Online    "SuperTag  speeds  up  tag  implementa@on  and  gives  us  increased  flexibility  [...]  manage  media  and  website  analy;cs  [...].”  Alex  Crompton,  Head  of  Digital,  Aussie  October  2012   ©  Datalicious  Pty  Ltd   227  
  • 228. Contact  us   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twiKer.com/datalicious    October  2012   ©  Datalicious  Pty  Ltd   228  
  • 229. Data  >  Insights  >  Ac-on  October  2012   ©  Datalicious  Pty  Ltd   229  

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