ADMA Digital Analytics

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

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ADMA Digital Analytics

  1. 1. >  ADMA  Digital  Analy-cs  <   Measuring  and  op.mising  digital  
  2. 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. 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. 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. 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. 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. 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. 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. 9. >  Best  of  breed  partners  November  2012   ©  Datalicious  Pty  Ltd   9  
  10. 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. 11. >  Clients  across  all  industries  November  2012   ©  Datalicious  Pty  Ltd   11  
  12. 12. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Metrics  framework  October  2012   ©  Datalicious  Pty  Ltd   12  
  13. 13. October  2012   ©  Datalicious  Pty  Ltd   13  
  14. 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. 15. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac.on)   (Delight)  October  2012   ©  Datalicious  Pty  Ltd   15  
  16. 16. >  Marke-ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  October  2012   ©  Datalicious  Pty  Ltd   16  
  17. 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. 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. 19. October  2012   ©  Datalicious  Pty  Ltd   19  
  20. 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. 21. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   21  
  22. 22. Exercise:  Internal  traffic  October  2012   ©  Datalicious  Pty  Ltd   22  
  23. 23. Exercise:  Custom  segments  October  2012   ©  Datalicious  Pty  Ltd   23  
  24. 24. Google:  “google  analy-cs  custom  variables”   October  2012   ©  Datalicious  Pty  Ltd   24  
  25. 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. 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. 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. 28. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   28  
  29. 29. Exercise:  Conversion  goals  October  2012   ©  Datalicious  Pty  Ltd   29  
  30. 30. Exercise:  Sta-s-cal  significance  October  2012   ©  Datalicious  Pty  Ltd   30  
  31. 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. 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. 33. >  Conversion  metrics  by  category  October  2012   ©  Datalicious  Pty  Ltd   33   Source:  Omniture  Summit,  Ma8  Belkin,  2007  
  34. 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. 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. 36. Exercise:  Metrics  framework  October  2012   ©  Datalicious  Pty  Ltd   36  
  37. 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. 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. 39. >  NPS  survey  and  page  ra-ngs   Page  ra.ngs  October  2012   ©  Datalicious  Pty  Ltd   39  
  40. 40. Google:  “google  analy-cs  custom  events”  October  2012   ©  Datalicious  Pty  Ltd   40  
  41. 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. 42. October  2012   ©  Datalicious  Pty  Ltd   42  
  43. 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. 44. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   44  
  45. 45. Exercise:  Calendar  events  October  2012   ©  Datalicious  Pty  Ltd   45  
  46. 46. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Campaign  tracking  October  2012   ©  Datalicious  Pty  Ltd   46  
  47. 47. October  2012   ©  Datalicious  Pty  Ltd   47  
  48. 48. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   48  
  49. 49. Exercise:  Track  campaigns  October  2012   ©  Datalicious  Pty  Ltd   49  
  50. 50. Google:  “google  analy-cs  url  builder”  October  2012   ©  Datalicious  Pty  Ltd   50  
  51. 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. 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. 53. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   53  
  54. 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. 55. October  2012   ©  Datalicious  Pty  Ltd   55  
  56. 56. Google:  “link  google  analy-cs  webmaster  tools”   October  2012   ©  Datalicious  Pty  Ltd   56  
  57. 57. October  2012   ©  Datalicious  Pty  Ltd   57  
  58. 58. Google:  “link  google  analy-cs  google  adwords”  October  2012   ©  Datalicious  Pty  Ltd   58  
  59. 59. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   59  
  60. 60. Exercise:  Organic  op-misa-on  October  2012   ©  Datalicious  Pty  Ltd   60  
  61. 61. October  2012   ©  Datalicious  Pty  Ltd   61  
  62. 62. October  2012   ©  Datalicious  Pty  Ltd   62  
  63. 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. 64. >  Social  as  the  new  search  October  2012   ©  Datalicious  Pty  Ltd   64  
  65. 65. October  2012   ©  Datalicious  Pty  Ltd   65  
  66. 66. October  2012   ©  Datalicious  Pty  Ltd   66  
  67. 67. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Measuring  brand  October  2012   ©  Datalicious  Pty  Ltd   67  
  68. 68. October  2012   ©  Datalicious  Pty  Ltd   68  
  69. 69. October  2012   ©  Datalicious  Pty  Ltd   69  
  70. 70. October  2012   ©  Datalicious  Pty  Ltd   70  
  71. 71. October  2012   ©  Datalicious  Pty  Ltd   71  
  72. 72. October  2012   ©  Datalicious  Pty  Ltd   72  
  73. 73. October  2012   ©  Datalicious  Pty  Ltd   73  
  74. 74. >  Measuring  brand:  Search  vs.  social   Search   Social   Quan-ty   Quality  October  2012   ©  Datalicious  Pty  Ltd   74  
  75. 75. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  aKribu-on  October  2012   ©  Datalicious  Pty  Ltd   75  
  76. 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. 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. 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. 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. 80. Exercise:  Campaign  flow  October  2012   ©  Datalicious  Pty  Ltd   80  
  81. 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. 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. 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. 84. >  Indirect  display  impact    October  2012   ©  Datalicious  Pty  Ltd   84  
  85. 85. >  Indirect  display  impact    October  2012   ©  Datalicious  Pty  Ltd   85  
  86. 86. >  Indirect  display  impact    October  2012   ©  Datalicious  Pty  Ltd   86  
  87. 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. 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. 89. >  Purchase  path  example  October  2012   ©  Datalicious  Pty  Ltd   89  
  90. 90. October  2012   ©  Datalicious  Pty  Ltd   90  
  91. 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. 92. >  Understanding  channel  mix  October  2012   ©  Datalicious  Pty  Ltd   92  
  93. 93. October  2012   ©  Datalicious  Pty  Ltd   93  
  94. 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. 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. 96. October  2012   ©  Datalicious  Pty  Ltd   96  
  97. 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. 98. >  Adjus-ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10  October  2012   ©  Datalicious  Pty  Ltd   98  
  99. 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. 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. 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. 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. 103. October  2012   ©  Datalicious  Pty  Ltd   103  
  104. 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. 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. 106. >  Purchase  path  for  each  cookie   Mobile   Home   Work   Tablet   Media   Etc  October  2012   ©  Datalicious  Pty  Ltd   106  
  107. 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. 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. 109. Exercise:  AKribu-on  models  October  2012   ©  Datalicious  Pty  Ltd   109  
  110. 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. 111. >  Media  aKribu-on  example   Even/weighted   a8ribu.on   Last  click   a8ribu.on   COST  PER  CONVERSION  October  2012   ©  Datalicious  Pty  Ltd   111  
  112. 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. 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. 114. October  2012   ©  Datalicious  Pty  Ltd   114  
  115. 115. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   115  
  116. 116. Exercise:  Neglected  keywords  October  2012   ©  Datalicious  Pty  Ltd   116  
  117. 117. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Channel  integra-on  October  2012   ©  Datalicious  Pty  Ltd   117  
  118. 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. 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. 120. >  Search  call  to  ac-on  for  offline    October  2012   ©  Datalicious  Pty  Ltd   120  
  121. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 131. >  Combining  targe-ng  plaTorms   On-­‐site     Off-­‐site   targe.ng   targe.ng   CRM  October  2012   ©  Datalicious  Pty  Ltd   131  
  132. 132. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Re-­‐marke-ng  October  2012   ©  Datalicious  Pty  Ltd   132  
  133. 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. 134. October  2012   ©  Datalicious  Pty  Ltd   134  
  135. 135. >  Increase  revenue  by  10-­‐20%    October  2012   ©  Datalicious  Pty  Ltd   135  
  136. 136. October  2012   ©  Datalicious  Pty  Ltd   136  
  137. 137. APPLY  NOW  October  2012   ©  Datalicious  Pty  Ltd   137  
  138. 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. 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. 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. 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. 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. 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. 144. Exercise:  Re-­‐targe-ng  matrix   October  2012   ©  Datalicious  Pty  Ltd   144  
  145. 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. 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. 147. Google:  “enable  remarke-ng  google  analy-cs”  October  2012   ©  Datalicious  Pty  Ltd   147  
  148. 148. Exercise:  Google  Analy-cs  October  2012   ©  Datalicious  Pty  Ltd   148  
  149. 149. Exercise:  Remarke-ng  lists  October  2012   ©  Datalicious  Pty  Ltd   149  
  150. 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. 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  

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