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Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
Analyze to Optimize (Part 2)
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Analyze to Optimize (Part 2)

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The presentation discusses training on data, measurement and ROI.

The presentation discusses training on data, measurement and ROI.

Published in: Technology
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  • 1. >  Analyse  to  op-mise  <   ADMA  short  course  on  data,     measurement  and  ROI  
  • 2. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Quick  recap    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   2  
  • 3. >  Day  1:  Basic  Analy-cs    §  Defining  a  metrics  framework   –  What  to  report  on,  when  and  why?   –  Matching  strategic  and  tacHcal  goals  to  metrics   –  Covering  all  major  categories  of  business  goals  §  Finding  and  developing  the  right  data   –  Data  sources  across  channels  and  goals   –  Meaningful  trends  vs.  100%  accurate  data   –  Human  and  technological  limitaHons  §  Plus  hands-­‐on  exercises  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   3  
  • 4. >  Day  1:  Basic  Analy-cs    §  Hands-­‐on  exercises  and  examples   –  Funnel  breakdowns   –  Conversions  metrics   –  Metrics  framework   –  Search  insights   –  DuplicaHon  impact   –  StaHsHcal  significance  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   4  
  • 5. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Course  overview    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   5  
  • 6. >  Day  2:  Advanced  Analy-cs    §  Campaign  flow  and  media  aWribuHon   –  Designing  a  campaign  flow  including  metrics   –  Omniture  vs.  Google  AnalyHcs  capabiliHes  §  How  to  reduce  media  waste   –  TesHng  and  targeHng  in  a  media  world   –  Media  vs.  content  and  usability  §  Plus  hands-­‐on  exercises  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   6  
  • 7. >  Get  the  most  out  of  the  course     Category   Data   Metrics   Insights   PlaForm   Why?   What?   How?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   7  
  • 8. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  aJribu-on    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   8  
  • 9. >  Campaign  flow  and  calls  to  ac-on     =  Paid  media   Organic     PR,  WOM,   search   events,  etc   =  Viral  elements   =  Coupons,  surveys   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   C1   C2   CRM   Facebook   program   TwiJer,  etc   C3   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   9  
  • 10. Exercise:  Campaign  flow  
  • 11. Exercise:  Calls  to  ac-on  
  • 12. >  Exercise:  Calls  to  ac-on    §  Unique  click-­‐through  URLs  §  Unique  vanity  domains  or  URLs  §  Unique  phone  numbers  §  Unique  search  terms  §  Unique  email  addresses  §  Unique  personal  URLs  (PURLs)  §  Unique  SMS  numbers,  QR  codes  §  Unique  promoHonal  codes,  vouchers  §  Geographic  locaHon  (Facebook,  FourSquare)  §  Regression  analysis  of  cause  and  effect  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   12  
  • 13. >  Search  call  to  ac-on  for  offline    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   13  
  • 14. hJp://www.domain.com?campaign=outdoor  
  • 15. >  Reach  and  channel  overlap     TV     audience   Banner   Search   audience   audience  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   16  
  • 16. >  Indirect  display  impact    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   17  
  • 17. >  Indirect  display  impact    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   18  
  • 18. >  Indirect  display  impact    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   19  
  • 19. >  De-­‐duplica-on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Central   Analy-cs   PlaForm   Email     Email   Blast   PlaForm   $   Organic   Google   Search   Analy-cs   $  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   20  
  • 20. De-­‐duplica-on  across  channels  
  • 21. >  Success  aJribu-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  2010   ©  ADMA  &  Datalicious  Pty  Ltd   22  
  • 22. >  First  and  last  click  aJribu-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  2010   ©  ADMA  &  Datalicious  Pty  Ltd   23  
  • 23. >  Paid  and  organic  stacking    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   24  
  • 24. >  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Generic   Click   Visit   Branded   $   Banner     SEO   Affiliate   Social   View   Generic   Click   Media   $   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   25  
  • 25. >  Where  to  collect  the  data     Ad  Server   Web  Analy-cs   Banner  impressions   Referral  visits   Banner  clicks   Social  media  visits   +   Organic  search  visits   Paid  search  clicks   Paid  search  visits   Other  paid  visits   Email  visits   Paid  Impressions/Clicks   Paid/Organic  Visits  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   26  
  • 26. >  Success  aJribu-on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AJrib.   Exclusion   33%   33%   33%   0%   AJrib.   PaJern   30%   20%   20%   30%   AJrib.  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   27  
  • 27. Exercise:  AJribu-on  model  
  • 28. >  Exercise:  AJribu-on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AJrib.   Exclusion   33%   33%   33%   0%   AJrib.   ?   ?   ?   ?   Custom   AJrib.  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   29  
  • 29. >  Exercise:  AJribu-on  model    §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  strong   baseline  to  sHmulate  repeat  purchases    §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  direct   response  focus  §  Allocate  more  conversion  credits  to  iniHaHng   touch  points  for  new  and  expensive  brands  and   products  to  insert  them  into  the  mindset  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   30  
  • 30. >  Understanding  channel  overlap    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   31  
  • 31. >  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  AdverHsing   9%   SEO  Generic   7%   Display  AdverHsing   14%   SEM  Generic   14%   Email  MarkeHng   7%   Display  AdverHsing   7%   Retail  PromoHons   14%   Affiliate  MarkeHng   9%   Referrals   5%   Conversions  aWributed  to  search  terms   Email  MarkeHng   7%   that  contain  brand  keywords  and  direct   website  visits  are  most  likely  not  the   originaHng  channel  that  generated  the   awareness  and  as  such  conversion   credits  should  be  re-­‐allocated.    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   32  
  • 32. >  Ad  server  exposure  test     1   User  qualifies  for  the  display  campaign   (if  the  user  has  already  been  tagged  go  to  step  3)   1st  impression   2   Audience  Segmenta-on   10%  of  users  in  control  group,  90%  in  exposed  group   User  tagged  with  segment   Measurement:   Conversions  per   1000  unique   Control   Exposed   visitors   (displayed  non-­‐branded  message)   (displayed  branded  message)   User  remains  in  segment   N  impressions   3   Control   Exposed   (displayed  non-­‐branded  message)   (displayed  branded  message)  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   33  
  • 33. >  Research  online,  shop  offline    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   34   Source:  2008  Digital  Future  Report,  Surveying  The  Digital  Future,  Year  Seven,  USC  Annenberg  School  
  • 34. >  Offline  sales  driven  by  online   Adver-sing     Phone   Credit  check,   campaign   order   fulfilment   Retail   Confirma-on   order   email   Website   Online   Online  order   Virtual  order   research   order   confirma-on   confirma-on   Cookie  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   35  
  • 35. Exercise:  Offline  conversions  
  • 36. >  Exercise:  Offline  conversions    §  Email  click-­‐through  aner  purchase  §  First  online  login  aner  purchase  §  Unique  website  phone  number  §  Call  back  request  or  online  chat  §  Unique  website  promoHon  code  §  Unique  printable  vouchers  §  Store  locator  searches  §  Make  an  appointment  online  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   37  
  • 37. >  Media  aJribu-on  phases    §  Phase  1:  De-­‐duplicaHon   –  Conversion  de-­‐duplicaHon  across  all  channels   –  Requires  one  central  reporHng  plaoorm   –  Limited  to  first/last  click  aWribuHon  §  Phase  2:  Direct  response  pathing   –  Response  pathing  across  paid  and  organic  channels   –  Only  covers  clicks  and  not  mere  banner  views   –  Can  be  enabled  in  Google  AnalyHcs  and  Omniture  §  Phase  3:  Full  purchase  path   –  Direct  response  tracking  including  banner  exposure   –  Cannot  be  done  in  Google  AnalyHcs  or  Omniture   –  Easier  to  import  addiHonal  channels  into  ad  server  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   38  
  • 38. >  Recommended  resources    §  200812  ComScore  How  Online  AdverHsing  Works  §  200905  iProspect  Research  Study  Search  And  Display  §  200904  ClearSaleing  American  AWribuHon  Index  §  201003  Datalicious  Tying  Offline  Sales  To  Online  Media  §  Google:  “Forrester  Campaign  AWribuHon  Framework  PDF”  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   39  
  • 39. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Reducing  waste    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   40  
  • 40. >  Reducing  waste  along  funnel     Media  aJribu-on   Op-mising  channel  mix   Targe-ng     Increasing  relevance   Tes-ng   Improving  usability   $$$  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   41  
  • 41. >  Increase  revenue  by  10-­‐20%     Capture  internet  traffic   Capture  50-­‐100%  of  fair  market  share  of  traffic   Increase  consumer  engagement   Exceed  50%  of  best  compeHtor’s  engagement  rate     Capture  qualified  leads  and  sell   Convert  10-­‐15%  to  leads  and  of  that  20%  into   sales   Building  consumer  loyalty   Build  60%  loyalty  rate  and  40%  sales  conversion   Increase  online  revenue   Earn  10-­‐20%  incremental  revenue  online  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   42  
  • 42. >  The  consumer  data  journey     To  transac-onal  data   To  reten-on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   43  
  • 43. >  Coordina-on  across  channels         Genera-ng   Crea-ng   Maximising   awareness   engagement   revenue   TV,  radio,  print,   Retail  stores,  in-­‐store   Outbound  calls,  direct   outdoor,  search   kiosks,  call  centers,   mail,  emails,  social   markeHng,  display   brochures,  websites,   media,  SMS,  mobile   ads,  performance   mobile  apps,  online   apps,  etc   networks,  affiliates,   chat,  social  media,  etc   social  media,  etc   Off-­‐site   On-­‐site   Profile     targe-ng   targe-ng   targe-ng  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   44  
  • 44. >  Combining  targe-ng  plaForms     Off-­‐site   targeHng   Profile   On-­‐site   targeHng   targeHng  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   45  
  • 45. >  Combining  technology     On-­‐site     Off-­‐site   segments   segments  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   48  
  • 46. >  Extended  targe-ng  plaForm     Publishers   Partners   Network   Brand  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   49  
  • 47. >  SuperTag  code  architecture     §  Central  JavaScript  container  tag   §  One  tag  for  all  sites  and  plaoorms   §  Hosted  internally  or  externally   §  Faster  tag  implementaHon/updates   §  Eliminates  JavaScript  caching   §  Enables  code  tesHng  on  live  site   §  Enables  heat  map  implementaHon   §  Enables  redirects  for  A/B  tesHng   §  Enables  network  wide  re-­‐targeHng   §  Enables  live  chat  implementaHon  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   50  
  • 48. >  Combining  data  sets     Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   51  
  • 49. >  Behaviours  plus  transac-ons     Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collecHon  of  demographical  data     +   browsing,  checkout,  etc   age,  gender,  address,  etc   tracking  of  content  preferences   customer  lifecycle  metrics  and  key  dates   products,  brands,  features,  etc   profitability,  expira-on,  etc   tracking  of  external  campaign  responses   predicHve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promoHon  responses   historical  data  from  previous  transacHons   emails,  internal  search,  etc   average  order  value,  points,  etc   Updated  Con-nuously   Updated  Occasionally  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   52  
  • 50. >  Maximise  iden-fica-on  points    160%  140%  120%  100%   80%   60%   −−−  Probability  of  idenHficaHon  through  Cookies   40%   20%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   53  
  • 51. >  Sample  customer  level  data    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   54  
  • 52. >  Sample  site  visitor  composi-on     30%  new  visitors  with  no   30%  repeat  visitors  with   previous  website  history   referral  data  and  some   aside  from  campaign  or   website  history  allowing   referrer  data  of  which   50%  to  be  segmented  by   maybe  50%  is  useful   content  affinity   30%  exis-ng  customers  with  extensive   10%  serious   profile  including  transacHonal  history  of   prospects   which  maybe  50%  can  actually  be   with  limited   idenHfied  as  individuals     profile  data  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   55  
  • 53. >  Poten-al  home  page  layout     Customise  content   Branded  header   delivery  on  the  fly   based  on  referrer   data,  past  content   Rule  based  offer   Login   consumpHon  or   profile  data  for   exisHng  customers.   Targeted   Targeted   offer   offer   Popular     links,     FAQs  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   56  
  • 54. >  Prospect  targe-ng  parameters    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   57  
  • 55. >  Affinity  targe-ng  in  ac-on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targeHng,     response  rates  are     lined  significantly     across  products.   CTR  By  Category  Affinity   Message   Postpay   Prepay   Broadb.   Business   Blackberry  Bold   - - - + Google:  “vodafone   5GB  Mobile  Broadband   - - + - omniture  case  study”     Blackberry  Storm   + - + + or  hJp://bit.ly/de70b7   12  Month  Caps   - + - +October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   58  
  • 56. >  Poten-al  newsleJer  layout     Using  profile  data   Rule  based  branded  header   enhanced  with   website  behaviour   Data  verifica-on   NPS   data  imported  into   the  email  delivery   plaoorm  to  build   Rule  based  offer   business  rules  and   Closest     stores,     customise  content   Profile  based  offer   delivery.   offers     etc  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   59  
  • 57. >  Customer  profiling  in  ac-on     Using  website  and  email  responses   to  learn  a  liWle  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   60  
  • 58. >  Poten-al  landing  page  layout     Passing  data  on  user   Rule  based  branded  header   preferences  through   to  the  website  via   parameters  in  email   Campaign  message  match   click-­‐through  URLs     to  customise   content  delivery.   Targeted  offer   Call  to  ac-on  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   61  
  • 59. Exercise:  Targe-ng  matrix  
  • 60. >  Exercise:  Targe-ng  matrix     Phase   Segment  A/B   Channels   Data  Points   Awareness   Considera-on   Purchase  Intent   Up/Cross-­‐Sell  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   63  
  • 61. >  Exercise:  Targe-ng  matrix     Phase   Segment  A/B   Channels   Data  Points   Social,  display,   Awareness   Seen  this?   Default   search,  etc   Social,  search,   Download,   Considera-on   Great  feature!   website,  etc   product  view   Search,  site,   Cart  add,   Purchase  Intent   Great  value!   emails,  etc   checkout,  etc   Direct  mail,   Email  response,   Up/Cross-­‐Sell   Add  this!   emails,  etc   login,  etc  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   64  
  • 62. >  Quality  content  is  key     Avinash  Kaushik:     “The  principle  of  garbage  in,  garbage  out   applies  here.  […  what  makes  a  behaviour   targe;ng  pla<orm  ;ck,  and  produce  results,  is   not  its  intelligence,  it  is  your  ability  to  actually   feed  it  the  right  content  which  it  can  then  target   [….  You  feed  your  BT  system  crap  and  it  will   quickly  and  efficiently  target  crap  to  your   customers.  Faster  then  you  could     ever  have  yourself.”  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   65  
  • 63. >  ClickTale  tes-ng  case  study    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   66  
  • 64. >  Bad  campaign  worse  than  none    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   67  
  • 65. >  Keys  to  effec-ve  targe-ng     1.  Define  success  metrics   2.  Define  and  validate  segments   3.  Develop  targeHng  and  message  matrix     4.  Transform  matrix  into  business  rules   5.  Develop  and  test  content   6.  Start  targeHng  and  automate   7.  Keep  tesHng  and  refining   8.  Communicate  results  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   68  
  • 66. >  Recommended  resources    §  201003  McKinsey  Get  More  Value  From  Digital  MarkeHng  §  200912  Unbounce  101  Landing  Page  OpHmizaHon  Tips  §  201008  eConsultancy  TV  Ad  Landing  Pages  §  200910  eMarketer  Bad  Campaign  Worse  Than  None  §  201003  WebCredible  10  Unexpected  User  Behaviours  §  200910  Myth  Of  The  Page  Fold  §  201008  Sample  Size  Currency  Of  MarkeHng  TesHng  §  200409  Roy  Taguchi  Or  MV  TesHng  For  Marketers  §  200702  Internet  Retailer  NavigaHng  Depths  Of  MV  TesHng  §  201009  Six  Revisions  10  Usability  Tips  Based  On  Research  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   69  
  • 67. Summary  
  • 68. >  Get  the  most  out  of  the  course     Category   Data   Metrics   Insights   PlaForm   Why?   What?   How?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   71  
  • 69. >  Summary  and  ac-on  items    §  Campaign  flow  and  media  aWribuHon   –  Draw  campaign  flow  for  your  company   –  Check  plaoorm  cookie  expiraHon  periods   –  Enable  pathing  of  direct  campaign  responses   –  InvesHgate  how  to  track  offline  conversions  §  How  to  reduce  media  waste   –  Develop  basic  targeHng  matrix  to  get  started   –  Combine  targeHng  plaoorms  for  consistency   –  List  all  customer  touch  points  for  idenHficaHon   –  Check  for  common  ID  across  all  data  sources  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   72  
  • 70. Exercise:  Google  Analy-cs  
  • 71. >  Google  Analy-cs  prac-ce    §  Describing  website  visitors  §  IdenHfying  traffic  sources  (reach)   –  Campaign  tracking  mechanics  §  Analyzing  content  usage  (engagement)  §  Analyzing  conversion  drop-­‐out  (conversion)    §  Defining  custom  segments  (breakdowns)  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   74  
  • 72. >  Describing  website  visitors    §  Average  connecHon  speed  §  Plug-­‐in  usage  (i.e.  Flash,  etc)  §  Mobile  vs.  normal  computers  §  Geographic  locaHon  of  visitors  §  Time  of  day,  day  of  week  §  Repeat  visitaHon  §  What  else?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   75  
  • 73. >  Iden-fying  traffic  sources    §  GeneraHng  de-­‐duplicated  reports  §  Campaign  tracking  mechanics  §  Conversion  goals  and  success  events  §  Plus  adding  addiHonal  metrics  §  Paid  vs.  organic  traffic  sources  §  Branded  vs.  generic  search  §  Traffic  quanHty  vs.  quality  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   76  
  • 74. >  Analysing  content  usage    §  Page  traffic  vs.  engagement  §  Entry  vs.  exit  pages  §  Popular  page  paths  §  Internal  search  terms  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   77  
  • 75. >  Analysing  conversion  drop-­‐out    §  Defining  conversion  funnels  §  IdenHfying  main  problem  pages  §  Pages  visited  aner  conversion  barriers  §  Conversion  drop-­‐out  by  segment  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   78  
  • 76. >  Defining  custom  segments    §  New  vs.  repeat  visitors  §  By  geographic  locaHon  §  By  connecHon  speed  §  By  products  purchased  §  New  vs.  exisHng  customers  §  Branded  vs.  generic  search  §  By  demographics,  custom  segments  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   79  
  • 77. >  Useful  analy-cs  tools    §  hWp://labs.google.com/sets    §  hWp://www.google.com/trends      §  hWp://www.google.com/insights/search    §  hWp://bit.ly/googlekeywordtoolexternal    §  hWp://www.google.com/webmasters    §  hWp://www.facebook.com/insights    §  hWp://www.google.com/adplanner    §  hWp://www.google.com/videotargeHng    §  hWp://www.keywordspy.com      §  hWp://www.compete.com    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   80  
  • 78. >  Useful  analy-cs  tools    §  hWp://bit.ly/hitwisedatacenter      §  hWp://www.socialmenHon.com    §  hWp://twiWersenHment.appspot.com    §  hWp://bit.ly/twiWerstreamgraphs    §  hWp://twitrratr.com    §  hWp://bit.ly/listonools1      §  hWp://bit.ly/listonools2    §  hWp://manyeyes.alphaworks.ibm.com    §  hWp://www.wordle.net      §  hWp://www.tagxedo.com    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   81  
  • 79. Contact  us  cbartens@datalicious.com     Follow  us   twiWer.com/datalicious     Learn  more   blog.datalicious.com    

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