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Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
Digi-Tech Marketing Data Strategy
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Digi-Tech Marketing Data Strategy

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

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

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  1. >  Marke(ng  Data  Strategy  <   Smart  data  driven  marke-ng  
  2. >  Short  but  sharp  history  §  Datalicious  was  founded  late  2007  §  Strong  Omniture  web  analy-cs  history  §  Now  360  data  agency  with  specialist  team  §  Combina-on  of  analysts  and  developers  §  Carefully  selected  best  of  breed  partners  §  Driving  industry  best  prac-ce  (ADMA)  §  Turning  data  into  ac-onable  insights  §  Execu-ng  smart  data  driven  campaigns  June  2010   ©  Datalicious  Pty  Ltd   2  
  3. >  Smart  data  driven  marke(ng   “Using  data  to  widen  the  funnel”   Media  A;ribu(on  &  Modeling   Op(mise  channel  mix,  predict  sales   Targeted  Direct  Marke(ng     Increase  relevance,  reduce  churn   Tes(ng  &  Op(misa(on   Remove  barriers,  drive  sales   Boos(ng  ROI  June  2010   ©  Datalicious  Pty  Ltd   3  
  4. >  Wide  range  of  data  services   Data   Insights   Ac(on   PlaIorms   Analy(cs   Campaigns         Data  collec(on  and  processing   Data  mining  and  modelling   Data  usage  and  applica(on         Web  analy(cs  solu(ons   Customised  dashboards   Marke(ng  automa(on         Omniture,  Google  Analy(cs,  etc   Tableau,  SpoIire,  SPSS,  etc   Alterian,  SiteCore,  Inxmail,  etc         Tag-­‐less  online  data  capture   Media  a;ribu(on  models   Targe(ng  and  merchandising         End-­‐to-­‐end  data  plaIorms   Market  and  compe(tor  trends   Internal  search  op(misa(on         IVR  and  call  center  repor(ng   Social  media  monitoring   CRM  strategy  and  execu(on         Single  customer  view   Customer  profiling   Tes(ng  programs    June  2010   ©  Datalicious  Pty  Ltd   4  
  5. >  Clients  across  all  industries  June  2010   ©  Datalicious  Pty  Ltd   5  
  6. >  Today  §  Capturing  data   –  Op-ons,  limita-ons,  innova-ons  §  Genera-ng  insights   –  Process,  metrics,  examples  §  Taking  ac-on   –  Media,  targe-ng,  tes-ng  June  2010   ©  Datalicious  Pty  Ltd   6  
  7. Ques(ons?   Yell  out  or  tweet  @datalicious    June  2010   ©  Datalicious  Pty  Ltd   7  
  8. Clive  Humby:  Data  is  the  new  oil   June  2010   ©  Datalicious  Pty  Ltd   8  
  9. Oil  and  data  come  at  a  price  June  2010   ©  Datalicious  Pty  Ltd   9  
  10. >  Google  Ngram:  Privacy    June  2010   ©  Datalicious  Pty  Ltd   10  
  11. Collec(ng  data     for  the  sake  of  it   or  to  add  value   to  customers?  June  2010   ©  Datalicious  Pty  Ltd   11  
  12. Product   Partners   Price   Marke(ng   Process   Mix   Place   People   Promo(on   Physical   Evidence  June  2010   ©  Datalicious  Pty  Ltd   12  
  13. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Capturing  data  June  2010   ©  Datalicious  Pty  Ltd   13  
  14. >  Digital  data  is  plen(ful  and  cheap      June  2010   ©  Datalicious  Pty  Ltd   14   Source:  Omniture  Summit,  MaV  Belkin,  2007  
  15. >  Digital  metric  categories   +Social  June  2010   ©  Datalicious  Pty  Ltd   15   Source:  Accuracy  Whitepaper  for  web  analy-cs,  Brian  CliYon,  2008  
  16. >  What  plaIorm  to  use   Stage  1:  Data   Stage  2:  Insights   Stage  3:  Ac(on   Data  is  fully  owned       Sophis-ca-on in-­‐house,  advanced   Data  is  being  brought     predic-ve  modelling   in-­‐house,  shiY  towards   and  trigger  based   Third  par-es  control   insights  genera-on  and   marke-ng,  i.e.  what     data  mining,  i.e.  why   will  happen  and     most  data,  ad  hoc   did  it  happen?   making  it  happen!   repor-ng  only,  i.e.     what  happened?   Time,  Control  June  2010   ©  Datalicious  Pty  Ltd   16  
  17. >  Governance  and  data  integrity  June  2010   ©  Datalicious  Pty  Ltd   17   Source:  Omniture  Summit,  MaV  Belkin,  2007  
  18. >  Tag-­‐less  data  capture   Google:  “atomic  labs”       www.atomiclabs.com  June  2010   ©  Datalicious  Pty  Ltd   18  
  19. >  Google  data  in  Australia     Source:  hVp://www.hitwise.com/au/resources/data-­‐centre  June  2010   ©  Datalicious  Pty  Ltd   19  
  20. >  Search  at  all  stages    June  2010   ©  Datalicious  Pty  Ltd   20   Source:  Inside  the  Mind  of  the  Searcher,  Enquiro  2004  
  21. >  Search  call  to  ac(on  for  offline    June  2010   ©  Datalicious  Pty  Ltd   21  
  22. June  2010   ©  Datalicious  Pty  Ltd   22  
  23. >  PURLs  boos(ng  DM  response  rates   Text  June  2010   ©  Datalicious  Pty  Ltd   23  
  24. >  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  June  2010   ©  Datalicious  Pty  Ltd   24  
  25. >  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  June  2010   ©  Datalicious  Pty  Ltd   25  
  26. >  Jet  Interac(ve  phone  call  data  June  2010   ©  Datalicious  Pty  Ltd   26  
  27. >  Bad  experience:  67%  hang  up   2/3  of  callers   hang  up  the   phone  as  they   cannot  get   what  they   want  fast   enough.  June  2010   ©  Datalicious  Pty  Ltd   27  
  28. >  Poten(al  calls  to  ac(on    §  Unique  click-­‐through  URLs   Calls  to  ac(on  §  Unique  vanity  domains  or  URLs   can  help  shape  §  Unique  phone  numbers   the  customer  §  Unique  search  terms   experience  not   just  evaluate  §  Unique  email  addresses   responses  §  Unique  personal  URLs  (PURLs)  §  Unique  SMS  numbers,  QR  codes  §  Unique  promo-onal  codes,  vouchers  §  Geographic  loca-on  (Facebook,  FourSquare)  §  Plus  regression  analysis  of  cause  and  effect  June  2010   ©  Datalicious  Pty  Ltd   28  
  29. >  Cookie  based  tracking  process     What  if:  Someone  deletes  their  cookies?  Or  uses  a  device   that  does  not  support  JavaScript?  Or  uses  two  computers   (work  vs.  home)?  Or  two  people  use  the  same  computer?  June  2010   ©  Datalicious  Pty  Ltd   29   Source:  Google  Analy-cs,  Jus-n  Cutroni,  2007  
  30. >  Duplica(on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Email     Email   Blast   PlaIorm   $   Organic   Google   Search   Analy(cs   $  June  2010   ©  Datalicious  Pty  Ltd   30  
  31. >  De-­‐duplica(on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy(cs   PlaIorm   Email     Blast   $   Organic   Search   $  June  2010   ©  Datalicious  Pty  Ltd   31  
  32. >  Datalicious  SuperTag   Ad  Sever,   Web   SuperTag   Paid  Search   Analy-cs   Use  the  same  business  rules  to  trigger  conversions     across  all  plaIorms  to  reduce  discrepancies  June  2010   ©  Datalicious  Pty  Ltd   32  
  33. >  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.    June  2010   ©  Datalicious  Pty  Ltd   33   Source:  White  Paper,  RedEye,  2007  
  34. >  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  June  2010   ©  Datalicious  Pty  Ltd   34  
  35. >  Customer  profiling  in  ac(on     Using  website  and  email  responses   to  learn  a  liVle  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  June  2010   ©  Datalicious  Pty  Ltd   35  
  36. >  Online  form  best  prac(ce   Maximise  data  integrity   Age  vs.  year  of  birth   Free  text  vs.  op-ons   Use  auto-­‐complete     wherever  possible  June  2010   ©  Datalicious  Pty  Ltd   36  
  37. >  Research  online,  shop  offline    June  2010   ©  Datalicious  Pty  Ltd   37   Source:  2008  Digital  Future  Report,  Surveying  The  Digital  Future,  Year  Seven,  USC  Annenberg  School  
  38. >  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  June  2010   ©  Datalicious  Pty  Ltd   38  
  39. >  Summary:  Capturing  data  §  Plenty  of  data  sources  and  planorms  §  Especially  search  is  great  free  data  source  §  Maintaining  data  integrity  takes  effort  §  Cookie  technology  has  its  limita-ons  §  New  tag-­‐less  technologies  emerging  §  Maximise  iden-fica-on  points  §  Offline  can  be  -ed  to  online  June  2010   ©  Datalicious  Pty  Ltd   39  
  40. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Genera(ng  insights  June  2010   ©  Datalicious  Pty  Ltd   40  
  41. >  Corporate  data  journey     Stage  1   Stage  2     Stage  3 Data   Insights   Ac(on   “Leaders”   Data  is  fully  owned     “Followers”     Sophis-ca-on in-­‐house,  advanced   Data  is  being  brought     predic-ve  modelling   “Laggards”   in-­‐house,  shiY  towards   and  trigger  based   Third  par-es  control   insights  genera-on  and   marke-ng,  i.e.  what     data  mining,  i.e.  why   will  happen  and     most  data,  ad  hoc   did  it  happen?   making  it  happen!   repor-ng  only,  i.e.     what  happened?   Time,  Control  June  2010   ©  Datalicious  Pty  Ltd   41  
  42. >  Process  is  key  to  success    June  2010   ©  Datalicious  Pty  Ltd   42   Source:  Omniture  Summit,  MaV  Belkin,  2007  
  43. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac(on   Sa(sfac(on   Social  media  June  2010   ©  Datalicious  Pty  Ltd   43  
  44. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)  June  2010   ©  Datalicious  Pty  Ltd   44  
  45. >  Marke(ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  June  2010   ©  Datalicious  Pty  Ltd   45  
  46. >  Addi(onal  funnel  breakdowns     Brand  vs.  direct  response  campaign   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   New  prospects  vs.  exis-ng  customers  June  2010   ©  Datalicious  Pty  Ltd   46  
  47. New  vs.  returning  visitors  June  2010   ©  Datalicious  Pty  Ltd   47  
  48. AU/NZ  vs.  rest  of  world  June  2010   ©  Datalicious  Pty  Ltd   48  
  49. >  Poten(al  funnel  breakdowns    §  Brand  vs.  direct  response  campaign  §  New  prospects  vs.  exis-ng  customers  §  Baseline  vs.  incremental  conversions  §  Compe--ve  ac-vity,  i.e.  none,  a  lot,  etc  §  Segments,  i.e.  age,  loca-on,  influence,  etc  §  Channels,  i.e.  search,  display,  social,  etc  §  Campaigns,  i.e.  this/last  week,  month,  year,  etc  §  Products  and  brands,  i.e.  iphone,  htc,  etc  §  Offers,  i.e.  free  minutes,  free  handset,  etc  §  Devices,  i.e.  home,  office,  mobile,  tablet,  etc      June  2010   ©  Datalicious  Pty  Ltd   49  
  50. >  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  June  2010   ©  Datalicious  Pty  Ltd   50  
  51. >  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  June  2010   ©  Datalicious  Pty  Ltd   51  
  52. >  Addi(onal  success  metrics   Click   Through   $   Click   Add  To   Cart   Through   Cart   Checkout   ?   $   Click   Bounce   Pages  Per   Avg  Cart   Through   Rate   Visit   Value   $   Click   Call  back   Store   Through   requests   Searches   >  ...   $  June  2010   ©  Datalicious  Pty  Ltd   52  
  53. Exercise:  Sta(s(cal  significance  June  2010   ©  Datalicious  Pty  Ltd   53  
  54. 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  June  2010   ©  Datalicious  Pty  Ltd   54   Google  “nss  sample  size  calculator”  
  55. 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  June  2010   ©  Datalicious  Pty  Ltd   55   Google  “nss  sample  size  calculator”  
  56. Exercise:  Metrics  framework  June  2010   ©  Datalicious  Pty  Ltd   56  
  57. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac(cal   Funnel   breakdowns  June  2010   ©  Datalicious  Pty  Ltd   57  
  58. >  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  June  2010   ©  Datalicious  Pty  Ltd   58  
  59. >  Establishing  a  baseline   Switch  all  adver-sing  off  for  a  period   of  -me  (unlikely)  or  establish  a  smaller   control  group  that  is  representa-ve  of   the  en-re  popula-on  (i.e.  search  term,   geography,  etc)  and  switch  off  selected   channels  one  at  a  -me  to  minimise   impact  on  overall  conversions.  June  2010   ©  Datalicious  Pty  Ltd   59  
  60. >  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  June  2010   ©  Datalicious  Pty  Ltd   60  
  61. >  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  June  2010   ©  Datalicious  Pty  Ltd   61  
  62. >  Sample  customer  level  data    June  2010   ©  Datalicious  Pty  Ltd   62  
  63. >  Enhancing  data  sources   Customer  profile  data   Geo-­‐demographic  data   +   The  whole  is  greater     than  the  sum  of  its  parts   3rd  party  data  June  2010   ©  Datalicious  Pty  Ltd   63  
  64. >  Geo-­‐demographic  segments  June  2010   ©  Datalicious  Pty  Ltd   64  
  65. June  2010   ©  Datalicious  Pty  Ltd   65  
  66. >  Hitwise  Mosaic  segment  swing  australia.com  vs.  newzealand.com   australia.com  vs.  bulafiji.com    June  2010   ©  Datalicious  Pty  Ltd   66   Source:  Hitwise,  2006  
  67. >  Single  source  of  truth  repor(ng   Insights   Repor(ng  June  2010   ©  Datalicious  Pty  Ltd   67  
  68. June  2010   ©  Datalicious  Pty  Ltd   68  
  69. Thinking  outside  the  box  June  2010   ©  Datalicious  Pty  Ltd   69  
  70. >  Store  locator  searches  June  2010   ©  Datalicious  Pty  Ltd   70  
  71. >  Search  and  brand  strength    June  2010   ©  Datalicious  Pty  Ltd   71  
  72. >  Search  and  media  planning    June  2010   ©  Datalicious  Pty  Ltd   72  
  73. >  Search  driving  offline  crea(ve    June  2010   ©  Datalicious  Pty  Ltd   73  
  74. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  June  2010   ©  Datalicious  Pty  Ltd   74  
  75. >  Summary:  Genera(ng  insights  §  Right  resources  and  processes  are  key  §  Define  a  standardised  metrics  framework  §  Maintain  framework  to  enable  comparison  §  Combine  data  sets  for  hidden  insights    §  Establish  a  single  (data)  source  of  truth  §  Think  outside  the  box  and  across  channels  §  Data  does  not  equal  significance  June  2010   ©  Datalicious  Pty  Ltd   75  
  76. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Taking  ac(on  June  2010   ©  Datalicious  Pty  Ltd   76  
  77. >  Smart  data  driven  marke(ng   “Using  data  to  widen  the  funnel”   Media  A;ribu(on  &  Modeling   Op(mise  channel  mix,  predict  sales   Targeted  Direct  Marke(ng     Increase  relevance,  reduce  churn   Tes(ng  &  Op(misa(on   Remove  barriers,  drive  sales   Boos(ng  ROI  June  2010   ©  Datalicious  Pty  Ltd   77  
  78. >  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   Twi;er,  etc   C3   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc  June  2010   ©  Datalicious  Pty  Ltd   78  
  79. >  Success  a;ribu(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  June  2010   ©  Datalicious  Pty  Ltd   79  
  80. >  First  and  last  click  a;ribu(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    June  2010   ©  Datalicious  Pty  Ltd   80  
  81. >  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update  June  2010   ©  Datalicious  Pty  Ltd   81  
  82. >  Understanding  channel  mix  June  2010   ©  Datalicious  Pty  Ltd   82  
  83. >  ClearSaleing  media  a;ribu(on  June  2010   ©  Datalicious  Pty  Ltd   83  
  84. June  2010   ©  Datalicious  Pty  Ltd   84  
  85. Targe(ng   The  right  message   Via  the  right  channel   To  the  right  person   At  the  right  -me  June  2010   ©  Datalicious  Pty  Ltd   85  
  86. >  Increase  revenue  by  10-­‐20%     Capture  internet  traffic   Capture  50-­‐100%  of  fair  market  share  of  traffic   Increase  consumer  engagement   Exceed  50%  of  best  compe-tor’s  engagement  rate     Capture  qualified  leads  and  sell   Convert  10-­‐15%  to  leads  and  of  that  20%  to  sales   Building  consumer  loyalty   Build  60%  loyalty  rate  and  40%  sales  conversion   Increase  online  revenue   Earn  10-­‐20%  incremental  revenue  online  June  2010   ©  Datalicious  Pty  Ltd   86  
  87. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  June  2010   ©  Datalicious  Pty  Ltd   87  
  88. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.   Online  research     Change  increases   the  importance  of   experience  during   research  phase.  June  2010   ©  Datalicious  Pty  Ltd   88  
  89. June  2010   ©  Datalicious  Pty  Ltd   89  
  90. >  Coordina(on  across  channels         Genera(ng   Crea(ng   Maximising   awareness   engagement   revenue   TV,  radio,  print,   Retail  stores,  in-­‐store   Outbound  calls,  direct   outdoor,  search   kiosks,  call  centers,   mail,  emails,  social   marke-ng,  display   brochures,  websites,   media,  SMS,  mobile   ads,  performance   mobile  apps,  online   apps,  etc   networks,  affiliates,   chat,  social  media,  etc   social  media,  etc   Off-­‐site   On-­‐site   Profile     targe(ng   targe(ng   targe(ng  June  2010   ©  Datalicious  Pty  Ltd   90  
  91. >  Combining  targe(ng  plaIorms     Off-­‐site   targe-ng   Profile   On-­‐site   targe-ng   targe-ng  June  2010   ©  Datalicious  Pty  Ltd   91  
  92. June  2010   ©  Datalicious  Pty  Ltd   92  
  93. Take  a  closer   look  at  our   cash  flow   solu(ons  June  2010   ©  Datalicious  Pty  Ltd   93  
  94. June  2010   ©  Datalicious  Pty  Ltd   94  
  95. >  Combining  technology     On-­‐site     Off-­‐site   segments   segments   CRM  June  2010   ©  Datalicious  Pty  Ltd   95  
  96. >  Datalicious  SuperTag   §  One  tag  for  all  sites  and  planorms   §  Hosted  internally  or  externally   §  Fast  tag  implementa-on/updates   §  Eliminates  JavaScript  caching   §  Enables  code  tes-ng  on  live  site   §  Enables  heat  map  implementa-on   §  Enables  redirects  for  A/B  tes-ng   §  Enables  network  wide  re-­‐targe-ng   §  Enables  live  chat  implementa-on   §  Plus  mul--­‐channel  media  aVribu-on  June  2010   ©  Datalicious  Pty  Ltd   96  
  97. >  Affinity  re-­‐targe(ng  in  ac(on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe-ng,     response  rates  are     liYed  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  h;p://bit.ly/de70b7   12  Month  Caps   - + - +June  2010   ©  Datalicious  Pty  Ltd   97  
  98. >  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  June  2010   ©  Datalicious  Pty  Ltd   98  
  99. >  Sample  site  visitor  composi(on     30%  new  visitors  with  no   30%  repeat  visitors  with   previous  website  history   referral  data  and  some   aside  from  campaign  or   website  history  allowing   referrer  data  of  which   50%  to  be  segmented  by   maybe  50%  is  useful   content  affinity   30%  exis(ng  customers  with  extensive   10%  serious   profile  including  transac-onal  history  of   prospects   which  maybe  50%  can  actually  be   with  limited   iden-fied  as  individuals     profile  data  June  2010   ©  Datalicious  Pty  Ltd   99  
  100. Exercise:  Targe(ng  matrix  June  2010   ©  Datalicious  Pty  Ltd   100  
  101. >  Exercise:  Targe(ng  matrix   Purchase   Segments:  Colour,  price,   Media   Data     Cycle   product  affinity,  etc   Channels   Points   Default,   awareness   Research,   considera(on   Purchase   intent   Reten(on,   up/cross-­‐sell  June  2010   ©  Datalicious  Pty  Ltd   101  
  102. >  Exercise:  Targe(ng  matrix   Purchase   Segments:  Colour,  price,   Media   Data     Cycle   product  affinity,  etc   Channels   Points   Default,   Have  you     Have  you     Display,   Default   awareness   seen  A?   seen  B?   search,  etc   Research,   A  has  great     B  has  great     Search,   Ad  clicks,   considera(on   features!   features!   website,  etc   prod  views   Purchase   A  delivers   B  delivers   Website,   Cart  adds,   intent   great  value!   great  value!   emails,  etc   checkouts   Reten(on,   Why  not   Why  not   Direct  mails,   Email  clicks,   up/cross-­‐sell   buy  B?   buy  A?   emails,  etc   logins,  etc  June  2010   ©  Datalicious  Pty  Ltd   102  
  103. >  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.”  June  2010   ©  Datalicious  Pty  Ltd   103  
  104. >  ClickTale  tes(ng  case  study    June  2010   ©  Datalicious  Pty  Ltd   104  
  105. >  Developing  a  tes(ng  matrix   Test   Segment   Content   KPIs   Poten(al   Results   New   Conversion   Next  step,   Test  #1A     prospects   form  A   order,  etc   ?   ?   New   Conversion   Next  step,   Test  #1B   prospects   form  B   order,  etc   ?   ?   New   Conversion   Next  step,   Test  #1N   prospects   form  N   order,  etc   ?   ?   ?   ?   ?   ?   ?   ?  June  2010   ©  Datalicious  Pty  Ltd   105  
  106. >  Summary  §  There  is  no  magic  formula  for  ROI  §  Focus  on  the  en-re  conversion  funnel  §  Media  aVribu-on  is  hard  but  necessary  §  Neither  first  nor  last  click  method  works  §  Create  a  coordinated  targeted  experience  §  Content  is  always  king  no  maVer  what  §  Test,  learn  and  refine  con-nuously  June  2010   ©  Datalicious  Pty  Ltd   106  
  107. Don’t  wait     for  be;er  data,   get  started  now.  June  2010   ©  Datalicious  Pty  Ltd   107  
  108. Contact  me   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  me   twi;er.com/datalicious    June  2010   ©  Datalicious  Pty  Ltd   108  
  109. Data  >  Insights  >  Ac(on  June  2010   ©  Datalicious  Pty  Ltd   109  

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