ADMA Digital Certificate: Why Data Is Sexy

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September 2009

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ADMA Digital Certificate: Why Data Is Sexy

  1. 1. [  Why  data  is  sexy  ]   No  really,  it  is!  
  2. 2. [  Main  data  categories  ]   Campaign   data   Consumer   Customer   data   data   Compe5tor   data   9/10/09   ©  Datalicious  Pty  Ltd   2  
  3. 3. Campaign  data   9/10/09   ©  Datalicious  Pty  Ltd   3  
  4. 4. [  Defining  analy:cs  strategy  ]   Search,  display  ads   Web  analy5cs   Awareness   Interest   Desire   Ac5on   Sa5sfac5on   Online  surveys,  site  polls   Social  media   Social  media   9/10/09   ©  Datalicious  Pty  Ltd   4  
  5. 5. [  Single  source  of  truth  ]   data   ROAS   9/10/09   ©  Datalicious  Pty  Ltd   5  
  6. 6. [  De-­‐duplica:on  across  channels  ]   Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Omniture   PlaGorm   Email     Email   Blast   PlaGorm   $   Organic   Google   Search   Analy:cs   $   9/10/09   ©  Datalicious  Pty  Ltd   6  
  7. 7. [  De-­‐duplica:on  across  channels  ]   9/10/09   ©  Datalicious  Pty  Ltd   7  
  8. 8. [  Success  aIribu: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   9/10/09   ©  Datalicious  Pty  Ltd   8  
  9. 9. [  Forrester  media  aIribu:on  ]   Chart  shows  an   example  only,   aQribu5on  model   needs  to  be  defined   for  each  company   separately  based  on   their  individual   success  metrics  (and   cookie  expira5on   policies).   9/10/09   ©  Datalicious  Pty  Ltd   9  
  10. 10. [  Campaign  stacking  by  channel  ]   Chart  shows   percentage  of   channel  touch   points  that  lead   to  a  conversion.   Neither  first     nor  last-­‐click   measurement   would  provide   true  picture     9/10/09   ©  Datalicious  Pty  Ltd   10  
  11. 11. [  Search  call  to  ac:on  ]   9/10/09   ©  Datalicious  Pty  Ltd   11  
  12. 12. [  Analyse  channel  overlap  ]   Call  Centre   DM  &  EDM   Radio   &   Paid  Search   Other   ATL   Display  Media   Straight  to  Site     Organic  Search   Partners   9/10/09   ©  Datalicious  Pty  Ltd   12  
  13. 13. [  Cross-­‐channel  impact  ]   9/10/09   ©  Datalicious  Pty  Ltd   13  
  14. 14. [  Email  iden:fica:on  points  ]   @   Vodafone.com.au   Phone   Online  Receipt   Research   Credit  Check   Confirma5on   Conversion   Fulfilment   @   Adver5sing   Vodafone.com.au   Retail   Online  Receipt   Research   Credit  Check   Confirma5on   Campaign   Conversion   Fulfilment   @   Vodafone.com.au   Online   Online  Order   Online  Receipt   Research   Confirma5on   Credit  Check   Confirma5on   Conversion   Fulfilment   Cookie  ID   9/10/09   ©  Datalicious  Pty  Ltd   14  
  15. 15. [  Campaign  data  summary  ]     Define  key  performance  indicators  for  each  phase  of  the   campaign  funnel  but  web  analy5cs  can  only  cover  part  of  the   funnel     Import  and  analyze  data  in  single  plaaorm,  i.e.  single  source  of   truth,  to  enable  comparison  of  channel  performance  and  avoid   duplica5on  of  orders  across  channels     Define  success  aQribu5on  model  including  cookie  expira5on   policies  for  your  company  and  test  different  budget  alloca5ons   and  channel  combina5ons  to  find  the  op5mal  mix     Use  search  calls  to  ac5on  on  above  the  line  broadcast  ads  such  as   TV  and  print  for  higher  recollec5on  and  response  rates  plus  an   improved  ability  to  track  offline  media     Consider  sending  email  receipts  for  phone  and  retail  sales  to   aQempt  to  more  accurately  track  impact  of  online  on  offline  sales   and  vice  versa   9/10/09   ©  Datalicious  Pty  Ltd   15  
  16. 16. Customer  data   9/10/09   ©  Datalicious  Pty  Ltd   16  
  17. 17. [  Store  locator  searches  ]   9/10/09   ©  Datalicious  Pty  Ltd   17  
  18. 18. [  Targe:ng  framework  ]   Reten5on   Customer  Profile   Prospect   Customer   -­‐12   -­‐11   -­‐10   -­‐9   -­‐8   -­‐7   -­‐6   -­‐5   -­‐4   -­‐3   -­‐2   -­‐1   0   1   2   3   4   5   6   7   8   9   10   11   12   Considera5on   Visitor  Behaviour   Weeks   9/10/09   ©  Datalicious  Pty  Ltd   18  
  19. 19. [  Prospect  targe:ng  parameters  ]   9/10/09   ©  Datalicious  Pty  Ltd   19  
  20. 20. [  Affinity  targe:ng  in  ac:on  ]   Different  types  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe5ng,     response  rates  are     liged  significantly     across  products.   Click-­‐Through  Rate  By  Category  Affinity   Message   Postpay   Prepay   Broadb.   Business   Blackberry  Bold   - - - + 5GB  Mobile  Broadband   - - + - Blackberry  Storm   + - + + 12  Month  Caps   - + - + 9/10/09   ©  Datalicious  Pty  Ltd   20  
  21. 21. [  Matching  segments  are  key  ]   On-­‐site     Off-­‐site   segments   segments   On  and  off-­‐site  targe5ng  plaaorms  should  use     iden5cal  triggers  to  sort  visitors  into  segments   9/10/09   ©  Datalicious  Pty  Ltd   21  
  22. 22. 9/10/09   ©  Datalicious  Pty  Ltd   22  
  23. 23. [  Combining  data  sets  ]   Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collec5on  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   predic5ve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promo5on  responses   historical  data  from  previous  transac5ons   emails,  internal  search,  etc   average  order  value,  points,  etc   UPDATED  CONTINUOUSLY   UPDATED  OCCASIONALLY   9/10/09   ©  Datalicious  Pty  Ltd   23  
  24. 24. [  Mosaic  ]   9/10/09   ©  Datalicious  Pty  Ltd   24  
  25. 25. [  Where  is  the  money  ]   $   $   $   $   $   $   $   $   $   $   9/10/09   ©  Datalicious  Pty  Ltd   25  
  26. 26. [  Customer  data  summary  ]     Customer  data  collected  online  can  and  should  influence  offline   decisions,  e.g.  retail  store  loca5ons,  outdoor  adver5sing,  print   placements,  etc     Website  data  cannot  answer  all  ques5ons  but  rather  only  iden5fies   poten5al  problem  points,  online  surveys  are  powerful  tools  to  find  out   more  about  what’s  actually  the  issue     Your  products  and  services  solve  different  problems  for  different  people   and  you  need  to  cater  for  that  and  adjust  your  adver5sing,  change  you   message  according  to  segments  and  purchase  funnel  stage  to  make   your  offering  more  relevant     CRM  databases  are  powerful  tools  to  target  but  their  informa5on  is   rela5vely  sta5c  and  needs  to  be  updated  manually  through  your   customers,  extend  your  customer  profiles  to  include  website  behavior   to  make  them  more  accurate  and  keep  them  up  to  date     In  the  end  this  is  about  iden5fying  the  20%  of  customers  you  make  80%   of  your  revenue  with  and  speak  and  treat  them  in  a  different  way   9/10/09   ©  Datalicious  Pty  Ltd   26  
  27. 27. Compe:tor  data   9/10/09   ©  Datalicious  Pty  Ltd   27  
  28. 28. [  Brand  search  volume  ]   9/10/09   ©  Datalicious  Pty  Ltd   28  
  29. 29. [  Market  share  trends  ]   9/10/09   ©  Datalicious  Pty  Ltd   29  
  30. 30. 9/10/09   ©  Datalicious  Pty  Ltd   30  
  31. 31. 9/10/09   ©  Datalicious  Pty  Ltd   31  
  32. 32. 9/10/09   ©  Datalicious  Pty  Ltd   32  
  33. 33. [  Australia  vs.  New  Zealand  ]   australia.com  vs.  newzealand.com   9/10/09   ©  Datalicious  Pty  Ltd   33   [  september  2007  ]   [  datalicious.com.au  ]  
  34. 34. [  Compe:tor  data  summary  ]     There  is  plenty  of  accessible  and  free  compe5tor  data  out  there   and  you  need  to  start  reviewing  it  on  a  regular  basis     Search  data  in  par5cular  is  a  very  powerful  tool  to  iden5fy   market  trends,  threats  and  opportuni5es  quickly  and  cost   effec5vely  as  it  provides  a  direct  insight  into  consumers  minds     Given  that  roughly  90%  of  searches  are  conducted  through   Google  in  Australia  the  overall  search  volume  of  brand  search   terms  on  Google  should  be  roughly  equivalent  with  the  brand   strength  or  at  least  awareness       But  interpret  all  data  sources  with  a  grain  of  salt  and  don’t  take   anything  for  a  fact  un5l  you  have  validated  it,  you  know  your   market  and  customers  the  best   9/10/09   ©  Datalicious  Pty  Ltd   34  
  35. 35. Consumer  data   9/10/09   ©  Datalicious  Pty  Ltd   35  
  36. 36. [  Search  term  volumes  ]   9/10/09   ©  Datalicious  Pty  Ltd   36  
  37. 37. 9/10/09   ©  Datalicious  Pty  Ltd   37  
  38. 38. 9/10/09   ©  Datalicious  Pty  Ltd   38  
  39. 39. [  Media  planning  ]   9/10/09   ©  Datalicious  Pty  Ltd   39  
  40. 40. [  Leading  lifestyles,  45+  ]   9/10/09   ©  Datalicious  Pty  Ltd   40  
  41. 41. [  Consumer  data  summary  ]     Search  is  not  only  useful  to  analyze  brand  trends  but  can  also  be   used  to  find  out  more  about  more  granular  trends  such  as   product  categories  or  single  products     Again,  online  data  can  and  should  be  used  to  drive  offline   marke5ng  decisions,  e.g.  products  matching  most  popular  search   terms  should  not  only  be  promoted  most  on  the  homepage  but   also  in  retail,  print,  etc     Google  Ad  Planner  data  can  also  be  used  to  iden5fy  what   websites  a  segment  that  is  searching  for  a  certain  terms  visits   regularly  which  in  turn  should  influence  partnership,  sponsorship   and  media  planning       If  your  company  has  customer  address  data  a  match  with   MOSAIC  might  be  a  very  powerful  tool  to  find  out  more  about   the  media  habits  of  a  specific  customer  segment  including   websites  visits  if  combined  with  Hitwise  data   9/10/09   ©  Datalicious  Pty  Ltd   41  
  42. 42. [  Main  data  categories  ]   Campaign   data   Consumer   Customer   data   data   Compe5tor   data   9/10/09   ©  Datalicious  Pty  Ltd   42  
  43. 43. 9/10/09   ©  Datalicious  Pty  Ltd   43  
  44. 44. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010   [  Appendix  ]   9/10/09   ©  Datalicious  Pty  Ltd   44  
  45. 45. [  Useful  data  tools  ]     hQps://adwords.google.com/select/KeywordToolExternal     hQp://www.google.com/sktool     hQp://www.google.com/insights/search     hQp://www.google.com/trends     hQp://labs.google.com/sets     hQps://www.google.com/adplanner     hQps://www.google.com/adplanner     hQp://www.socialmen5on.com     hQp://www.trendis5c.com     hQp://www.yureekah.com     hQp://www.wordle.net   9/10/09   ©  Datalicious  Pty  Ltd   45  
  46. 46. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010   [  About  Datalicious  ]   9/10/09   ©  Datalicious  Pty  Ltd   46  
  47. 47. [  Con:nuous  op:miza:on  ]     Data   Ac:on   Insights   9/10/09   ©  Datalicious  Pty  Ltd   47  
  48. 48. [  Best  prac:ce  approach  ]   Con:nuous  op:miza:on   Research   SEM   SEO   Tes5ng   Targe5ng   Analy:cs  framework   9/10/09   ©  Datalicious  Pty  Ltd   48  
  49. 49. [  Wide  range  of  services  ]   Data   Insights   Ac:on   Web  Analy:cs  Solu:ons   Keyword  Research   Search  Lead  Media   Marke:ng  System  Integra:on   Campaign  Repor:ng   Campaign  Op:misa:on   Cross  Channel  Media  Tracking   Segmenta:on/Data  Mining   Internal  Search  Op:misa:on   Online  Surveys/Panels   Quan:ta:ve  Research   Targe:ng/Merchandizing   Omniture  Specialists   Market/Consumer  Trends   A/B,  Mul:variate  Tes:ng   Google  Analy:cs  Specialists   Compe:tor  Analysis   Staff  Training/Workshops   9/10/09   ©  Datalicious  Pty  Ltd   49  
  50. 50. [  Challenging  clients  ]   9/10/09   ©  Datalicious  Pty  Ltd   50  
  51. 51. Ques:ons   cbartens@datalicious.com   Updates   twiQer.com/datalicious   blog.datalicious.com   9/10/09   ©  Datalicious  Pty  Ltd   51  

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