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Analyze to Optimize

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The presentation discusses training on data, measurement and ROI.

The presentation discusses training on data, measurement and ROI.

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  • 1. >  Analyse  to  op-mise  <     ADMA  short  course  on  data,     measurement  and  ROI  
  • 2. >  Company  history    §  Datalicious  was  founded  in  late  2007  §  Strong  Omniture  web  analy@cs  history  §  1  of  4  Omniture  Service  Partners  globally  §  Now  360  data  agency  with  specialist  team  §  Combina@on  of  analysts  and  developers  §  Making  data  accessible  and  ac@onable  §  Evangelizing  smart  data  driven  marke@ng  §  Driving  industry  best  prac@ce  (ADMA)  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   2  
  • 3. >  Smart  data  driven  marke-ng     Media  A:ribu-on   Op-mise  channel  mix   Targe-ng     Increase  relevance   Tes-ng   Improve  usability   $$$  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   3  
  • 4. >  Wide  range  of  data  services   Data   Insights   Ac-on   PlaGorms   Repor-ng   Applica-ons         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   Media  a:ribu-on  models   Aprimo,  Trac-on,  Inxmail,  etc         Tag-­‐less  online  data  capture   Market  and  compe-tor  trends   Targe-ng  and  merchandising         End-­‐to-­‐end  data  plaGorms   Social  media  monitoring   Internal  search  op-misa-on         IVR  and  call  center  repor-ng   Online  surveys  and  polls   CRM  strategy  and  execu-on         Single  customer  view   Customer  profiling   Tes-ng  programs    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   4  
  • 5. >  Clients  across  all  industries    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   5  
  • 6. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Course  overview    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   6  
  • 7. >  Day  1:  Basic  Analy-cs    §  Defining  a  metrics  framework   –  What  to  report  on,  when  and  why?   –  Matching  strategic  and  tac@cal  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  limita@ons  §  Plus  hands-­‐on  exercises  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   7  
  • 8. >  Day  2:  Advanced  Analy-cs    §  Campaign  flow  and  media  a^ribu@on   –  Designing  a  campaign  flow  including  metrics   –  Omniture  vs.  Google  Analy@cs  capabili@es  §  How  to  reduce  media  waste   –  Tes@ng  and  targe@ng  in  a  media  world   –  Media  vs.  content  and  usability  §  Plus  hands-­‐on  exercises  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   8  
  • 9. >  Training  outcomes    §  Aber  successful  comple@on  of  the  training   course  par@cipants  will  be  able  to   –  Define  a  metrics  framework  for  any  client   –  Enable  benchmarking  across  campaigns   –  Incorporate  analy@cs  into  the  planning  process   –  Pull  and  interpret  key  reports  in  Google  Analy@cs   –  Impress  with  insights  instead  of  spreadsheets   –  Know  how  to  extend  op@misa@on  past  media  buy   –  Show  the  true  value  of  digital  media  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   9  
  • 10. >  Get  the  most  out  of  the  course     Category   Data   Metrics   Insights   PlaGorm   Why?   What?   How?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   10  
  • 11. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Metrics  framework    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   11  
  • 12. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac-on   Sa-sfac-on   Social  media  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   12  
  • 13. >  Importance  of  social  media     Search   Company   Promo-on   Consumer   WOM,  blogs,  reviews,   ra-ngs,  communi-es,   social  networks,  photo   sharing,  video  sharing  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   13  
  • 14. >  Social  as  the  new  search    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   14  
  • 15. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac@on)   (Sa@sfac@on)  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   15  
  • 16. >  Marke-ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   16  
  • 17. >  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  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   17  
  • 18. New  vs.  returning  visitors  
  • 19. AU/NZ  vs.  rest  of  world  
  • 20. Prospect  vs.  customer  High  vs.  low  value  Product  affinity  Post  code,  age,  sex,  etc  
  • 21. Exercise:  Funnel  breakdowns  
  • 22. >  Exercise:  Funnel  breakdowns    §  List  poten@ally  insighful  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  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   22  
  • 23. Exercise:  Conversion  metrics  
  • 24. >  Exercise:  Conversion  metrics    §  Key  conversion  metrics  differ  by  category   –  Commerce   –  Lead  genera@on   –  Content  publishing   –  Customer  service  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   24  
  • 25. >  Exercise:  Conversion  metrics    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   25   Source:  Omniture  Summit,  Ma^  Belkin,  2007  
  • 26. Custom  conversion  goals  
  • 27. >  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  2010   ©  ADMA  &  Datalicious  Pty  Ltd   27  
  • 28. >  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  2010   ©  ADMA  &  Datalicious  Pty  Ltd   28  
  • 29. >  Addi-onal  success  metrics     Click   Through   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   29  
  • 30. Pages  per  visit   Time  on  site  
  • 31. >  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  2010   ©  ADMA  &  Datalicious  Pty  Ltd   31  
  • 32. >  eMarketer  interac-ve  metrics    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   32  
  • 33. >  Measuring  social  media     Sen@ment   Influence   Reach  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   33  
  • 34. Exercise:  Metrics  framework  
  • 35. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   Level  2   Strategic   Level  3   Tac-cal  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   35  
  • 36. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   People   People   People   People   reached   engaged   converted   delighted   Search   Level  2   Strategic   impressions,   UBs,  etc   ?   ?   ?   Click-­‐through   Level  3   Tac-cal   or  interac-on   rate,  etc   ?   ?   ?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   36  
  • 37. >  ROI,  ROMI,  BE,  etc     R−I R  Revenue   = ROI   I  Investment     I   ROI  Return  on    investment     IR − MI IR  Incremental    revenue   = ROMI   MI MI    Marke@ng    investment   ROMI  Return  on   IR − MI  marke@ng    investment   = ROMI + BE   BE  Brand  equity   MIOctober  2010   ©  ADMA  &  Datalicious  Pty  Ltd   37  
  • 38. >  Success:  ROMI  +  BE     IR − MI = ROMI + BE MI §  Establish  incremental  revenue  (IR)   –  Requires  baseline  revenue  to  calculate  addi@onal     revenue  as  well  as  revenue  from  cost  savings   §  Establish  marke@ng  investment  (MI)   –  Requires  all  costs  across  technology,  content,  data     and  resources  plus  promo@ons  and  discounts   §  Establish  brand  equity  contribu@on  (BE)   –  Requires  addi@onal  sob  metrics  to  evaluate  subscriber   percep@ons,  experience,  altudes  and  word  of  mouth    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   38  
  • 39. >  Process  is  key  to  success    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   39   Source:  Omniture  Summit,  Ma^  Belkin,  2007  
  • 40. >  Recommended  resources    §  200501  WAA  Key  Metrics  &  KPIs  §  200708  WAA  Analy@cs  Defini@ons  Volume  1  §  200612  Omniture  Effec@ve  Measurement  §  200804  Omniture  Calculated  Metrics  White  Paper  §  200702  Omniture  Effec@ve  Segmenta@on  Guide  §  200810  Ronnestam  Online  Adver@sing  And  AIDAS  §  201004  Al@meter  Social  Marke@ng  Analy@cs  §  201008  CSR  Customer  Sa@sfac@on  Vs  Delight  §  Google  “Enquiro  Search  Engine  Results  2010  PDF”  §  Google  “Razorfish  Ac@onable  Analy@cs  Report  PDF”  §  Google  “Forrester  Interac@ve  Marke@ng  Metrics  PDF”  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   40  
  • 41. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Data  sources    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   41  
  • 42. >  Digital  data  is  plen-ful  and  cheap      October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   42   Source:  Omniture  Summit,  Ma^  Belkin,  2007  
  • 43. >  Digital  data  categories     +Social  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   43   Source:  Accuracy  Whitepaper  for  web  analy@cs,  Brian  Clibon,  2008  
  • 44. >  Customer  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   44  
  • 45. >  Corporate  data  journey     Stage  1   Stage  2     Stage  3 Data   Insights   Ac-on   Data  is  fully  owned       Sophis@ca@on in-­‐house,  advanced   Data  is  being  brought     predic@ve  modelling   in-­‐house,  shib  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  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   45  
  • 46. >  What  analy-cs  plaGorm  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,  shib  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  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   46  
  • 47. >  Poten-al  data  sources     Media  and  search  data   Website,  call  center  and  retail  data   People   People   People   People   Reached   40%   Engaged   10%   Converted   1%   Delighted   Quan@ta@ve  and  qualita@ve  research  data   Social  media  data   Social  media  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   47  
  • 48. >  Atomic  Labs  tag-­‐less  data  capture     §  Keep  all  your  favourite  reports  but   §  Eliminate  tag  maintenance  and  ensure     §  New  pages/content  is  tracked  automa@cally   §  Across  normal  websites,  mobiles  and  apps  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   48  
  • 49. >  Atomic  labs  integra-on  model     §  Single  point  of  data   capture  and  processing   §  Real-­‐@me  queries  to   enrich  website  data     §  Mul@ple  data  export   op@ons  for  web  analy@cs   §  Enriching  single-­‐customer   view  website  behaviour  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   49  
  • 50. >  Google  data  in  Australia     Source:  h^p://www.hitwise.com/au/datacentre  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   50  
  • 51. >  Search  at  all  stages    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   51   Source:  Inside  the  Mind  of  the  Searcher,  Enquiro  2004  
  • 52. >  Search  and  brand  strength    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   52  
  • 53. >  Search  and  the  product  lifecycle     Nokia  N-­‐Series   Apple  iPhone  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   53  
  • 54. >  Search  and  media  planning    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   54  
  • 55. >  Search  and  media  planning    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   55  
  • 56. >  Search  driving  offline  crea-ve    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   56  
  • 57. Exercise:  Search  insights  
  • 58. >  Exercise:  Search  insights    §  Iden@fy  key  category  search  terms   –  Data  from  Google  AdWords  Keyword  Tool   –  Search  for  “google  keyword  tool”   –  Wordle  and  IBM  Many  Eyes  for  visualiza@ons   –  Search  for  “wordle  word  clouds”  and  “ibm  many  eyes”  §  Iden@fy  search  term  trends  and  compe@tors   –  Google  Trends  and  Google  Search  Insights   –  Search  for  “google  trends”  and  “google  search  insights”  §  Search  and  media  planning   –  DoubleClick  Ad  Planner  by  Google   –  Search  for  “google  ad  planner”  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   58  
  • 59. >  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?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   59   Source:  Google  Analy@cs,  Jus@n  Cutroni,  2007  
  • 60. >  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  2010   ©  ADMA  &  Datalicious  Pty  Ltd   60   Source:  White  Paper,  RedEye,  2007  
  • 61. Datalicious  SuperCookie  Persistent  Flash  cookie  that  cannot  be  deleted  
  • 62. >  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  2010   ©  ADMA  &  Datalicious  Pty  Ltd   62  
  • 63. >  De-­‐duplica-on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Central   Analy-cs   PlaGorm   Email     Email   Blast   PlaGorm   $   Organic   Google   Search   Analy-cs   $  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   63  
  • 64. De-­‐duplica-on  across  channels  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   64  
  • 65. De-­‐duplica-on  across  channels  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   65  
  • 66. Addi-onal  funnel  breakdowns  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   66  
  • 67. Exercise:  Duplica-on  impact  
  • 68. >  Exercise:  Duplica-on  impact    §  Double-­‐coun@ng  of  conversions  across  channels  can   have  a  significant  impact  on  key  metrics,  especially  CPA  §  Example:  Display  ads  and  paid  search   –  Total  media  budget  of  $10,000  of  which  50%  is  spend  on  paid   search  and  50%  on  display  ads   –  Total  of  100  conversions  across  both  channels  with  a  channel   overlap  of  50%,  i.e.  both  channels  claim  100%  of  conversions   based  on  their  own  repor@ng  but  once  de-­‐duplicated  they   each  only  contributed  50%  of  conversions   –  What  are  the  ini@al  CPA  values  and  what  is  the  true  CPA?  §  Solu@on:  $50  ini@al  CPA  and  $100  true  CPA   –  $5,000  /  100  =  $50  ini@al  CPA  and  $5,000  /  50  =  $100  true   CPA  (which  represents  a  100%  increase)  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   68  
  • 69. >  Reach  and  channel  overlap     TV     audience   Banner   Search   audience   audience  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   69  
  • 70. >  Es-ma-ng  reach  and  overlap    §  Apply  average  unique  visitor  count  per  recorded   unique  user  names  to  all  unique  visitor  figures  in   Google  Analy@cs,  Omniture,  etc  §  Apply  ra@o  of  total  banner  impressions  to  unique   banner  impressions  from  ad  server  to  paid  and   organic  search  impressions  in  Google  AdWords  and   Google  Webmaster  Tools  §  Compare  Google  Keyword  Tool  impressions  for  a   specific  search  term  to  reach  for  the  same  term  in   Google  Ad  Planner  §  Custom  website  entry  survey  and  campaign     stacking  to  establish  channel  overlap  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   70  
  • 71. October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   71  
  • 72. Sen-ment  analysis:  People  vs.  machine  
  • 73. >  Al-meter  social  analy-cs     Social  Marke@ng   Analy@cs  is  the   discipline  that  helps   companies  measure,   assess  and  explain  the   performance  of  social   media  ini@a@ves  in  the   context  of  specific   business  objec@ves.  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   73  
  • 74. Data  from  
  • 75. >  Overall  volume  and  influence     Data  from  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   75  
  • 76. >  Influence  and  media  value     US   Data  from   UK   AU/NZ  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   76  
  • 77. >  Facebook                insights     Using  Facebook  Like   bu^ons  is  a  free  and   powerful  way  to  gain   addi@onal  insights   into  consumer   preferences  and   enabling  social  sharing   of  content     as  well  as  possibly   influence  organic   search  rankings  in     the  near  future.  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   77  
  • 78. >  Facebook  Connect  single  sign  on     Facebook  Connect  gives  your   company  the  following  data   and  more  with  just  one  click     Email  address,  first  name,  last  name,   gender,  birthday,  interests,  picture,   affilia@ons,  last  profile  update,  @me  zone,   religion,  poli@cal  interests,  a^racted  to   which  sex,  why  they  want  to  meet   someone,  home  town,  rela@onship   status,  current  loca@on,  ac@vi@es,  music   interests,  tv  show  interests,  educa@on   history,  work  history,  family,  etc   Need  anything  else?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   78  
  • 79. Appending  social  data  to  customer  profiles   Name,  age,  gender,  occupa-on,  loca-on,  social     profiles  and  influencer  ranking  based  on  email   (influencers  only)   (all  contacts)  
  • 80. Exercise:  Sta-s-cal  significance  
  • 81. 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   Google  “nss  sample  size  calculator”  
  • 82. 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   Google  “nss  sample  size  calculator”  
  • 83. >  Addi-onal  success  metrics     Click   Through   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   83  
  • 84. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   84  
  • 85. Calendar  events  
  • 86. >  Recommended  resources    §  200311  UK  RedEye  Cookie  Case  Study  §  200807  Kaushik  Tracking  Offline  Conversion  §  200904  Kaushik  Standard  Metrics  Revisited  §  201002  Kaushik  8  Compe@@ve  Intelligence  Data  Sources  §  201005  Google  Ad  Planner  Data  Wrong  By  Up  To  20%  §  201005  MPI  How  Sta@s@cally  Valid  Is  Your  Survey  §  201009  Google  Analy@cs  How  To  Tag  Links  §  200903  Coremetrics  Conversion  Benchmarks  By  Industry  §  200906  WOM  Online  The  People  Vs  Machines  Debate  §  201007  WSJ  The  Webs  New  Gold  Mine  Your  Secrets  §  201008  Adver@singAge  Are  Marketers  Really  Spying  On  You  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   86  
  • 87. Summary  
  • 88. >  Get  the  most  out  of  the  course     Category   Data   Metrics   Insights   PlaGorm   Why?   What?   How?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   88  
  • 89. >  Summary  and  ac-on  items    §  Defining  a  metrics  framework   –  Develop  standardised  metrics  framework   –  Define  addi@onal  funnel  breakdowns   –  Establish  baseline  and  incremental   –  Define  addi@onal  success  metrics  §  Finding  and  developing  the  right  data   –  Ensure  de-­‐duplica@on  via  central  analy@cs   –  Check  reports  for  sta@s@cal  significance   –  Check  data  sources  and  their  accuracy   –  Start  popula@ng  a  calendar  of  events  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   89  
  • 90. Exercise:  Google  Analy-cs  
  • 91. >  Google  Analy-cs  prac-ce    §  Describing  website  visitors  §  Iden@fying  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   91  
  • 92. >  Describing  website  visitors    §  Average  connec@on  speed  §  Plug-­‐in  usage  (i.e.  Flash,  etc)  §  Mobile  vs.  normal  computers  §  Geographic  loca@on  of  visitors  §  Time  of  day,  day  of  week  §  Repeat  visita@on  §  What  else?  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   92  
  • 93. >  Iden-fying  traffic  sources    §  Genera@ng  de-­‐duplicated  reports  §  Campaign  tracking  mechanics  §  Conversion  goals  and  success  events  §  Plus  adding  addi@onal  metrics  §  Paid  vs.  organic  traffic  sources  §  Branded  vs.  generic  search  §  Traffic  quan@ty  vs.  quality  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   93  
  • 94. >  Analysing  content  usage    §  Page  traffic  vs.  engagement  §  Entry  vs.  exit  pages  §  Popular  page  paths  §  Internal  search  terms  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   94  
  • 95. >  Analysing  conversion  drop-­‐out    §  Defining  conversion  funnels  §  Iden@fying  main  problem  pages  §  Pages  visited  aber  conversion  barriers  §  Conversion  drop-­‐out  by  segment  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   95  
  • 96. >  Defining  custom  segments    §  New  vs.  repeat  visitors  §  By  geographic  loca@on  §  By  connec@on  speed  §  By  products  purchased  §  New  vs.  exis@ng  customers  §  Branded  vs.  generic  search  §  By  demographics,  custom  segments  October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   96  
  • 97. >  Useful  analy-cs  tools    §  h^p://labs.google.com/sets    §  h^p://www.google.com/trends      §  h^p://www.google.com/insights/search    §  h^p://bit.ly/googlekeywordtoolexternal    §  h^p://www.google.com/webmasters    §  h^p://www.facebook.com/insights    §  h^p://www.google.com/adplanner    §  h^p://www.google.com/videotarge@ng    §  h^p://www.keywordspy.com      §  h^p://www.compete.com    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   97  
  • 98. >  Useful  analy-cs  tools    §  h^p://bit.ly/hitwisedatacenter      §  h^p://www.socialmen@on.com    §  h^p://twi^ersen@ment.appspot.com    §  h^p://bit.ly/twi^erstreamgraphs    §  h^p://twitrratr.com    §  h^p://bit.ly/listobools1      §  h^p://bit.ly/listobools2    §  h^p://manyeyes.alphaworks.ibm.com    §  h^p://www.wordle.net      §  h^p://www.tagxedo.com    October  2010   ©  ADMA  &  Datalicious  Pty  Ltd   98  
  • 99. Contact  us  cbartens@datalicious.com     Follow  us   twi^er.com/datalicious     Learn  more   blog.datalicious.com    

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