Group M Analytics

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Group M Analytics

  1. 1. [  GroupM  Analy.cs  ]   Advanced  analy+cs  training  
  2. 2. [  Company  history  ]  §  Datalicious  was  founded  in  2007  §  Strong  Omniture  web  analy+cs  history  §  One-­‐stop  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)  August  2010   ©  Datalicious  Pty  Ltd   2  
  3. 3. [  Smart  data  driven  marke.ng  ]   Media  A=ribu.on   Op.mise  channel  mix   Targe.ng     Increase  relevance   Tes.ng   Improve  usability   $$$  August  2010   ©  Datalicious  Pty  Ltd   3  
  4. 4. [  Main  business  units  and  services  ]     Data   Insights   Ac.on   PlaForms   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  plaForms   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    August  2010   ©  Datalicious  Pty  Ltd   4  
  5. 5. [  Clients  across  all  industries  ]  August  2010   ©  Datalicious  Pty  Ltd   5  
  6. 6. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Course  overview  ]  August  2010   ©  Datalicious  Pty  Ltd   6  
  7. 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  August  2010   ©  Datalicious  Pty  Ltd   7  
  8. 8. [  Day  2:  Advanced  Analy.cs  ]  §  Campaign  flow  and  media  aZribu+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  August  2010   ©  Datalicious  Pty  Ltd   8  
  9. 9. [  Training  outcomes  ]  §  A^er  successful  comple+on  of  the  training   course  par+cipants  will  be  able  to   –  Define  a  metrics  framework  for  any  client   –  Incorporate  analy+cs  into  the  planning  process   –  Enable  benchmarking  across  campaigns   –  Iden+fy  data  gaps  and  recommend  solu+ons   –  Use  more  than  just  ad  server  data  for  analy+cs   –  Impress  clients  with  insights  not  spreadsheets   –  Know  how  to  extend  op+misa+on  past  media  buy   –  Show  the  true  value  of  digital  media  August  2010   ©  Datalicious  Pty  Ltd   9  
  10. 10. Plenty  of  hands  on  exercises  
  11. 11. [  Prac.ce  session  prepara.on  ]  §  Organise  client  placorm  logins   –  Ad  servers:  DoubleClick,  Atlas,  Eyeblaster,  etc   –  Bid  management:  Google  AdWords,  etc   –  Web  analy+cs:  Google  Analy+cs,  Omniture,  etc   –  Social  media:  Radian6,  S2M,  etc  §  Plus  any  addi+onal  data  or  logins   –  Google  webmaster  tools,  Facebook  fan  pages   –  Phone  calls,  retail  sales,  etc  August  2010   ©  Datalicious  Pty  Ltd   11  
  12. 12. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Metrics  framework  ]  August  2010   ©  Datalicious  Pty  Ltd   12  
  13. 13. [  AIDA  and  AIDAS  formulas  ]   Old  media   New  media   Awareness   Interest   Desire   Ac.on   Sa.sfac.on   Social  media  August  2010   ©  Datalicious  Pty  Ltd   13  
  14. 14. [  Importance  of  social  media  ]   Search   Company   Promo.on   Consumer   WOM,  blogs,  reviews,   ra.ngs,  communi.es,   social  networks,  photo   sharing,  video  sharing  August  2010   ©  Datalicious  Pty  Ltd   14  
  15. 15. [  Social  as  the  new  search  ]  August  2010   ©  Datalicious  Pty  Ltd   15  
  16. 16. [  Simplified  AIDAS  funnel  ]   Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac+on)   (Sa+sfac+on)  August  2010   ©  Datalicious  Pty  Ltd   16  
  17. 17. [  Marke.ng  is  about  people  ]   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  August  2010   ©  Datalicious  Pty  Ltd   17  
  18. 18. [  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  August  2010   ©  Datalicious  Pty  Ltd   18  
  19. 19. Exercise:  Funnel  breakdowns  
  20. 20. [  Exercise:  Funnel  breakdowns  ]  §  List  poten+ally  insighcul  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  August  2010   ©  Datalicious  Pty  Ltd   20  
  21. 21. Exercise:  Conversion  metrics  
  22. 22. [  Exercise:  Conversion  metrics  ]  §  Key  conversion  metrics  differ  by  category   –  Commerce   –  Lead  genera+on   –  Content  publishing   –  Customer  service  August  2010   ©  Datalicious  Pty  Ltd   22  
  23. 23. [Exercise:  Conversion  metrics  ]  August  2010   ©  Datalicious  Pty  Ltd   23   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
  24. 24. [  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  August  2010   ©  Datalicious  Pty  Ltd   24  
  25. 25. [  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  August  2010   ©  Datalicious  Pty  Ltd   25  
  26. 26. [  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   ?   $  August  2010   ©  Datalicious  Pty  Ltd   26  
  27. 27. [  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  August  2010   ©  Datalicious  Pty  Ltd   27  
  28. 28. [  Pion  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  August  2010   ©  Datalicious  Pty  Ltd   28  
  29. 29. [  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  August  2010   ©  Datalicious  Pty  Ltd   29  
  30. 30. [  eMarketer  interac.ve  metrics  ]  August  2010   ©  Datalicious  Pty  Ltd   30  
  31. 31. [  Forrester  interac.ve  metrics  ]   Different     metrics  should   be  viewed  as   complementary   parts  of  the   measurement   jigsaw.  August  2010   ©  Datalicious  Pty  Ltd   31   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
  32. 32. [  Measuring  social  media  ]   Sen+ment   Influence   Reach  August  2010   ©  Datalicious  Pty  Ltd   32  
  33. 33. Exercise:  Metrics  framework  
  34. 34. [  Exercise:  Metrics  framework  ]   Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   Level  2   Strategic   Level  3   Tac.cal  August  2010   ©  Datalicious  Pty  Ltd   34  
  35. 35. [  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   ?   ?   ?  August  2010   ©  Datalicious  Pty  Ltd   35  
  36. 36. [  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   MIAugust  2010   ©  Datalicious  Pty  Ltd   36  
  37. 37. [  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  so^  metrics  to  evaluate  subscriber   percep+ons,  experience,  amtudes  and  word  of  mouth    August  2010   ©  Datalicious  Pty  Ltd   37  
  38. 38. [  Process  is  key  to  success  ]  August  2010   ©  Datalicious  Pty  Ltd   38   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
  39. 39. [  Recommended  resources  ]  §  200501  WAA  Key  Metrics  &  KPIs  §  200708  WAA  Analy+cs  Defini+ons  Volume  1  §  200805  Forrester  Interac+ve  Marke+ng  Metrics  Guide  §  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  §  200612  Razorfish  Ac+onable  Analy+cs  Report  §  200708  Enquiro  Search  Engine  Results  2010  §  201004  Al+meter  Social  Marke+ng  Analy+cs  §  201008  CSR  Customer  Sa+sfac+on  Vs  Delight  August  2010   ©  Datalicious  Pty  Ltd   39  
  40. 40. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Data  sources  ]  August  2010   ©  Datalicious  Pty  Ltd   40  
  41. 41. [  Digital  data  is  plen.ful  and  cheap    ]  August  2010   ©  Datalicious  Pty  Ltd   41   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
  42. 42. [  Digital  data  categories  ]   +Social  August  2010   ©  Datalicious  Pty  Ltd   42   Source:  Accuracy  Whitepaper  for  web  analy+cs,  Brian  Cli^on,  2008  
  43. 43. [  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  August  2010   ©  Datalicious  Pty  Ltd   43  
  44. 44. [  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,  shi^  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  August  2010   ©  Datalicious  Pty  Ltd   44  
  45. 45. [  What  analy.cs  plaForm  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,  shi^  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  August  2010   ©  Datalicious  Pty  Ltd   45  
  46. 46. [  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  August  2010   ©  Datalicious  Pty  Ltd   46  
  47. 47. [  Google  data  in  Singapore]   Source:  hZp://www.hitwise.com/sg/datacentre  August  2010   ©  Datalicious  Pty  Ltd   47  
  48. 48. [  Search  at  all  stages  ]  August  2010   ©  Datalicious  Pty  Ltd   48   Source:  Inside  the  Mind  of  the  Searcher,  Enquiro  2004  
  49. 49. [  Search  and  brand  strength  ]  August  2010   ©  Datalicious  Pty  Ltd   49  
  50. 50. [  Search  and  the  product  lifecycle  ]   Nokia  N-­‐Series   Apple  iPhone  August  2010   ©  Datalicious  Pty  Ltd   50  
  51. 51. [  Search  and  media  planning  ]  August  2010   ©  Datalicious  Pty  Ltd   51  
  52. 52. [  Search  driving  offline  crea.ve  ]  August  2010   ©  Datalicious  Pty  Ltd   52  
  53. 53. Exercise:  Search  insights  
  54. 54. [  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”  August  2010   ©  Datalicious  Pty  Ltd   54  
  55. 55. [  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?  August  2010   ©  Datalicious  Pty  Ltd   55   Source:  Google  Analy+cs,  Jus+n  Cutroni,  2007  
  56. 56. [  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.    August  2010   ©  Datalicious  Pty  Ltd   56   Source:  White  Paper,  RedEye,  2007  
  57. 57. Datalicious  SuperCookie  Persistent  Flash  cookie  that  cannot  be  deleted  
  58. 58. [  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  August  2010   ©  Datalicious  Pty  Ltd   58  
  59. 59. [  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   $  August  2010   ©  Datalicious  Pty  Ltd   59  
  60. 60. Exercise:  Duplica.on  impact  
  61. 61. [  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)  August  2010   ©  Datalicious  Pty  Ltd   61  
  62. 62. Exercise:  Web  analy.cs  
  63. 63. [  Reach  and  channel  overlap  ]   TV     audience   Banner   Search   audience   audience  August  2010   ©  Datalicious  Pty  Ltd   63  
  64. 64. [  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  August  2010   ©  Datalicious  Pty  Ltd   64  
  65. 65. August  2010   ©  Datalicious  Pty  Ltd   65  
  66. 66. Sen.ment  analysis:  People  vs.  machine  
  67. 67. [  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.  August  2010   ©  Datalicious  Pty  Ltd   67  
  68. 68. [  Facebook                insights  ]   Using  Facebook  Like   buZons  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.  August  2010   ©  Datalicious  Pty  Ltd   68  
  69. 69. [  Facebook  Connect  single  sign  on  ]   Facebook  Connect  gives  your   company  the  following  data   and  more  with  just  one  click!     ID,  first  name,  last  name,  middle  name,   picture,  affilia+ons,  last  profile  update,   +me  zone,  religion,  poli+cal  interests,   interests,  sex,  birthday,  aZracted  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  and  email     Need  anything  else?  August  2010   ©  Datalicious  Pty  Ltd   69  
  70. 70. 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)  
  71. 71. Exercise:  Sta.s.cal  significance  
  72. 72. 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”  
  73. 73. 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”  
  74. 74. [  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   ?   $  August  2010   ©  Datalicious  Pty  Ltd   74  
  75. 75. [  Importance  of  calendar  events  ]   Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  August  2010   ©  Datalicious  Pty  Ltd   75  
  76. 76. [  Recommended  resources  ]  §  200311  UK  RedEye  Cookie  Case  Study  §  200807  Kaushik  Tracking  Offline  Conversion  §  200906  WOM  Online  The  People  Vs  Machines  Debate  §  201005  Google  Ad  Planner  Data  Wrong  By  Up  To  20%  §  201005  MPI  How  Sta+s+cally  Valid  Is  Your  Survey  §  201005  Wikipedia  Sta+s+cal  Significance  §  201005  Wikipedia  Sta++cal  Validity  §  201005  Omniture  Campaign  Management  §  200910  Eyeblaster  Global  Benchmark  §  200903  Coremetrics  Conversion  Benchmarks  By  Industry  §  201007  WSJ  The  Webs  New  Gold  Mine  Your  Secrets  §  201008  Adver+singAge  Are  Marketers  Really  Spying  On  You  August  2010   ©  Datalicious  Pty  Ltd   76  
  77. 77. Summary  
  78. 78. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Prac.ce  session  ]  August  2010   ©  Datalicious  Pty  Ltd   78  
  79. 79. Exercise:  Web  analy.cs  
  80. 80. [  Web  analy.cs  plaForm  prac.ce  ]  §  Google  Analy+cs  and  Omniture  SiteCatalyst   –  Placorm  basics  and  comparison   –  Describing  website  visitors   –  Iden+fying  traffic  sources  (reach)   §  Campaign  tracking  mechanics   –  Analyzing  content  usage  (engagement)   –  Analyzing  conversion  drop-­‐out  (conversion)     –  Defining  custom  segments  (funnel  breakdowns)  August  2010   ©  Datalicious  Pty  Ltd   80  
  81. 81. [  Top  5  Omniture  usage  .ps]  §  Bookmark  interes+ng  reports  and  frequently  used  report   semng  right  away  so  they’re  easy  to  find  again  later    §  Use  mul+ple  browser  windows  and  con+nue  browsing  in   a  new  window  once  you  find  an  interes+ng  report  to   facilitate  comparison  and  data  explora+on  §  Set  automa+c  email  alerts  for  all  key  metrics  you  come   across  right  away  so  you  are  always  the  first  to  know   about  anomalies  rather  than  the  client  telling  you  §  Use  short  URLs  next  to  all  graphs  used  in  client   presenta+ons  to  facilitate  naviga+on  to  the  underlying   report  and  to  save  +me  on  poten+al  change  requests  §  Read  the  ‘200708  Omniture  SiteCatalyst  Report   Descrip+ons’  and  ask  for  the  clients’  Solu+on  Design  August  2010   ©  Datalicious  Pty  Ltd   81  
  82. 82. [  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?  August  2010   ©  Datalicious  Pty  Ltd   82  
  83. 83. [  Iden.fying  traffic  sources  ]  §  Genera+ng  de-­‐duplicated  reports  §  Campaign  tracking  mechanics   –  Google  URL  Builder  and  Omniture  SAINT  §  Conversion  goals  and  success  events  §  Plus  adding  addi+onal  metrics  §  Paid  vs.  organic  traffic  sources  §  Branded  vs.  generic  search  §  Traffic  quan+ty  vs.  quality  August  2010   ©  Datalicious  Pty  Ltd   83  
  84. 84. [  Analysing  content  usage  ]  §  Page  traffic  vs.  engagement  §  Entry  vs.  exit  pages  §  Popular  page  paths  §  Internal  search  terms  August  2010   ©  Datalicious  Pty  Ltd   84  
  85. 85. [  Analysing  conversion  drop-­‐out  ]  §  Defining  conversion  funnels  §  Iden+fying  main  problem  pages  §  Pages  visited  a^er  conversion  barriers  §  Conversion  drop-­‐out  by  segment  August  2010   ©  Datalicious  Pty  Ltd   85  
  86. 86. [  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  August  2010   ©  Datalicious  Pty  Ltd   86  
  87. 87. [  Useful  analy.cs  tools  ]  §  hZp://labs.google.com/sets  §  hZp://www.google.com/trends    §  hZp://www.google.com/insights/search  §  hZp://www.google.com/sktool  §  hZp://bit.ly/googlekeywordtoolexternal  §  hZp://www.google.com/webmasters  §  hZp://www.google.com/adplanner  §  hZp://www.google.com/videotarge+ng  §  hZp://www.keywordspy.com    §  hZp://www.compete.com  June  2010   ©  Datalicious  Pty  Ltd   87  
  88. 88. [  Useful  analy.cs  tools  ]  §  hZp://bit.ly/hitwisedatacenter    §  hZp://www.socialmen+on.com  §  hZp://twiZersen+ment.appspot.com  §  hZp://bit.ly/twiZerstreamgraphs  §  hZp://twitrratr.com  §  hZp://bit.ly/listo^ools1    §  hZp://bit.ly/listo^ools2  §  hZp://manyeyes.alphaworks.ibm.com  §  hZp://www.wordle.net  June  2010   ©  Datalicious  Pty  Ltd   88  
  89. 89. Contact  me  cbartens@datalicious.com     Follow  us   twiZer.com/datalicious     Learn  more   blog.datalicious.com    

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