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Group M Analytics (Part 2)

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The presentation discusses course training on advanced analytics.

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Group M Analytics (Part 2)

  1. 1. [  GroupM  Analy.cs  ]   Advanced  analy+cs  training  
  2. 2. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Quick  recap  ]  August  2010   ©  Datalicious  Pty  Ltd   2  
  3. 3. [  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   3  
  4. 4. [  Day  1:  Basic  Analy.cs  ]  §  Hands-­‐on  exercises  and  examples   –  Funnel  breakdowns   –  Conversions  metrics   –  Metrics  framework   –  Search  insights   –  Duplica+on  impact   –  Sta+s+cal  significance  August  2010   ©  Datalicious  Pty  Ltd   4  
  5. 5. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Course  overview  ]  August  2010   ©  Datalicious  Pty  Ltd   5  
  6. 6. [  Day  2:  Advanced  Analy.cs  ]  §  Campaign  flow  and  media  aSribu+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   6  
  7. 7. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Media  a?ribu.on  ]  August  2010   ©  Datalicious  Pty  Ltd   7  
  8. 8. [  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  August  2010   ©  Datalicious  Pty  Ltd   8  
  9. 9. Exercise:  Campaign  flow  
  10. 10. [  Unique  calls  to  ac.on  ]  §  Unique  click-­‐through  URLs  §  Unique  vanity  domains  or  URLs  §  Unique  phone  numbers  §  Unique  search  terms  §  Unique  email  addresses  §  Unique  personal  URLs  (PURLs)  §  Unique  SMS  numbers,  QR  codes  §  Unique  promo+onal  codes,  vouchers  §  Geographic  loca+on  (Facebook,  FourSquare)  §  Regression  analysis  of  cause  and  effect  August  2010   ©  Datalicious  Pty  Ltd   10  
  11. 11. [  Search  call  to  ac.on  for  offline  ]  August  2010   ©  Datalicious  Pty  Ltd   11  
  12. 12. [  Reach  and  channel  overlap  ]   TV     audience   Banner   Search   audience   audience  August  2010   ©  Datalicious  Pty  Ltd   12  
  13. 13. [  Indirect  display  impact  ]  August  2010   ©  Datalicious  Pty  Ltd   13  
  14. 14. [  Indirect  display  impact  ]  August  2010   ©  Datalicious  Pty  Ltd   14  
  15. 15. [  De-­‐duplica.on  across  channels  ]   Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Central   Analy.cs   Pla^orm   Email     Email   Blast   Pla^orm   $   Organic   Google   Search   Analy.cs   $  August  2010   ©  Datalicious  Pty  Ltd   15  
  16. 16. [  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  August  2010   ©  Datalicious  Pty  Ltd   16  
  17. 17. [  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    August  2010   ©  Datalicious  Pty  Ltd   17  
  18. 18. [  Paid  and  organic  stacking  ]  August  2010   ©  Datalicious  Pty  Ltd   18  
  19. 19. [  Full  path  to  purchase  ]   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Generic   Click   Visit   Branded   $   Banner     SEO   Affiliate   Social   View   Generic   Click   Media   $   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update  August  2010   ©  Datalicious  Pty  Ltd   19  
  20. 20. August  2010   ©  Datalicious  Pty  Ltd   20  
  21. 21. [  Impact  of  cookie  expira.on  ]   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Generic   Click   Visit   Branded   $   Banner     SEO   Affiliate   Social   View   Generic   Click   Media   $   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update  August  2010   ©  Datalicious  Pty  Ltd   21  
  22. 22. [  Success  a?ribu.on  models  ]   Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   A?rib.   Exclusion   33%   33%   33%   0%   A?rib.   Pa?ern   30%   20%   20%   30%   A?rib.  August  2010   ©  Datalicious  Pty  Ltd   22  
  23. 23. [  Forrester  media  a?ribu.on  ]   Forrester  adds   another  dimension   to  media  aSribu+on   by  sugges+ng  to   change  the  allocated   credit  for  each   campaign  touch   point  based  on   addi+onal  factors   such  as  site   interac+on.  August  2010   ©  Datalicious  Pty  Ltd   23   Source:  Forrester,  2009  
  24. 24. Exercise:  A?ribu.on  model  
  25. 25. [  Exercise:  A?ribu.on  models  ]   Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   A?rib.   Exclusion   33%   33%   33%   0%   A?rib.   ?   ?   ?   ?   Custom   A?rib.  August  2010   ©  Datalicious  Pty  Ltd   25  
  26. 26. [  Exercise:  A?ribu.on  model  ]  §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  strong   baseline  to  s+mulate  repeat  purchases    §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  direct   response  focus  §  Allocate  more  conversion  credits  to  ini+a+ng   touch  points  for  new  and  expensive  brands  and   products  to  insert  them  into  the  mindset  August  2010   ©  Datalicious  Pty  Ltd   26  
  27. 27. [  Understanding  channel  overlap  ]   Direct,   Paid     Organic   Display     Affiliates,   Email   Channel   Branded   Search   Search   Ads   Partners   Updates   Direct,   n/a   Branded   Paid   n/a   Search   Organic   n/a   Search   Display     n/a   Ads   Affiliates   n/a   Partners   Email   Updates   display  >  sem  >  seo  >  affiliate  >  email  >  direct  >  $$$   n/a  August  2010   ©  Datalicious  Pty  Ltd   27  
  28. 28. [  Understanding  channel  overlap  ]   DM   Call  Centre   eDMs   Radio   Paid   Search   Display     Ads   Organic   Direct   Search   Partners  August  2010   ©  Datalicious  Pty  Ltd   28  
  29. 29. [  Website  entry  survey  ]   De-­‐duped  Campaign  Report   Greatest  Influencer  on  Branded  Search  /  STS   }   Channel   %  of  Conversions   Channel   %  of  Influence   Straight  to  Site   27%   Word  of  Mouth   32%   SEO  Branded   15%   Blogging  &  Social  Media   24%   SEM  Branded   9%   Newspaper  Adver+sing   9%   SEO  Generic   7%   Display  Adver+sing   14%   SEM  Generic   14%   Email  Marke+ng   7%   Display  Adver+sing   7%   Retail  Promo+ons   14%   Affiliate  Marke+ng   9%   Referrals   5%   Conversions  aSributed  to  search  terms   Email  Marke+ng   7%   that  contain  brand  keywords  and  direct   website  visits  are  most  likely  not  the   origina+ng  channel  that  generated  the   awareness  and  as  such  conversion   credits  should  be  re-­‐allocated.    August  2010   ©  Datalicious  Pty  Ltd   29  
  30. 30. [  Ad  server  exposure  test  ]   1   User  qualifies  for  the  display  campaign   (if  the  user  has  already  been  tagged  go  to  step  3)   1st  impression   2   Audience  Segmenta.on   10%  of  users  in  control  group,  90%  in  exposed  group   User  tagged  with  segment   Measurement:   Conversions  per   1000  unique   Control   Exposed   visitors   (displayed  non-­‐branded  message)   (displayed  branded  message)   User  remains  in  segment   N  impressions   3   Control   Exposed   (displayed  non-­‐branded  message)   (displayed  branded  message)  August  2010   ©  Datalicious  Pty  Ltd   30  
  31. 31. [  Research  online,  shop  offline  ]  August  2010   ©  Datalicious  Pty  Ltd   31   Source:  2008  Digital  Future  Report,  Surveying  The  Digital  Future,  Year  Seven,  USC  Annenberg  School  
  32. 32. [  Track  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  August  2010   ©  Datalicious  Pty  Ltd   32  
  33. 33. Exercise:  Offline  conversions  
  34. 34. [  Exercise:  Offline  conversions  ]  §  Email  click-­‐through  aner  purchase  §  First  online  login  aner  purchase  §  Unique  website  phone  number  §  Unique  website  promo+on  code  §  Unique  printable  vouchers  §  Store  locator  searches  §  Make  an  appointment  online  August  2010   ©  Datalicious  Pty  Ltd   34  
  35. 35. [  Media  a?ribu.on  phases  ]  §  Phase  1:  De-­‐duplica+on   –  Conversion  de-­‐duplica+on  across  all  channels   –  Requires  one  central  repor+ng  plaoorm   –  Limited  to  first/last  click  aSribu+on  §  Phase  2:  Direct  response  pathing   –  Response  pathing  across  paid  and  organic  channels   –  Only  covers  clicks  and  not  mere  banner  views   –  Can  be  enabled  in  Google  Analy+cs  and  Omniture  §  Phase  3:  Full  purchase  path   –  Direct  response  tracking  including  banner  exposure   –  Cannot  be  done  in  Google  Analy+cs  or  Omniture   –  Easier  to  import  addi+onal  channels  into  ad  server  August  2010   ©  Datalicious  Pty  Ltd   35  
  36. 36. [  Recommended  resources  ]  §  200812  ComScore  How  Online  Adver+sing  Works  §  200905  iProspect  Research  Study  Search  And  Display  §  200902  Forrester  Mul+-­‐Campaign  ASribu+on  §  200904  ClearSaleing  American  ASribu+on  Index  §  201003  Datalicious  Tying  Offline  Sales  To  Online  Media  August  2010   ©  Datalicious  Pty  Ltd   36  
  37. 37. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Reducing  waste  ]  August  2010   ©  Datalicious  Pty  Ltd   37  
  38. 38. [  Reducing  waste  along  the  funnel  ]   Media  a?ribu.on   Op.mising  channel  mix   Targe.ng     Increasing  relevance   Tes.ng   Improving  usability   $$$  August  2010   ©  Datalicious  Pty  Ltd   38  
  39. 39. [  Increase  revenue  by  10-­‐20%  ]   By  coordina.ng  the  consumer’s  end-­‐to-­‐end  experience,   companies  could  enjoy  revenue  increases  of  10-­‐20%.   Google:  “get  more  value  from  digital  marke.ng”     or  h?p://bit.ly/cAtSUN  August  2010   ©  Datalicious  Pty  Ltd   39   Source:  McKinsey  Quarterly,  2010  
  40. 40. [  The  consumer  data  journey  ]   To  transac.onal  data   To  reten.on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages  August  2010   ©  Datalicious  Pty  Ltd   40  
  41. 41. [  Prospect  targe.ng  parameters  ]  August  2010   ©  Datalicious  Pty  Ltd   41  
  42. 42. [  Coordina.on  across  channels  ]       Genera.ng   Crea.ng   Maximising   awareness   engagement   revenue   TV,  radio,  print,   Retail  stores,  call   Outbound  calls,  direct   outdoor,  search   centers,  brochures,   mail,  emails,  SMS,  etc   marke+ng,  display   websites,  landing   ads,  performance   pages,  mobile  apps,   networks,  affiliates,   online  chat,  etc   social  media,  etc   Off-­‐site   On-­‐site   Profile     targe.ng   targe.ng   targe.ng  August  2010   ©  Datalicious  Pty  Ltd   42  
  43. 43. [  Combining  targe.ng  pla^orms  ]   Off-­‐site   targe+ng   Profile   On-­‐site   targe+ng   targe+ng  August  2010   ©  Datalicious  Pty  Ltd   43  
  44. 44. [  Combining  technology  ]   On-­‐site     Off-­‐site   segments   segments  August  2010   ©  Datalicious  Pty  Ltd   44  
  45. 45. August  2010   ©  Datalicious  Pty  Ltd   45  
  46. 46. August  2010   ©  Datalicious  Pty  Ltd   46  
  47. 47. [  Datalicious  SuperTag  ]   §  Central  JavaScript  based  container  tag   §  One  tag  for  all  plaoorms  incl.  Omniture   §  Either  hosted  internally  or  externally   §  Faster  tag  implementa+on  and  updates   §  Consistent  network  wide  re-­‐targe+ng   §  Transfer  or  profiling  data  between  sites   §  Iden+fica+on  of  exis+ng  customers   §  Re-­‐targe+ng  by  brand  preferences  August  2010   ©  Datalicious  Pty  Ltd   47  
  48. 48. [  Combining  data  sets  ]   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  August  2010   ©  Datalicious  Pty  Ltd   48  
  49. 49. [  Behaviours  plus  transac.ons  ]   Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collec+on  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   predic+ve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promo+on  responses   historical  data  from  previous  transac+ons   emails,  internal  search,  etc   average  order  value,  points,  etc   UPDATED  CONTINUOUSLY   UPDATED  OCCASIONALLY  August  2010   ©  Datalicious  Pty  Ltd   49  
  50. 50. [  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   50  
  51. 51. [  Sample  customer  level  data  ]  August  2010   ©  Datalicious  Pty  Ltd   51  
  52. 52. [  Sample  site  visitor  composi.on  ]   30%  new  visitors  with  no   30%  repeat  visitors  with   previous  website  history   referral  data  and  some   aside  from  campaign  or   website  history  allowing   referrer  data  of  which   50%  to  be  segmented  by   maybe  50%  is  useful   content  affinity   30%  exis.ng  customers  with  extensive   10%  serious   profile  including  transac+onal  history  of   prospects   which  maybe  50%  can  actually  be   with  limited   iden+fied  as  individuals     profile  data  August  2010   ©  Datalicious  Pty  Ltd   52  
  53. 53. Exercise:  Targe.ng  matrix  
  54. 54. [  Exercise:  Targe.ng  matrix  ]   Phase   Segment  A   Segment  B   Channels   Awareness   Considera.on   Purchase  Intent   Up/Cross-­‐Sell  August  2010   ©  Datalicious  Pty  Ltd   54  
  55. 55. [  Exercise:  Targe.ng  matrix  ]   Phase   Segment  A   Segment  B   Channels   Social,  display,   Awareness   Seen  this?   search,  etc   Social,  search,   Considera.on   Great  feature!   website,  etc   Search,  site,   Purchase  Intent   Great  value!   emails,  etc   Direct  mail,   Up/Cross-­‐Sell   Add  this!   emails,  etc  August  2010   ©  Datalicious  Pty  Ltd   55  
  56. 56. [  Exercise:  Targe.ng  matrix  ]   Phase   Segment  A   Segment  B   Data  Points   Awareness   Seen  this?   Default   Download,   Considera.on   Great  feature!   product  view   Cart  add,   Purchase  Intent   Great  value!   checkout,  etc   Email  response,   Up/Cross-­‐Sell   Add  this!   login,  etc  August  2010   ©  Datalicious  Pty  Ltd   56  
  57. 57. [  Poten.al  landing  page  layout  ]   Passing  data  on  user   Branded  header   preferences  through   to  the  website  via   Email  or  campaign  message  match   parameters  in  email   click-­‐through  URLs     to  customise   content  delivery.   Targeted  offers   Call  to  ac.on  August  2010   ©  Datalicious  Pty  Ltd   57  
  58. 58. [  Poten.al  newsle?er  layout  ]   Using  data  on   Rule  based  header  theme   website  behaviour   imported  into  the   Data  verifica.on   NPS   email  delivery   plaoorm  to  build   business  rules  to   Rule  based  offer   customise  content   Closest     stores,     delivery.   Profile  based  offer   offers     etc  August  2010   ©  Datalicious  Pty  Ltd   58  
  59. 59. [  Affinity  targe.ng  in  ac.on  ]   Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe+ng,     response  rates  are     lined  significantly     across  products.   CTR  By  Category  Affinity   Message   Postpay   Prepay   Broadb.   Business   Blackberry  Bold   - - - + Google:  “vodafone   5GB  Mobile  Broadband   - - + - omniture  case  study”     Blackberry  Storm   + - + + or  h?p://bit.ly/de70b7   12  Month  Caps   - + - +August  2010   ©  Datalicious  Pty  Ltd   59  
  60. 60. [  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.”  August  2010   ©  Datalicious  Pty  Ltd   60  
  61. 61. [  ClickTale  tes.ng  case  study  ]  August  2010   ©  Datalicious  Pty  Ltd   61  
  62. 62. [  Bad  campaign  worse  than  none  ]  August  2010   ©  Datalicious  Pty  Ltd   62  
  63. 63. [  Recommended  resources  ]  §  201003  McKinsey  Get  More  Value  From  Digital  Marke+ng  §  200912  Unbounce  101  Landing  Page  Op+miza+on  Tips  §  201008  eConsultancy  TV  Ad  Landing  Pages  §  200910  eMarketer  Bad  Campaign  Worse  Than  None  §  201003  WebCredible  10  Unexpected  User  Behaviours  §  200910  Myth  Of  The  Page  Fold  §  201008  Sample  Size  Currency  Of  Marke+ng  Tes+ng  §  200409  Roy  Taguchi  Or  MV  Tes+ng  For  Marketers  §  200702  Internet  Retailer  Naviga+ng  Depths  Of  MV  Tes+ng  August  2010   ©  Datalicious  Pty  Ltd   63  
  64. 64. Summary  
  65. 65. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Prac.ce  session  ]  August  2010   ©  Datalicious  Pty  Ltd   65  
  66. 66. Exercise:  Web  analy.cs  
  67. 67. [  Web  analy.cs  pla^orm  prac.ce  ]  §  Google  Analy+cs  and  Omniture  SiteCatalyst   –  Plaoorm  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   67  
  68. 68. [  Top  5  Omniture  usage  .ps]  §  Bookmark  interes+ng  reports  and  frequently  used  report   sevng  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   68  
  69. 69. [  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   69  
  70. 70. [  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   70  
  71. 71. [  Analysing  content  usage  ]  §  Page  traffic  vs.  engagement  §  Entry  vs.  exit  pages  §  Popular  page  paths  §  Internal  search  terms  August  2010   ©  Datalicious  Pty  Ltd   71  
  72. 72. [  Analysing  conversion  drop-­‐out  ]  §  Defining  conversion  funnels  §  Iden+fying  main  problem  pages  §  Pages  visited  aner  conversion  barriers  §  Conversion  drop-­‐out  by  segment  August  2010   ©  Datalicious  Pty  Ltd   72  
  73. 73. [  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   73  
  74. 74. [  Useful  analy.cs  tools  ]  §  hSp://labs.google.com/sets  §  hSp://www.google.com/trends    §  hSp://www.google.com/insights/search  §  hSp://www.google.com/sktool  §  hSp://bit.ly/googlekeywordtoolexternal  §  hSp://www.google.com/webmasters  §  hSp://www.google.com/adplanner  §  hSp://www.google.com/videotarge+ng  §  hSp://www.keywordspy.com    §  hSp://www.compete.com  June  2010   ©  Datalicious  Pty  Ltd   74  
  75. 75. [  Useful  analy.cs  tools  ]  §  hSp://bit.ly/hitwisedatacenter    §  hSp://www.socialmen+on.com  §  hSp://twiSersen+ment.appspot.com  §  hSp://bit.ly/twiSerstreamgraphs  §  hSp://twitrratr.com  §  hSp://bit.ly/listonools1    §  hSp://bit.ly/listonools2  §  hSp://manyeyes.alphaworks.ibm.com  §  hSp://www.wordle.net  June  2010   ©  Datalicious  Pty  Ltd   75  
  76. 76. Contact  me  cbartens@datalicious.com     Follow  us   twiSer.com/datalicious     Learn  more   blog.datalicious.com    

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