Amnesia Omniture Training


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

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Amnesia Omniture Training

  1. 1. >  Omniture  Training  <   Smart  Data  Driven  
  2. 2. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Background  March  2011   ©  Datalicious  Pty  Ltd   2  
  3. 3. >  What  we  do…   Data   Insights   Ac=on   Pla;orms   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  aMribu=on  models   Alterian,  Trac=on,  Inxmail,  etc         Tag-­‐less  online  data  capture   Market  and  compe=tor  trends   Targe=ng  and  merchandising         End-­‐to-­‐end  data  pla;orms   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    March  2011   ©  Datalicious  Pty  Ltd   3  
  4. 4. >  Corporate  data  journey     Stage  1   Stage  2     Stage  3 Data   Insights   Ac=on   Data  is  fully  owned in-­‐house,  advanced   Data  is  being  brought  modelling   in-­‐house,  shiI  towards   and  trigger  based   Third  control   insights  genera.on  and,  i.e.  what     data  mining,  i.e.  why   will  happen  and     most  data,  ad  hoc   did  it  happen?   making  it  happen!  only,  i.e.     what  happened?   Time,  Control  May  2011   ©  Datalicious  Pty  Ltd   4  
  5. 5. >  Smart  data  driven  marke=ng     Metrics  Framework Metrics  Framework Media  AMribu=on Benchmarking  and  trending   Benchmarking  and  trending     Op=mise  channel  mix   Targe=ng     Increase  relevance   Tes=ng   Improve  usability     $$$  March  2011   ©  Datalicious  Pty  Ltd   5  
  6. 6. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Web  Analy=cs    March  2011   ©  Datalicious  Pty  Ltd   6  
  7. 7. >  Measuring  Online  Success   Campaign  spend  ($$$)   Conversion  funnel   Product  page,  start  a  form,  download  content,  watch   %   content,  add  to  shopping  cart,  view  shopping  cart,  cart   checkout,  payment  details,  shipping  informa.on,  order   confirma.on,  applica.on  submiOed,  etc   Conversion  income  ($$$$$)  March  2011   ©  Datalicious  Pty  Ltd   7  
  8. 8. >  Addi=onal  success  metrics     Click   Through   ?   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Product   Call  back   Phone   Through   View   request   Sale   $  March  2011   ©  Datalicious  Pty  Ltd   8  
  9. 9. >  Conversion  Funnel  Maps  March  2011   ©  Datalicious  Pty  Ltd   9  
  10. 10. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac=on   Sa=sfac=on   Social  media  March  2011   ©  Datalicious  Pty  Ltd   10  
  11. 11. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac.on)   (Sa.sfac.on)  March  2011   ©  Datalicious  Pty  Ltd   11  
  12. 12. >  It’s  all  about  people  numbers   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  March  2011   ©  Datalicious  Pty  Ltd   12  
  13. 13. >  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.  March  2011   ©  Datalicious  Pty  Ltd   13  
  14. 14. >  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  March  2011   ©  Datalicious  Pty  Ltd   14  
  15. 15. >  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  March  2011   ©  Datalicious  Pty  Ltd   15  
  16. 16. >  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,  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  May  2011   ©  Datalicious  Pty  Ltd   16  
  17. 17. New  vs.  returning  visitors  
  18. 18. AU/NZ  vs.  rest  of  world  
  19. 19. >  Automa=c  Affinity  Segmenta=on   Skiing   Rugby   Content   Content   Affinity     “Skiing”  March  2011   ©  Datalicious  Pty  Ltd   19  
  20. 20. March  2011   ©  Datalicious  Pty  Ltd   20  
  21. 21. What  is  likely  to    maximise  conversion?  
  22. 22. >  Targe=ng  matrixes   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  March  2011   ©  Datalicious  Pty  Ltd   22  
  23. 23. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  What  is  Omniture?  March  2011   ©  Datalicious  Pty  Ltd   23  
  24. 24. >  Omniture  is  a  BEAST!  March  2011   ©  Datalicious  Pty  Ltd   24  
  25. 25. Omniture  SiteCatalyst  
  26. 26. >  Omniture  SiteCatalyst  Outline    §  How  it  works  §  The  data  structure  §  Key  Variables  §  Classifica.ons  §  Examples  March  2011   ©  Datalicious  Pty  Ltd   26  
  27. 27. >  How  it  works  §  Collect  info  from  the  page  (.tle,  url,   referrer,  sec.on,  content,  .me,  day,  etc)  §  Collect  info  on  the  user  (new/repeat,   segments,  customer/prospect,  interests,   etc)  §  Take  all  the  above  and  request  it  as  a  URL  §  Collect  the  info  above  into  a  database  March  2011   ©  Datalicious  Pty  Ltd   27  
  28. 28. >  Basic  Data  Structure  .me   Visitor  ID   Events   campaign   products   pageName  12:30  Sat…   12345567   event1,event2   p:sem   102,103   travel:why:skiiing  12:31  Sat…   13323222   event3   direct   travel:home  12:32  Sat…   13323222   event5   travel:why:skiiing   •  Every  request  (pageview)  to  Omniture  becomes  a  new  row   •  Think  of  it  as  a  giant  spreadsheet   •  Events  are  grouped  together   •  Products  are  grouped  together   •  Custom  variables  are  also  available     March  2011   ©  Datalicious  Pty  Ltd   28  
  29. 29. >  Events  become  counters  pageName   event1   event2   event3   event4   event5  travel:why:skiiing   1   1   0   0   1  travel:home   0   0   1   0   0   •  This  is  how  reports  show  actual  numbers   •  Events  are  counted  against  each  variable  value  in  the  same   row   •  When  thinking  of  a  visit,  all  rows  of  a  par.cular  visitorID  in  a   session  .me  window  are  added  together     March  2011   ©  Datalicious  Pty  Ltd   29  
  30. 30. >  Types  of  Variables  §  PageName  à  think  site  structure  §  Products  à  what  are  you  selling  §  Campaign  à  where  did  people  come   from  §  Props  à  What  happened  on  a  page  §  Evars  à  Something  you  learned  about   the  visitor  March  2011   ©  Datalicious  Pty  Ltd   30  
  31. 31. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  The  PageName  Variable  March  2011   ©  Datalicious  Pty  Ltd   31  
  32. 32. >  KPIs  Depend  On  Solid  Founda=on   Business   Objec.ves   1 KPIs Web   2 Analy.c   3 Data   Founda.on   Key  Performance  Indicators  also  depend  on  a  solid  founda.on  of  well-­‐ defined  page  names,  content  hierarchy,  and  report  suite  architecture.   Without  these  building  blocks  in  place,  making  decisions  and  taking   ac.on  on  the  data  will  prove  difficult.  
  33. 33. >  User-­‐friendly  Page  Names  §  Defining  a  good  page  naming  strategy  is   one  of  the  most  important  steps  in   Three  C’s  of  Page  Naming   maximizing  Web  analy.cs  success.   Context:  Include  directory  structure  or  content   hierarchy  in  the  page  name  to  help  users  orient  §  In  order  to  help  people  understand  the   the  page  within  the  site  and  simplify  report   filtering.   performance  of  site  content,  TA  should   consider  more  user-­‐friendly   Clarity:  Ensure  the  page  name  is  clear  and  easily   iden.fiable  for  infrequent  users.   page  names.   Conciseness:  Keep  the  page  name  as  short  as  §  You  will  need  to  create  page  names   possible  to  maximize  limited  character  space.   that  are  contextual,  clear,  and  concise.  
  34. 34. >  Structure  of  a  Quality  Page  Name   Will  users  know  what  page  this  is?   Clarity  is  an  overarching  concern  for  the  page   name.   Clarity  Which   Context  neighborhood?  A  Friendly  Page  n  the  URL   Context  focuses  o Name  has  two  parts:  URL  structure   structure  stem,  which  helps   directory:subdirectory:sub-­‐subdirectory:specific  page  name  stem  and  where  a  page   to  iden.fy  specific  page   resides.  name.   URL  structure  stem   Conciseness   Use  “US”  instead  of  “United  States”?     Conciseness  primarily  focuses  on  making  the  URL  structure  stem   as  short  as  possible.  The  specific  page  name  part  should  also  be   as  concise  as  possible  but  most  of  the  emphasis  will  be  on  the   stem.  
  35. 35. >  PageNames  should  be  like  Pyramids  
  36. 36. >  Content  Hierarchy   Site  Sec.on  Level   Page  Type  Different  aggrega.ons  of  content  data  will  allow  you  to  iden.fy   Sub  Sec.on  Level  key  paOerns  at  higher  levels  and  then  drill  into  specific  details  at  lower  levels   Page  Level  
  37. 37. >  Page  Naming  Examples  Good  Page  Naming   vs.   Bad  Page  Naming   §  Clear and user-friendly §  Unclear and confusing §  More concise §  Too long (URLs) §  Easy to filter and search §  Awkward to filter and search §  Consistent format §  Inconsistent format
  38. 38. >  Sample  Page  Naming   Sample  page  name:       Travel:  things  to  do:  snowboarding    site   sec.on   Sub  Sec.on     Page  names  could  leverage  the  bread  crumbs  on   each  page.  The  bread  crumb  captures  the  page   CONTEXT  as  it  reveals  the  loca.on  of  each  page.   The  sec.on,  department,  and  category  of  each   page  should  go  into  each  page  name  for  page   filtering  purposes.   NOTE:  Page  names  should  not  be  exact  copies  of   the  bread  crumb  because  for  example  “Home  >”  is   unnecessary  and  other  text  in  the  bread  crumb   may  need  to  be  abbreviated  for  CONCISE  page   names.  hOp://   ©  2007  Omniture  Inc.,  &  Proprietary  
  39. 39. >  Page  Naming  Op=ons  Recommended  1.  Server-­‐side:  Use  server-­‐side  logic  to  populate  page  name   for  each  web  page.  2.  Hard-­‐code:  Manually  set  page  name  on  each  web  page.  Use  With  Cau2on  3.  PageName  plug-­‐in:  JavaScript  plug-­‐in  strips  “hOp://”  from  URL  page  names.  Not  Recommended  4.  Leave  blank:  SiteCatalyst  defaults  to  page  URL.  5.  Document.=tle:  Uses  the  .tle  of  each  page  instead  of   URL.  
  40. 40. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  The  Campaign  Variable  March  2011   ©  Datalicious  Pty  Ltd   40  
  41. 41. >  External  Campaign  Tracking  § Campaigns  demand  very  special   aOen.on  within  a  global  type   structure.   –  Dont  duplicate  tracking  codes  for  disparate  campaigns   –  Use  structured  tracking  codes:  cid=e:tar:0003  § As  a  best  prac.ce,  we  recommend  uniform  tracking  codes.   Examples:   All   –  cid=a:033007  à  “a” flags  affiliate  campaign     –  cid=e:033007  à  “e” flags  email  campaign   –  cid=sem:g:033007  à”sem:g” signifies  Google  and  is  a  paid   search  campaign   Affiliates   Other  § U.lize  SAINT  to  upload  valuable  meta   Emails   Redirects   data  for  analysis   Online  
  42. 42. >  TNZ  Campaign  Setup  March  2011   ©  Datalicious  Pty  Ltd   42  
  43. 43. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  The  Products  Variable  March  2011   ©  Datalicious  Pty  Ltd   43  
  44. 44. >  The  Products  variable  §  Is  for  things  that  directly  generate  $$  §  Can  store  mul.ple  values  §  Can  store  $  value,  §  Can  increment  other  events  (tax,  etc)  §  Is  commonly  used  for  SKU  codes,   product  codes,  product  names,  etc  March  2011   ©  Datalicious  Pty  Ltd   44  
  45. 45. Product  Classifica=ons  §  Product  pages  are  a  key  focus  of  and   analysis.   12345  §  Populate  the  s.products  variable  with  all  product   IDs  viewed  on  a  page.  §  In  order  to  gain  more  insights  into  these   important  pages,  you  can  leverage  SiteCatalyst’s   SAINT  classifica.on  tool  to  upload  metadata  with   67891   different  product  aOributes  into  SiteCatalyst.  §  Able  to  analyze  the  conversion  performance  of  its   product  pages  by  aggregated  product  aOributes   such  as  category,  subcategory,  region,,   23456   accommoda.on  type,  star,  etc.  §  All  meta  data  is  .ed  to  the  specific  product  id   s.products=";  12345,;  67891,;  23456"    
  46. 46. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Props  March  2011   ©  Datalicious  Pty  Ltd   46  
  47. 47. >  Props  (custom  traffic)  •  Informa.on  related  to  a  par.cular  page  load.  i.e.  not  relevant   outside  the  scope  of  that  pageview  •  You  get  up  to  75  to  customise.  •  They  can  contain  lists  of  mul.ple  items  •  Are  counters  for  things  that  have  names  (as   opposed  to  “events”,  which  are  counters  for  specific  events)  •  Example  usage:  Internal  Search  Keywords,  Tags,  etc  March  2011   ©  Datalicious  Pty  Ltd   47  
  48. 48. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Evars  March  2011   ©  Datalicious  Pty  Ltd   48  
  49. 49. >  Evars  (custom  conversion)   •  Evars  are  like  props  except  the  value  remains  with  the   user  un.l  it  is  set  with  a  different  value   •  Think  of  these  as  labelling  the  user   •  User  them  to  create  segments,  such  as  new/repeat   visitor,  category  affinity,  customer/non-­‐customer,  etc   •  Once  you  set  an  evar  for  a  user,  custom  events  will   con.nue  to  register  against  the  evars  value  March  2011   ©  Datalicious  Pty  Ltd   49  
  50. 50. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Classifica=ons  March  2011   ©  Datalicious  Pty  Ltd   50  
  51. 51. >  Classifica=ons  are  labels   Q.  Show  me  revenue  by  brand?   Q.  Show  me  which  product  categories  had  the  most  views?   Q.  Did  we  see  increased  traffic  for  products  we  recently  adver.sed  on  TV?    product   Category   Name   Brand   Profit  Band   TV  Ad  -­‐  7  days  001   TV   Samsung  plasma   Samsung   High   No  002   Internet   NetGear  N50   Netgear   Low   No  003   Internet   Netgear  G800   Netgear   Medium   No  004   Mobile  Phone   Samsung  Galaxy   Samsung   Low   Yes  005   Accessory   Headphones   Sony   Low   No  006   TV   Sony  LCD   Sony   High   Yes  007   Internet   Wireless  modem   Alcatel   Low   No   March  2011   ©  Datalicious  Pty  Ltd   51  
  52. 52. >  SAINT:  Campaigns  Classifica=ons   XID  §  By  u.lizing  the  SAINT  classifica.on  template,  you  can   AOributes   con.nue  to  upload  meta  data  for  each  variable  §  Meta  data  examples:   SAINT   –  Campaign:  Name,  Channel,  Owner,  Paid  vs  Nonpaid,   Branded  keywords  vs  non-­‐branded,  etc   –  Product:  Name,  Category,  Brand,  etc   –  Customer  ID:  Profitability,  segment,  churn  risk,   demographics,  loca=on,  etc  §  The  process  of  assigning  aOributes  through  SAINT  can  be   automated  via  FTP  §  Classifica.ons  can  be  updated  at  any  .me  and  will   change  all  data  retrospec.vely,  because  they  are  a  label   and  do  not  change  the  underlying  variable  value  and  the   data  recorded  against  it.  
  53. 53. March  2011   ©  Datalicious  Pty  Ltd   53  
  54. 54. Things  to  think  about  
  55. 55. >  Cookie  expira=on  impact   Paid     Bid     Search   Mgmt   $   Banner     Banner     Ad     Ad  Click   Ad  View   Server   $   Email     Email   Expira=on   Blast   Pla;orm   $   Organic   Google   Search   Analy=cs   $  March  2011   ©  Datalicious  Pty  Ltd   55  
  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.    March  2011   ©  Datalicious  Pty  Ltd   56   Source:  White  Paper,  RedEye,  2007  
  57. 57. >  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  March  2011   ©  Datalicious  Pty  Ltd   57  
  58. 58. Omniture  Test  and  Target  
  59. 59. >  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  March  2011   ©  Datalicious  Pty  Ltd   59  
  60. 60. >  Prospect  targe=ng  parameters    March  2011   ©  Datalicious  Pty  Ltd   60  
  61. 61. >  Affinity  re-­‐targe=ng  in  ac=on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity,     response  rates  are     liIed  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  hMp://   12  Month  Caps   - + - +March  2011   ©  Datalicious  Pty  Ltd   61  
  62. 62. >  Customer  profiling  in  ac=on     Using  website  and  email  responses   to  learn  a  liOle  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  May  2011   ©  Datalicious  Pty  Ltd   62  
  63. 63. ©  Datalicious  Pty  Ltd   63  
  64. 64. >  Tes=ng  matrixes   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   ?   ?   ?   ?   ?   ?   ?   ?  March  2011   ©  Datalicious  Pty  Ltd   64  
  65. 65. >  Keys  to  effec=ve  targe=ng     1.  Define  success  metrics   2.  Define  and  validate  segments   3.  Develop  and  message  matrix     4.  Transform  matrix  into  business  rules   5.  Develop  and  test  content   6.  Start  and  automate   7.  Keep  and  refining   8.  Communicate  results  March  2011   ©  Datalicious  Pty  Ltd   65  
  66. 66. SuperTagging  
  67. 67. >  SuperTag  code  architecture     §  Central  JavaScript  container  tag   §  One  tag  for  all  sites  and  pla|orms   §  Hosted  internally  or  externally   §  Faster  tag  implementa.on/updates   §  Eliminates  JavaScript  caching   §  Enables  code  on  live  site   §  Enables  heat  map  implementa.on   §  Enables  redirects  for  A/B   §  Enables  network  wide  re-­‐   §  Enables  live  chat  implementa.on  March  2011   ©  Datalicious  Pty  Ltd   67  
  68. 68. Appendix  
  69. 69. PageNaming  Op=ons  –  Pros  /  Cons     Pros   Cons   Server-side —  Provides flexibility to create —  Only applies to dynamic pages friendly page names —  Flexibility depends on platform —  Logic automates page —  Ongoing diligence to ensure logic doesn’t break in future naming process Hard-code —  Free to create friendly page —  Can be labor-intensive name that isn’t bound by —  Doesn’t apply to dynamic pages URL or page title —  Must be careful when using pages as boilerplates Page Name —  Requires less effort to create —  Only recommended for sites with well-defined URL page names structures Plug-in —  Shortens URL page names —  Can still lead to long page names and makes them more —  Won’t aggregate a page with different URL variations manageable —  Problematic with dynamic pages Leave Blank —  Requires no effort —  URL page names can be long and unwieldy —  Limited character space is wasted on domain root (defaults to URL) —  Won’t aggregate a page with different URL variations —  Problematic with dynamic pages Document.title —  Requires little effort to set —  Page titles aren’t always unique to a page up —  Can be long and contain unnecessary keywords (SEO) —  May change frequently (SEO) —  Problems caused by translation tools (e.g., Babel Fish)