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ExactTarget Media Attribution
 

ExactTarget Media Attribution

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The presentation illustrates and describes media attribution across channels.

The presentation illustrates and describes media attribution across channels.

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    ExactTarget Media Attribution ExactTarget Media Attribution Presentation Transcript

    • >  Media  A)ribu-on  <  Cross-­‐channel  media  a0ribu3on  best   prac3ce  and  technology  op3ons  
    • >  Short  but  sharp  history  §  Datalicious  was  founded  in  late  2007  §  Strong  Omniture  web  analy3cs  history  §  1  of  4  preferred  Omniture  partners  globally  §  Now  360  data  agency  with  specialist  team  §  Combina3on  of  analysts  and  developers  §  Carefully  selected  best  of  breed  partners  §  Driving  industry  best  prac3ce  (ADMA)  §  Turning  data  into  ac3onable  insights  §  Execu3ng  smart  data  driven  campaigns      July  2012   ©  Datalicious  Pty  Ltd   2  
    • >  Smart  data  driven  marke-ng   “Using  data  to  widen  the  funnel”   Media  A)ribu-on  &  Modeling   Op-mise  channel  mix,  predict  sales   Targe-ng  &  Merchandising   Increase  relevance,  reduce  churn   Tes-ng  &  Op-misa-on   Remove  barriers,  drive  sales   Boos-ng  ROMI  July  2012   ©  Datalicious  Pty  Ltd   3  
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  a)ribu-on  July  2012   ©  Datalicious  Pty  Ltd   4  
    • >  The  ideal  media  dashboard  Channel   Investment   ROMI   Return  Brand  equity   ($100)   n/a   $40  Baseline  Offline   $7   330%   $30  TV,  print,  outdoor,  etc  Direct   $1   400%   $5  Direct  mail,  email,  etc  Online   $2   1150%   $25  Search,  display,  social,  etc  
    • >  Duplica-on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Email     Email   Blast   PlaNorm   $   Organic   Google   Search   Analy-cs   $  July  2012   ©  Datalicious  Pty  Ltd   6  
    • >  De-­‐duplica-on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy-cs   PlaNorm   Email     Blast   $   Organic   Search   $  July  2012   ©  Datalicious  Pty  Ltd   7  
    • >  Campaign  flows  are  complex   =  Paid  media   Organic     PR,  WOM,   search   events,  etc   =  Viral  elements   =  Sales  channels   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   CRM   Facebook   program   Twi)er,  etc   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc  July  2012   ©  Datalicious  Pty  Ltd   8  
    • >  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  July  2012   ©  Datalicious  Pty  Ltd   9  
    • >  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    July  2012   ©  Datalicious  Pty  Ltd   10  
    • >  Full  purchase  path  tracking   Introducer   Influencer   Influencer   Closer   $   Paid     Display     Social   Direct     Online   search   ad  clicks   referrals   site  visits   sales   Display     Affiliate   Social     Retail     Offline   ad  views   clicks   buzz   store  visits   sales   TV/print     Organic   Website   Direct  mail,   Life-me   responses   search   events   emails   profit  July  2012   ©  Datalicious  Pty  Ltd   11  
    • >  Path  across  different  segments   Introducer   Influencer   Influencer   Closer   $   Product     Channel  1   Channel  2   Channel  3   Channel  4   A  vs.  B   New   Channel  1   Channel  2   Channel  3   Channel  4   prospects   Exis-ng   Channel  1   Channel  2   Channel  3   Product  4   customers  July  2012   ©  Datalicious  Pty  Ltd   12  
    • >  Tracking  offline  responses  online  §  Search  calls  to  ac3on  for  TV,  radio,  print   –  Unique  search  term  only  adver3sed  in  print  so  all     responses  from  that  term  must  have  come  from  print  §  PURLs  (personalised  URLs)  for  direct  mail   –  Brand.com/clientname  redirects  to  new  URL  that  includes     tracking  parameter  iden3fying  response  as  direct  mail  §  Website  entry  survey  for  direct/branded  visits   –  Survey  website  visitors  that  have  come  to  site  directly     or  via  branded  search  about  their  media  habits,  etc  §  Combine  data  sets  into  media  a0ribu3on  model   –  Combine  raw  data  from  online  purchase  path,  website  entry   survey  and  offline  sales  with  offline  media  placement   informa3on  in  tradi3onal  media  a0ribu3on  model  July  2012   ©  Datalicious  Pty  Ltd   13  
    • >  Search  call  to  ac-on  for  offline    July  2012   ©  Datalicious  Pty  Ltd   14  
    • >  Personalised  URLs  for  direct  mail   VickyCarroll.myspaday.com  >  redirect  to  >  myspaday.com?     CampaignID=DM:123&   Demographics=F|35&   CustomerSegment=A1&   CustomerValue=High&   CustomerSince=2001&   ProductHistory=P1|P2&   NextBestOffer=P3&   ChurnRisk=Low   [...]  July  2012   ©  Datalicious  Pty  Ltd   15  
    • >  Tracking  offline  sales  online  §  Email  click-­‐through   –  Include  offline  sales  flag  in  URL  parameter  in   welcome  email  click-­‐through  URLs  (or  1st  email   newsle0er  aner  offline  sale)  to  trigger  a  custom   ‘assisted  offline  sales’  conversion  event  §  First  login  aner  purchase   –  Similar  to  the  above  method,  however  offline   sales  flag  happens  via  JavaScript  parameter   defined  on  login  rather  than  URL  parameter      July  2012   ©  Datalicious  Pty  Ltd   16  
    • >  Offline  sales  driven  by  online   Adver-sing     Phone   Credit  check,   campaign   order   fulfilment   Retail   Confirma-on   order   email,  1st  login   Website   Online   Online  order   Virtual  order   research   order   confirma-on   confirma-on   Cookie  July  2012   ©  Datalicious  Pty  Ltd   17  
    • >  Understanding  channel  mix  July  2012   ©  Datalicious  Pty  Ltd   18  
    • July  2012   ©  Datalicious  Pty  Ltd   19  
    • >  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  Adver3sing   9%   SEO  Generic   7%   Display  Adver3sing   14%   SEM  Generic   14%   Email  Marke3ng   7%   Display  Adver3sing   7%   Retail  Promo3ons   14%   Affiliate  Marke3ng   9%   Referrals   5%   Conversions  a0ributed  to  search  terms   Email  Marke3ng   7%   that  contain  brand  keywords  and  direct   website  visits  are  most  likely  not  the   origina3ng  channel  that  generated  the   awareness  and  as  such  conversion   credits  should  be  re-­‐allocated.    July  2012   ©  Datalicious  Pty  Ltd   20  
    • >  Adjus-ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10  July  2012   ©  Datalicious  Pty  Ltd   21  
    • >  Purchase  path  example  July  2012   ©  Datalicious  Pty  Ltd   22  
    • >  Media  a)ribu-on  models     Introducer   Influencer   Influencer   Closer   $100   Even     25%   25%   25%   25%   A)rib.   Exclusion   33%   33%   33%   0%   A)rib.   ?   ?   ?   ?   Custom   A)rib.  July  2012   ©  Datalicious  Pty  Ltd   23  
    • >  Media  a)ribu-on  example   Even/weighted   a0ribu3on   Last  click   a0ribu3on   COST  PER  CONVERSION  July  2012   ©  Datalicious  Pty  Ltd   24  
    • >  Purchase  path  vs.  a)ribu-on  §  Important  to  make  a  dis3nc3on  between  media   a0ribu3on  and  purchase  path  tracking   –  Not  the  same,  one  is  necessary  to  enable  the  other  §  Tracking  the  complete  purchase  path,  i.e.  every  paid   and  organic  campaign  touch  point  leading  up  to  a   conversion  is  a  necessary  requirement  to  be  able  to   actually  do  media  a0ribu3on  or  the  alloca3on  or   conversion  credits  back  to  campaign  touch  points     –  Purchase  path  tracking  is  the  data  collec3on  and     media  a0ribu3on  is  the  actual  analysis  or  modelling      July  2012   ©  Datalicious  Pty  Ltd   25  
    • >  Single  source  of  truth  repor-ng   Insights   Repor-ng  July  2012   ©  Datalicious  Pty  Ltd   26  
    • >  Where  to  track  purchase  path   Ad  Server   Web  Analy-cs   Banner  impressions   Referral  visits   Banner  clicks   Social  media  visits   +   Organic  search  visits   Paid  search  clicks   Paid  search  visits   Email  visits,  etc   Lacking  organic  visits   Lacking  ad  impressions   More  granular  &  complex   Less  granular  &  complex  July  2012   ©  Datalicious  Pty  Ltd   27  
    • >  Purchase  path  data  samples  Web  Analy-cs  data  sample  (AD  IMPRESSION  >)  AFFILIATE  >  SEARCH  >  $$$  SEARCH  >  SOCIAL  >  DIRECT  >  $$$    Ad  Server  data  sample  01/01/2012  11:45  AD  IMPRESSION  01/01/2012  12:00  AD  IMPRESSION  01/01/2012  12:05  SEARCH  07/01/2012  17:00  DIRECT  08/01/2012  15:00  $$$  July  2012   ©  Datalicious  Pty  Ltd   28  
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  PlaNorm  op-ons  July  2012   ©  Datalicious  Pty  Ltd   29  
    • >  Purchase  path  op-ons  §  Google  Analy3cs     –  Mul3-­‐channel  funnels  §  Adobe  SiteCatalyst   –  Cross-­‐visit  par3cipa3on  JavaScript  plugin   –  DoubleClick/Mediamind  Genesis  integra3on   –  Light  Server  Calls  §  Atlas,  Mediamind,  DoubleClick,  etc   –  Campaign  touch  point  report  (Mediamind)   –  Raw  cookie  level  data  &  manual  analysis  §  ClearSaleing  July  2012   ©  Datalicious  Pty  Ltd   30  
    • >  Google  Analy-cs  §  Mul3-­‐channel  funnels   –  Concatenates  and  groups  campaign  codes  across  visits  §  Pros   –  Free  of  charge  as  part  of  main  tool   –  Out  of  the  box  reports  that  require  no  addi3onal  tags   –  Does  not  require  data  warehousing  and  custom  analy3cs  §  Cons   –  Raw  data  cannot  be  exported  for  advanced  modelling   –  Cannot  combine  purchase  paths  across  devices   –  Doesn’t  include  ad  impressions  (maybe  DoubleClick  in  the  future)   –  Channels  can  be  grouped  but  not  re-­‐classified  in  the  interface   §  Offline  responses  via  search  call  to  ac3on  show  up  as  search,  etc   –  Platorm  cannot  accommodate  offline  data  (surveys,  CRM,  etc)   –  Purchase  path  data  only,  no  a0ribu3on  modeling  or  ROMI  July  2012   ©  Datalicious  Pty  Ltd   31  
    • July  2012   ©  Datalicious  Pty  Ltd   32  
    • >  Adobe  SiteCatalyst  §  Cross-­‐visit  par3cipa3on  &  light  server  calls   –  Concatenates  campaign  codes  across  visits  into  single  string   –  Light  Server  Call  adds  missing  ad  impressions  into  campaign  code  string  §  DoubleClick/Mediamind  Genesis   –  Imports  last  ad  impression,  click  and  ad  costs  into  SiteCatalyst  as  events  §  Pros   –  Easy  add-­‐on  for  SiteCatalyst  clients,  requires  JavaScript  change  only   –  Doesn’t  require  addi3onal  data  warehousing  as  stored  in  SiteCatalyst   –  Campaign  responses  can  be  re-­‐classified  aner  the  fact  (SAINT)  §  Cons   –  Conflic3ng  un-­‐integrated  solu3ons  (Light  Server  Call  vs.  Genesis)   –  Addi3onal  costs  due  to  a  light  server  call  for  each  ad  impression   –  Light  Server  Call  tracks  ad  impression  but  lacks  more  granular  ad  data   –  Cannot  concatenate  campaign  codes  across  different  top  level  domains   –  Data  can  only  be  exported  as  concatenated  strings  without  3me  stamps   –  Platorm  cannot  integrate  offline  data  into  modelling  (surveys,  CRM,  etc)   –  Basic  a0ribu3on  modelling  only  that  cannot  be  changed  in  retrospect   –  Purchase  path  data  only,  no  flexible  a0ribu3on  modeling  or  ROMI  July  2012   ©  Datalicious  Pty  Ltd   33  
    • July  2012   ©  Datalicious  Pty  Ltd   34  
    • >  Purchase  path  data  samples  Web  Analy-cs  data  sample  LAST  AD  IMPRESSION  >  SEARCH  >  $$$|  PV  $$$  AD  IMPRESSION  >  AD  IMPRESSION  >  SEARCH  >  $$$    Ad  Server  data  sample  01/01/2012  11:45  AD  IMP  YAHOO  HOME  $33  01/01/2012  12:00  AD  IMP  SMH  FINANCE  $33  01/01/2012  12:05  SEARCH  KEYWORD    -­‐  07/01/2012  17:00  DIRECT        $33  08/01/2012  15:00  $$$        $100  July  2012   ©  Datalicious  Pty  Ltd   35  
    • >  Atlas,  Mediamind,  DoubleClick  §  Campaign  touch  points  (Mediamind  only)   –  Last  10  touch  points  before  conversion  aggregated  across  users  §  Raw  cookie  level  data  (all  ad  servers)   –  Full  list  of  all  ad  touch  points  for  each  cookie  ID  §  Pros   –  Low  to  very  low  cost  for  raw  data   –  Complete  raw  data  available  (cookie  level  op3on  only)   –  Increased  flexibility  due  to  complete  and  very  granular  data   –  Campaign  responses  can  be  re-­‐classified  aner  the  fact   –  Solu3on  can  accommodate  offline  data  (surveys,  CRM,  etc)   –  Ad  server  can  track  across  different  domains  due  to  3rd  party  cookie   –  Enables  advanced  ROMI  and  a0ribu3on  modelling  with  offline  data   –  A0ribu3on  model  can  be  changed  in  retrospect  and  recalculated  §  Cons   –  Requires  custom  JavaScript  tags  to  capture  organic  touch  points   –  Requires  addi3onal  data  warehousing  and  custom  analy3cs  July  2012   ©  Datalicious  Pty  Ltd   36  
    • July  2012   ©  Datalicious  Pty  Ltd   37  
    • >  Purchase  path  data  samples  Ad  Server  summary  data  sample  AD  IMPRESSION  >  DIRECT  >  SEARCH  >  $$$  10x  AD  IMPRESSION  >  AFFILIATE  >  SEARCH  >  $$$  5x    Ad  Server  data  sample  UID123  01/01/2012  11:45  AD  IMP  YAHOO    $33  UID123  01/01/2012  12:00  AD  IMP  SMH  $33  UID123  01/01/2012  12:05  SEARCH    -­‐  UID123  07/01/2012  17:00  DIRECT    $33  UID123  08/01/2012  15:00  $$$      $100  July  2012   ©  Datalicious  Pty  Ltd   38  
    • July  2012   ©  Datalicious  Pty  Ltd   39  
    • >  ClearSaleing  §  Pros   –  Fully  managed  media  a0ribu3on  platorm   –  Extensive  out  of  the  box  repor3ng  func3onality     –  Can  customise  a0ribu3on  model  (not  black  box)   –  Supports  ROMI  calcula3on  and  a0ribu3on  modeling  §  Cons   –  Priced  on  percentage  of  media  spend   –  Requires  website  tagging  and  backend  integra3ons   –  Only  pre-­‐processed  data  export,  not  raw  data      July  2012   ©  Datalicious  Pty  Ltd   40  
    • July  2012   ©  Datalicious  Pty  Ltd   41  
    • >  Forrester:  ClearSaleing  ROI/TEI  §  Improved  decision  making  and  media  buying  §  Labor  savings  and  improved  produc3vity  §  Accurate  media  repor3ng  and  improved  visibility  §  Improved  flexibility  and  faster  market  response  §  Transparency  and  consistency  of  metrics  §  Increase  in  online  adver3sing  budget  July  2012   ©  Datalicious  Pty  Ltd   42  
    • Contact  me   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  me   twi)er.com/datalicious    July  2012   ©  Datalicious  Pty  Ltd   43  
    • Data  >  Insights  >  Ac-on  July  2012   ©  Datalicious  Pty  Ltd   44  
    • Upcoming  Tradeshow  &  Field  Marke-ng  Events