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SunCorp Analytics
 

SunCorp Analytics

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The presentation discusses the impact of data driven targeting to marketing campaigns.

The presentation discusses the impact of data driven targeting to marketing campaigns.

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    SunCorp Analytics SunCorp Analytics Presentation Transcript

    • >  Suncorp  Analy.cs  <   Smart  data  driven  marke-ng  
    • >  Short  but  sharp  history  §  Datalicious  was  founded  late  2007  §  Strong  Omniture  web  analy-cs  history  §  Now  360  data  agency  with  specialist  team  §  Combina-on  of  analysts  and  developers  §  Carefully  selected  best  of  breed  partners  §  Evangelizing  smart  data  driven  marke-ng  §  Making  data  accessible  and  ac-onable  §  Driving  industry  best  prac-ce  (ADMA)  November  2010   ©  Datalicious  Pty  Ltd   2  
    • >  Clients  across  all  industries  November  2010   ©  Datalicious  Pty  Ltd   3  
    • >  Wide  range  of  data  services   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  aKribu.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  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    November  2010   ©  Datalicious  Pty  Ltd   4  
    • >  Smart  data  driven  marke.ng   Media  AKribu.on   Op.mise  channel  mix   Targe.ng     Increase  relevance   Tes.ng   Improve  usability   $$$  November  2010   ©  Datalicious  Pty  Ltd   5  
    • 15 tools are proposed largely centred on Adobe Once implemented these tools will deliver the capability for each LOB to leverage online data to optimise the customers journey across any channel or brand within the Suncorp group CUSTOMER JOURNEY CAPABILITYCAPABILITY TOOL AQUIRE CONVERT GROW EVOLUTION Multi-Media 1. Digital campaign tracking & attribution ü ü üMeasurement & Attribution 2. Full digital pathway tracking & attribution ü ü ü 3. Onsite promotion tracking ü Increased Personalisation 4. Fallout & conversion analysis ü 5. Adv site navigation & form analysis ü Site Anonymous Optimisation 6. Internal search analysis ü 7. Brand portfolio level measurement ü ü ü 8. Site surveys ü 9. Advanced visitor segmentation ü ü ü Advanced 10. A/B & content testing ü ü üSegmentation & Targeting 11. Campaign retargeting ü ü 12. Behavioural targeting ü ü Semi-Identified Personalised 13. Syndicate personalised content ü Targeting 14. Digital identification & CRM integration ü ü ü IdentifiedMulti-Channel 15. Online to offline conversion ü ü Optimisation Optimised Optimised Optimised channel mix customer OptimisedSTRATEGIC GOAL DELIVERED marketing mix RIGHT OFFER RIGHT CHANNEL RIGHT CUSTOMER Customer Journey 6
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  aKribu.on  November  2010   ©  Datalicious  Pty  Ltd   7  
    • >  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   TwiKer,  etc   C3   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc  November  2010   ©  Datalicious  Pty  Ltd   8  
    • Exercise:  Campaign  flow  November  2010   ©  Datalicious  Pty  Ltd   9  
    • >  Duplica.on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Email     Email   Blast   Pla>orm   $   Organic   Google   Search   Analy.cs   $  November  2010   ©  Datalicious  Pty  Ltd   10  
    • >  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   $  November  2010   ©  Datalicious  Pty  Ltd   11  
    • >  De-­‐duplica.on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy.cs   Pla>orm   Email     Blast   $   Organic   Search   $  November  2010   ©  Datalicious  Pty  Ltd   12  
    • Exercise:  Duplica.on  impact  November  2010   ©  Datalicious  Pty  Ltd   13  
    • >  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)  November  2010   ©  Datalicious  Pty  Ltd   14  
    • Quick  win:  Central  pla>orm  November  2010   ©  Datalicious  Pty  Ltd   15  
    • >  Reach  and  channel  overlap     TV/Print     audience   Banner   Search   audience   audience  November  2010   ©  Datalicious  Pty  Ltd   16  
    • >  Indirect  display  impact    November  2010   ©  Datalicious  Pty  Ltd   17  
    • >  Indirect  display  impact    November  2010   ©  Datalicious  Pty  Ltd   18  
    • >  Indirect  display  impact    November  2010   ©  Datalicious  Pty  Ltd   19  
    • >  Success  aKribu.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  November  2010   ©  Datalicious  Pty  Ltd   20  
    • >  First  and  last  click  aKribu.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    November  2010   ©  Datalicious  Pty  Ltd   21  
    • >  Adobe  stacking/par.cipa.on   Adobe  can  only  stack   direct  paid  and  organic   responses  that  end  up  on   your  website  proper.es,   mere  banner  impressions   are  missing  from  the  stack   and  cannot  be  included   via  Genesis  a`er  the  fact.  November  2010   ©  Datalicious  Pty  Ltd   22  
    • >  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update  November  2010   ©  Datalicious  Pty  Ltd   23  
    • >  Where  to  collect  the  data     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  banner  impressions   More  granular  &  complex   Less  granular  &  complex  November  2010   ©  Datalicious  Pty  Ltd   24  
    • Quick  win:  Data  into  ad  server   November  2010   ©  Datalicious  Pty  Ltd   25  
    • >  Search  call  to  ac.on  for  offline    November  2010   ©  Datalicious  Pty  Ltd   26  
    • Offline  response  tracking  and  improved  experience   November  2010   ©  Datalicious  Pty  Ltd   27  
    • Quick  win:  Search  call  to  ac.on   November  2010   ©  Datalicious  Pty  Ltd   28  
    • November  2010   ©  Datalicious  Pty  Ltd   29  
    • hKp://www.suncorp.com.au?campaign=workshop   November  2010   ©  Datalicious  Pty  Ltd   30  
    • >  Poten.al  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)  §  Plus  regression  analysis  of  cause  and  effect  November  2010   ©  Datalicious  Pty  Ltd   31  
    • >  Unique  phone  numbers  §  1  unique  phone  number     –  Phone  number  is  considered  part  of  the  brand   –  Media  origin  of  calls  cannot  be  established   –  Added  value  of  website  interac-on  unknown  §  2-­‐10  unique  phone  numbers   –  Different  numbers  for  different  media  channels   –  Exclusive  number(s)  reserved  for  website  use   –  Call  origin  data  more  granular  but  not  perfect   –  Difficult  to  rotate  and  pause  numbers  November  2010   ©  Datalicious  Pty  Ltd   32  
    • >  Unique  phone  numbers  §  10+  unique  phone  numbers   –  Different  numbers  for  different  media  channels   –  Different  numbers  for  different  product  categories   –  Different  numbers  for  different  conversion  steps   –  Call  origin  becoming  useful  to  shape  call  script   –  Feasible  to  pause  numbers  to  improve  integrity  §  100+  unique  phone  numbers   –  Different  numbers  for  different  website  visitors   –  Call  origin  and  -me  stamp  enable  individual  match   –  Call  conversions  matched  back  to  search  terms  November  2010   ©  Datalicious  Pty  Ltd   33  
    • >  Jet  Interac.ve  phone  call  data  November  2010   ©  Datalicious  Pty  Ltd   34  
    • Quick  win:  Unique  numbers  November  2010   ©  Datalicious  Pty  Ltd   35  
    • >  PURLs  boos.ng  DM  response  rates   Text  November  2010   ©  Datalicious  Pty  Ltd   36  
    • >  Media  aKribu.on  phases    §  Phase  1:  De-­‐duplica-on   –  Conversion  de-­‐duplica-on  across  all  channels   –  Requires  one  central  repor-ng  plaiorm   –  Limited  to  first/last  click  ajribu-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  November  2010   ©  Datalicious  Pty  Ltd   37  
    • >  Combining  data  sources  November  2010   ©  Datalicious  Pty  Ltd   38  
    • >  Single  source  of  truth  repor.ng   Insights   Repor.ng  November  2010   ©  Datalicious  Pty  Ltd   39  
    • >  Understanding  channel  mix  November  2010   ©  Datalicious  Pty  Ltd   40  
    • >  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  ajributed  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.    November  2010   ©  Datalicious  Pty  Ltd   41  
    • >  Adjus.ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10  November  2010   ©  Datalicious  Pty  Ltd   42  
    • >  Ad  server  exposure  test   Banner   TV/Print   Search   Impression   Response   Response   $   Banner   Search   Direct   Impression   Response   Response   $   Users  are   segmented   before  1st   ad  is  even   Exposed  group:  90%  of  users  get  branded  message   served     Control  group:  10%  of  users  get  non-­‐branded  message   Banner   Search   Direct   Impression   Response   Response   $  November  2010   ©  Datalicious  Pty  Ltd   43  
    • >  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update  November  2010   ©  Datalicious  Pty  Ltd   44  
    • >  Cross-­‐channel  impact  November  2010   ©  Datalicious  Pty  Ltd   45  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   46  
    • >  Tracking  offline  conversions    §  Email  click-­‐through  aoer  purchase  §  First  online  login  aoer  purchase  §  Unique  website  or  visitor  phone  number  §  Call  back  request  or  online  chat  §  Unique  website  promo-on  code  §  Unique  printable  vouchers  §  Store  locator  searches  §  Make  an  appointment  online  November  2010   ©  Datalicious  Pty  Ltd   47  
    • >  Success  aKribu.on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AKrib.   Exclusion   33%   33%   33%   0%   AKrib.   PaKern   30%   20%   20%   30%   AKrib.  November  2010   ©  Datalicious  Pty  Ltd   48  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   49  
    • >  Paths  across  business  units   Introducer   Influencer   Influencer   Closer   $   Brand  1   Brand  2   Brand  3   Brand  4   $   Brand  1   Brand  2   Brand  3   Brand  4   $   Brand  1   Brand  2   Brand  3   Brand  4   $  November  2010   ©  Datalicious  Pty  Ltd   50  
    • Exercise:  AKribu.on  model  November  2010   ©  Datalicious  Pty  Ltd   51  
    • >  Exercise:  AKribu.on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AKrib.   Exclusion   33%   33%   33%   0%   AKrib.   ?   ?   ?   ?   Custom   AKrib.  November  2010   ©  Datalicious  Pty  Ltd   52  
    • >  Common  aKribu.on  models  §  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  November  2010   ©  Datalicious  Pty  Ltd   53  
    • Exercise:  Sta.s.cal  significance  November  2010   ©  Datalicious  Pty  Ltd   54  
    • 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  November  2010   ©  Datalicious  Pty  Ltd   55   Google  “nss  sample  size  calculator”  
    • 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  November  2010   ©  Datalicious  Pty  Ltd   56   Google  “nss  sample  size  calculator”  
    • >  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   ?   $  November  2010   ©  Datalicious  Pty  Ltd   57  
    • Quick  win:  Addi.onal  metrics   November  2010   ©  Datalicious  Pty  Ltd   58  
    • >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  November  2010   ©  Datalicious  Pty  Ltd   59  
    • Calendar  events  to  add  context  November  2010   ©  Datalicious  Pty  Ltd   60  
    • Quick  win:  Event  calendar  November  2010   ©  Datalicious  Pty  Ltd   61  
    • >  Quick  wins  and  geung  started  §  Central  analy-cs  plaiorm  §  Addi-onal  data  into  ad  server  §  Unique  phone  numbers  §  Search  call  to  ac-on  §  Addi-onal  metrics  §  Event  calendar  November  2010   ©  Datalicious  Pty  Ltd   62  
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Targe.ng  November  2010   ©  Datalicious  Pty  Ltd   63  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   64  
    • >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  November  2010   ©  Datalicious  Pty  Ltd   65  
    • >  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.  November  2010   ©  Datalicious  Pty  Ltd   66  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   67  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   68  
    • >  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   marke-ng,  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  November  2010   ©  Datalicious  Pty  Ltd   69  
    • >  Combining  targe.ng  pla>orms     Off-­‐site   targe-ng   Profile   On-­‐site   targe-ng   targe-ng  November  2010   ©  Datalicious  Pty  Ltd   70  
    • November  2010   ©  Datalicious  Pty  Ltd   71  
    • Take  a  closer   look  at  our   cash  flow   solu.ons  November  2010   ©  Datalicious  Pty  Ltd   72  
    • November  2010   ©  Datalicious  Pty  Ltd   73  
    • +  Add  website  behaviour  to  submiKed  contact  form  data    November  2010   ©  Datalicious  Pty  Ltd   74  
    • Take  a  closer   look  at  our   cash  flow   solu.ons  November  2010   ©  Datalicious  Pty  Ltd   75  
    • Save  .me  and  get  your   business  insurance  November  2010   ©  Datalicious  Pty  Ltd   online.   76  
    • Our  Flexi-­‐Premium  car   insurance  can  help  you  November  2010   ©  Datalicious  Pty  Ltd   save.   77  
    • Save  with  our  combine   Our  Flexi-­‐Premium  car   car  and  life  an  help  you   insurance  c insurance  November  2010   ©  Datalicious  Pty  Ltd   offer.   save.   78  
    • November  2010   ©  Datalicious  Pty  Ltd   79  
    • November  2010   ©  Datalicious  Pty  Ltd   80  
    • It’s  no  accident    November  2010   ©  Datalicious  Pty  Ltd   81   we’re  cheaper  
    • >  Combining  technology     On-­‐site     Off-­‐site   segments   segments   CRM  November  2010   ©  Datalicious  Pty  Ltd   82  
    • >  Extended  targe.ng  pla>orm     Publishers   Partners   Network   Brand  November  2010   ©  Datalicious  Pty  Ltd   83  
    • >  SuperTag  code  architecture     §  Central  JavaScript  container  tag   §  One  tag  for  all  sites  and  plaiorms   §  Hosted  internally  or  externally   §  Faster  tag  implementa-on/updates   §  Eliminates  JavaScript  caching   §  Enables  code  tes-ng  on  live  site   §  Enables  heat  map  implementa-on   §  Enables  redirects  for  A/B  tes-ng   §  Enables  network  wide  re-­‐targe-ng   §  Enables  live  chat  implementa-on  November  2010   ©  Datalicious  Pty  Ltd   84  
    • >  Combining  data  sets     Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  November  2010   ©  Datalicious  Pty  Ltd   85  
    • >  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  Con.nuously   Updated  Occasionally  November  2010   ©  Datalicious  Pty  Ltd   86  
    • >  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.    November  2010   ©  Datalicious  Pty  Ltd   87   Source:  White  Paper,  RedEye,  2007  
    • Datalicious  SuperCookie   Persistent  Flash  cookie  that  cannot  be  deleted  November  2010   ©  Datalicious  Pty  Ltd   88  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   89  
    • Quick  win:  Iden.fying  users  November  2010   ©  Datalicious  Pty  Ltd   90  
    • >  Maximise  iden.fica.on  points   Mobile   Home   Work   Online   Phone   Branch  November  2010   ©  Datalicious  Pty  Ltd   91  
    • >  Sample  customer  level  data    November  2010   ©  Datalicious  Pty  Ltd   92  
    • >  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,  ajracted  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?  November  2010   ©  Datalicious  Pty  Ltd   93  
    • 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)  November  2010   ©  Datalicious  Pty  Ltd   94  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   95  
    • >  Poten.al  home  page  layout     Customise  content   Branded  header   delivery  on  the  fly   based  on  referrer   data,  past  content   Rule  based  offer   Login   consump-on  or   profile  data  for   exis-ng  customers.   Targeted   Targeted   offer   offer   Popular     links,     FAQs  November  2010   ©  Datalicious  Pty  Ltd   96  
    • >  Prospect  targe.ng  parameters    November  2010   ©  Datalicious  Pty  Ltd   97  
    • >  Affinity  re-­‐targe.ng  in  ac.on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe-ng,     response  rates  are     lioed  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  hKp://bit.ly/de70b7   12  Month  Caps   - + - +November  2010   ©  Datalicious  Pty  Ltd   98  
    • >  Ad-­‐sequencing  in  ac.on   Marke-ng  is  about   telling  stories  and   stories  are  not  sta-c   but  evolve  over  -me   Ad-­‐sequencing  can  help  to   evolve  stories  over  -me  the     more  users  engage  with  ads  November  2010   ©  Datalicious  Pty  Ltd   99  
    • Quick  win:  Basic  re-­‐targe.ng   November  2010   ©  Datalicious  Pty  Ltd   100  
    • >  Poten.al  newsleKer  layout     Using  profile  data   Rule  based  branded  header   enhanced  with   website  behaviour   Data  verifica.on   NPS   data  imported  into   the  email  delivery   plaiorm  to  build   Rule  based  offer   business  rules  and   Closest     stores,     customise  content   Profile  based  offer   delivery.   offers     etc  November  2010   ©  Datalicious  Pty  Ltd   101  
    • >  Customer  profiling  in  ac.on     Using  website  and  email  responses   to  learn  a  lijle  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  November  2010   ©  Datalicious  Pty  Ltd   102  
    • >  Poten.al  landing  page  layout     Passing  data  on  user   Rule  based  branded  header   preferences  through   to  the  website  via   parameters  in  email   Campaign  message  match   click-­‐through  URLs     to  customise   content  delivery.   Targeted  offer   Call  to  ac.on  November  2010   ©  Datalicious  Pty  Ltd   103  
    • November  2010   ©  Datalicious  Pty  Ltd   104  
    • >  Poten.al  call  center  interface   Customers  can  also   Call  center  menu  op.ons   be  iden-fied  offline   and  given  most  call   center  plaiorms  are   Customer  contact  history   now  web-­‐based  it   would  be  possible  to   use  online  targe-ng   Targeted  offer   Call  script   plaiorms  to  shape   the  call  experience.  November  2010   ©  Datalicious  Pty  Ltd   105  
    • Exercise:  Targe.ng  matrix  November  2010   ©  Datalicious  Pty  Ltd   106  
    • Segment  A   Segment  B   Purchase     Media   Data     cycle   channels   points   Default,   awareness   Research,  considera.on   Purchase   intent   Reten.on,  up/Cross-­‐Sell   November  2010   ©  Datalicious  Pty  Ltd   107  
    • Segment  A   Segment  B   Purchase     Media   Data     cycle   channels   points   Colour,  price,     product  affinity,  etc   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   product  views   Purchase   A  delivers   B  delivers   Website,   Cart  adds,   intent   great  value!   great  value!   emails,  etc   checkouts,  etc   Reten.on,   Why  not   Why  not   Direct  mails,   Email  clicks,  up/Cross-­‐Sell   November  2010   buy  B?   buy  A?   ©  Datalicious  Pty  Ltd   emails,  etc   logins,  108   etc  
    • >  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.”  November  2010   ©  Datalicious  Pty  Ltd   109  
    • >  ClickTale  tes.ng  case  study    November  2010   ©  Datalicious  Pty  Ltd   110  
    • >  Bad  campaign  worse  than  none    November  2010   ©  Datalicious  Pty  Ltd   111  
    • >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac.on   Sa.sfac.on   Social  media  November  2010   ©  Datalicious  Pty  Ltd   112  
    • >  Simplified  AIDA  funnel   Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)  November  2010   ©  Datalicious  Pty  Ltd   113  
    • >  Standardised  global  metrics   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  November  2010   ©  Datalicious  Pty  Ltd   114  
    • Quick  win:  Global  metrics  November  2010   ©  Datalicious  Pty  Ltd   115  
    • >  Keys  to  effec.ve  targe.ng    1.  Define  success  metrics  2.  Define  and  validate  segments  3.  Develop  targe-ng  and  message  matrix    4.  Transform  matrix  into  business  rules  5.  Develop  and  test  content  6.  Start  targe-ng  and  automate  7.  Keep  tes-ng  and  refining  8.  Communicate  results  November  2010   ©  Datalicious  Pty  Ltd   116  
    • >  Quick  wins  and  geung  started  §  Iden-fica-on  of  individual  users  §  Simple  home  page  re-­‐targe-ng    §  Simple  ad  server  re-­‐targe-ng  §  Global  targe-ng  matrix  §  Standardised  metrics  November  2010   ©  Datalicious  Pty  Ltd   117  
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Resources  November  2010   ©  Datalicious  Pty  Ltd   118  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   119  
    • >  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  November  2010   ©  Datalicious  Pty  Ltd   120  
    • >  Forrester  on  web  analy.cs  November  2010   ©  Datalicious  Pty  Ltd   121  
    • >  Forrester  on  tes.ng/targe.ng  November  2010   ©  Datalicious  Pty  Ltd   122  
    • >  Forrester  on  media  aKribu.on  November  2010   ©  Datalicious  Pty  Ltd   123  
    • >  Es.mate  resource  costs  November  2010   ©  Datalicious  Pty  Ltd   124  
    • Exercise:  Return  on  investment   November  2010   ©  Datalicious  Pty  Ltd   125  
    • Contact  us   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twiKer.com/datalicious    November  2010   ©  Datalicious  Pty  Ltd   126  
    • Data  >  Insights  >  Ac.on