[	  Data	  driven	  marke.ng	  ]	       Reducing	  waste	  and	  increasing	        relevance	  through	  targe3ng	  
[	  Using	  data	  to	  reduce	  waste	  ]	                       Media	  a8ribu.on                           	           ...
[	  Increase	  revenue	  by	  10-­‐20%	  ]	          By	  coordina.ng	  the	  consumer’s	  end-­‐to-­‐end	  experience,	  ...
[	  The	  consumer	  data	  journey	  ]	     To	  transac.onal	  data	                                               To	  ...
[	  Coordina.on	  across	  channels	  ]	  	  	                       Genera.ng	               Crea.ng	                    ...
[	  Combining	  targe.ng	  plaZorms	  ]	                                       Off-­‐site	                                 ...
[	  Targe.ng	  plaZorms	  ]	  §  Off-­‐site	  targe3ng	            –  Ad	  networks:	  Google,	  Yahoo,	  ValueClick,	  et...
[	  Combining	  technology	  plaZorms	  ]	                                   On-­‐site	  	                                ...
August	  2010	     ©	  Datalicious	  Pty	  Ltd	     9	  
August	  2010	     ©	  Datalicious	  Pty	  Ltd	     10	  
[	  Combining	  data	  sets	  ]	                       Web	  analy.cs	  data	                         Customer	  data	    ...
[	  Behaviours	  plus	  transac.ons	  ]	             Site	  Behaviour	                                                    ...
[	  Overes.ma.ng	  unique	  visitors	  ]	  The	  study	  examined	  data	  	  from	  two	  of	  the	  UK’s	  busiest	  	  ...
[	  Maximise	  iden.fica.on	  points	  ]	  160%	  140%	  120%	  100%	   80%	   60%	                                        ...
Datalicious	  SuperCookie	            Persistent	  Flash	  cookie	  that	  cannot	  be	  deleted	  August	  2010	         ...
August	  2010	     ©	  Datalicious	  Pty	  Ltd	     16	  
[	  Sample	  site	  visitor	  composi.on	  ]	     30%	  new	  visitors	  with	  no	                    30%	  repeat	  visi...
[	  Developing	  a	  targe.ng	  matrix	  ]	         Phase	            Segment	  A	     Segment	  B	     Channels	      Awa...
[	  Developing	  a	  targe.ng	  matrix	  ]	         Phase	              Segment	  A	        Segment	  B	         Channels	...
[	  Quality	  content	  is	  key	  ]	  Avinash	  Kaushik:	  “The	  principle	  of	  garbage	  in,	  garbage	  out	  applie...
[	  Keys	  to	  effec.ve	  targe.ng	  ]	   1.        Define	  success	  metrics	   2.        Define	  and	  validate	  segmen...
[	  ClickTale	  tes.ng	  case	  study	  ]	                       Google:	  “change	  one	  word	  double	  conversion”	  	...
ADMA	  short	  course	                       “Analyse	  to	  op.mise”	  	                          In	  Melbourne	  &	  Sy...
Email	  me	                       cbartens@datalicious.com	                                  	                            ...
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Data Driven Targeting - Behavioural Targeting

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The presentation discusses the significance of data in marketing through targeting.

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Data Driven Targeting - Behavioural Targeting

  1. 1. [  Data  driven  marke.ng  ]   Reducing  waste  and  increasing   relevance  through  targe3ng  
  2. 2. [  Using  data  to  reduce  waste  ]   Media  a8ribu.on   Op.mising  channel  mix   Targe.ng     Increasing  relevance   Tes.ng   Improving  usability   $$$  August  2010   ©  Datalicious  Pty  Ltd   2  
  3. 3. [  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  h8p://bit.ly/cAtSUN  August  2010   ©  Datalicious  Pty  Ltd   3   Source:  McKinsey  Quarterly,  2010  
  4. 4. [  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   4  
  5. 5. [  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   marke3ng,  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   5  
  6. 6. [  Combining  targe.ng  plaZorms  ]   Off-­‐site   targe3ng   Profile   On-­‐site   targe3ng   targe3ng  August  2010   ©  Datalicious  Pty  Ltd   6  
  7. 7. [  Targe.ng  plaZorms  ]  §  Off-­‐site  targe3ng   –  Ad  networks:  Google,  Yahoo,  ValueClick,  etc   –  Ad  servers:  DoubleClick,  Eyeblaster,  Atlas,  etc  §  On-­‐site  targe3ng   –  Paid:  Omniture  Test&Target  (Offerma3ca,  TouchClarity),   Memetrics  (Accenture),  Op3most  (Autonomy),  Ke[a   (Acxiom),  AudienceScience,  Maxymiser,  Amadesa,  etc   –  Free:  BTBuckets,  Google  Analy3cs,  etc  §  Profile  targe3ng   –  Email  pla^orms:  Inxmail,  Trac3on,  Returnity,  etc   –  Marke3ng  automa3on:  Aprimo,  Unica,  Eloqua,  etc  August  2010   ©  Datalicious  Pty  Ltd   7  
  8. 8. [  Combining  technology  plaZorms  ]   On-­‐site     Off-­‐site   segments   segments   On  and  off-­‐site  targe.ng  plaZorms  should  use     iden.cal  triggers  to  sort  visitors  into  segments  August  2010   ©  Datalicious  Pty  Ltd   8  
  9. 9. August  2010   ©  Datalicious  Pty  Ltd   9  
  10. 10. August  2010   ©  Datalicious  Pty  Ltd   10  
  11. 11. [  Combining  data  sets  ]   Web  analy.cs  data   Customer  data   +   The  whole  is  greater     than  the  sum  of  its  parts   3rd  party  data  August  2010   ©  Datalicious  Pty  Ltd   11  
  12. 12. [  Behaviours  plus  transac.ons  ]   Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collec3on  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   predic3ve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promo3on  responses   historical  data  from  previous  transac3ons   emails,  internal  search,  etc   average  order  value,  points,  etc   UPDATED  CONTINUOUSLY   UPDATED  OCCASIONALLY  August  2010   ©  Datalicious  Pty  Ltd   12  
  13. 13. [  Overes.ma.ng  unique  visitors  ]  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  overes3mated  visitors  by  up  to  7.6  3mes  whilst  a  cookie-­‐based  approach  overes.mated  visitors  by  up  to  2.3  .mes.    Google:  ”red  eye  cookie  report  pdf”  or  h8p://bit.ly/cszp2o       Source:  White  Paper,  RedEye,  2007  
  14. 14. [  Maximise  iden.fica.on  points  ]  160%  140%  120%  100%   80%   60%   −−−  Probability  of  iden3fica3on  through  Cookies   40%   20%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks  
  15. 15. Datalicious  SuperCookie   Persistent  Flash  cookie  that  cannot  be  deleted  August  2010   ©  Datalicious  Pty  Ltd   15  
  16. 16. August  2010   ©  Datalicious  Pty  Ltd   16  
  17. 17. [  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  transac3onal  history  of   prospects   which  maybe  50%  can  actually  be   with  limited   iden3fied  as  individuals     profile  data  August  2010   ©  Datalicious  Pty  Ltd   17  
  18. 18. [  Developing  a  targe.ng  matrix  ]   Phase   Segment  A   Segment  B   Channels   Awareness   Considera.on   Purchase  Intent   Up/Cross-­‐Sell  
  19. 19. [  Developing  a  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  
  20. 20. [  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.”  
  21. 21. [  Keys  to  effec.ve  targe.ng  ]   1.  Define  success  metrics   2.  Define  and  validate  segments   3.  Develop  targe3ng  and  message  matrix     4.  Transform  matrix  into  business  rules   5.  Develop  and  test  content   6.  Start  targe3ng  and  automate   7.  Keep  tes3ng  and  refining   8.  Communicate  results  August  2010   ©  Datalicious  Pty  Ltd   21  
  22. 22. [  ClickTale  tes.ng  case  study  ]   Google:  “change  one  word  double  conversion”     or  h8p://bit.ly/bpyqFp  August  2010   ©  Datalicious  Pty  Ltd   22  
  23. 23. ADMA  short  course   “Analyse  to  op.mise”     In  Melbourne  &  Sydney   October/November   By  Datalicious  August  2010   ©  Datalicious  Pty  Ltd   23  
  24. 24. Email  me   cbartens@datalicious.com     Talk  to  us   ADMA  Forum  Stand  347     Learn  more   www.datalicious.com    August  2010   ©  Datalicious  Pty  Ltd   24  
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