Data	  driven	  marke-ng	   Increasing	  campaign	  response	  rates	      through	  data	  driven	  targe3ng	  
Datalicious	  company	  history	  •      Datalicious	  was	  founded	  in	  late	  2007	  •      Strong	  Omniture	  web	 ...
Data	  driven	  marke-ng	                              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	  reten...
Coordina-on	  across	  channels	  	  	                        Genera-ng	                Crea-ng	                          ...
Combining	  targe-ng	  plaXorms	                                        Off-­‐site	                                       t...
October	  2010	     ©	  Datalicious	  Pty	  Ltd	     8	  
October	  2010	     ©	  Datalicious	  Pty	  Ltd	     9	  
Combining	  technology	                            On-­‐site	  	                                           Off-­‐site	     ...
Combining	  data	  sets	                Website	  behavioural	  data	                 Campaign	  response	  data	         ...
Behaviours	  plus	  transac-ons	                Site	  Behaviour	                                                         ...
Facebook	  as	  subscrip-on	  op-on	      Facebook	  Connect	  gives	  your	      company	  the	  following	  data	      a...
Appending	  social	  data	  to	  customer	  profiles	             Name,	  age,	  gender,	  occupa-on,	  loca-on,	  social	 ...
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%	                            ...
Sample	  site	  visitor	  composi-on	        30%	  new	  visitors	  with	  no	                    30%	  repeat	  visitors	...
Developing	  a	  targe-ng	  matrix	                    Phase	       Segment	  A/B	                        Channels	     Da...
Developing	  a	  targe-ng	  matrix	                    Phase	       Segment	  A/B	                           Channels	    ...
Poten-al	  home	  page	  layout	                                                                                          ...
Prospect	  targe-ng	  parameters	  October	  2010	        ©	  Datalicious	  Pty	  Ltd	     21	  
Affinity	  targe-ng	  in	  ac-on	                                                                                           ...
Poten-al	  newsle8er	  layout	                                                                                            ...
Customer	  profiling	  in	  ac-on	                                   Using	  website	  and	  email	  responses	            ...
Poten-al	  landing	  page	  layout	                                                                                       ...
Quality	  content	  is	  key	                                         Avinash	  Kaushik:	  	                 “The	  princi...
ClickTale	  tes-ng	  case	  study	  October	  2010	                ©	  Datalicious	  Pty	  Ltd	     27	  
Keys	  to	  effec-ve	  targe-ng	   1.        Define	  success	  metrics	   2.        Define	  and	  validate	  segments	   3....
ADMA	  short	  course	                        “Analyse	  to	  op-mise”	  	                           In	  Melbourne	  &	  ...
Email	  me	              	                                   	                          ...
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Data Driven Marketing - Aprimo Omniture Webex


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

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Data Driven Marketing - Aprimo Omniture Webex

  1. 1. Data  driven  marke-ng   Increasing  campaign  response  rates   through  data  driven  targe3ng  
  2. 2. Datalicious  company  history  •  Datalicious  was  founded  in  late  2007  •  Strong  Omniture  web  analy3cs  history  •  1  of  4  Omniture  Service  Partners  globally  •  Now  360  data  agency  with  specialist  team  •  Combina3on  of  analysts  and  developers  •  Making  data  accessible  and  ac3onable  •  Evangelizing  smart  data  driven  marke3ng  •  Driving  industry  best  prac3ce  (ADMA)  October  2010   ©  Datalicious  Pty  Ltd   2  
  3. 3. Data  driven  marke-ng   Media  a8ribu-on   Op-mising  channel  mix   Targe-ng     Increasing  relevance   Tes-ng   Improving  usability   $$$  October  2010   ©  Datalicious  Pty  Ltd   3  
  4. 4. 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://  October  2010   ©  Datalicious  Pty  Ltd   4  
  5. 5. 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  October  2010   ©  Datalicious  Pty  Ltd   5  
  6. 6. 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  October  2010   ©  Datalicious  Pty  Ltd   6  
  7. 7. Combining  targe-ng  plaXorms   Off-­‐site   targe3ng   Profile   On-­‐site   targe3ng   targe3ng  October  2010   ©  Datalicious  Pty  Ltd   7  
  8. 8. October  2010   ©  Datalicious  Pty  Ltd   8  
  9. 9. October  2010   ©  Datalicious  Pty  Ltd   9  
  10. 10. Combining  technology   On-­‐site     Off-­‐site   segments   segments  October  2010   ©  Datalicious  Pty  Ltd   10  
  11. 11. Combining  data  sets   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  October  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  Con-nuously   Updated  Occasionally  October  2010   ©  Datalicious  Pty  Ltd   12  
  13. 13. Facebook  as  subscrip-on  op-on   Facebook  Connect  gives  your   company  the  following  data   and  more  with  just  one  click!     Email  address,  first  name,  last  name,   middle  name,  picture,  affilia3ons,  last   profile  update,  3me  zone,  religion,   poli3cal  interests,  interests,  sex,   birthday,  aracted  to  which  sex,  why   they  want  to  meet  someone,  home   town,  rela3onship  status,  current   loca3on,  ac3vi3es,  music  interests,  tv   show  interests,  educa3on  history,  work   history,  family  and  ID  October  2010   ©  Datalicious  Pty  Ltd   13  
  14. 14. 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)  October  2010   ©  Datalicious  Pty  Ltd   14  
  15. 15. 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://      October  2010   ©  Datalicious  Pty  Ltd   15  
  16. 16. 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  October  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  October  2010   ©  Datalicious  Pty  Ltd   17  
  18. 18. Developing  a  targe-ng  matrix   Phase   Segment  A/B   Channels   Data  Points   Awareness   Considera-on   Purchase  Intent   Up/Cross-­‐Sell  October  2010   ©  Datalicious  Pty  Ltd   18  
  19. 19. Developing  a  targe-ng  matrix   Phase   Segment  A/B   Channels   Data  Points   Social,  display,   Awareness   Seen  this?   Default   search,  etc   Social,  search,   Download,   Considera-on   Great  feature!   website,  etc   product  view   Search,  site,   Cart  add,   Purchase  Intent   Great  value!   emails,  etc   checkout,  etc   Direct  mail,   Email  response,   Up/Cross-­‐Sell   Add  this!   emails,  etc   login,  etc  October  2010   ©  Datalicious  Pty  Ltd   19  
  20. 20. Poten-al  home  page  layout   Customise  content   Branded  header   delivery  on  the  fly   based  on  referrer   data,  past  content   Rule  based  offer   Login   consump3on  or   profile  data  for   exis3ng  customers.   Targeted   Targeted   offer   offer   Popular     links,     FAQs  October  2010   ©  Datalicious  Pty  Ltd   20  
  21. 21. Prospect  targe-ng  parameters  October  2010   ©  Datalicious  Pty  Ltd   21  
  22. 22. Affinity  targe-ng  in  ac-on   Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe3ng,     response  rates  are     liged  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  h8p://   12  Month  Caps   - + - +October  2010   ©  Datalicious  Pty  Ltd   22  
  23. 23. Poten-al  newsle8er  layout   Using  profile  data   Rule  based  branded  header   enhanced  with   website  behaviour   Data  verifica-on   NPS   data  imported  into   the  email  delivery   plahorm  to  build   Rule  based  offer   business  rules  and   Closest     stores,     customise  content   Profile  based  offer   delivery.   offers     etc  October  2010   ©  Datalicious  Pty  Ltd   23  
  24. 24. Customer  profiling  in  ac-on   Using  website  and  email  responses   to  learn  a  lile  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  October  2010   ©  Datalicious  Pty  Ltd   24  
  25. 25. 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  October  2010   ©  Datalicious  Pty  Ltd   25  
  26. 26. 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.”  October  2010   ©  Datalicious  Pty  Ltd   26  
  27. 27. ClickTale  tes-ng  case  study  October  2010   ©  Datalicious  Pty  Ltd   27  
  28. 28. 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  October  2010   ©  Datalicious  Pty  Ltd   28  
  29. 29. ADMA  short  course   “Analyse  to  op-mise”     In  Melbourne  &  Sydney   October/November   By  Datalicious  October  2010   ©  Datalicious  Pty  Ltd   29  
  30. 30. Email  me     Follow  us     Learn  more    October  2010   ©  Datalicious  Pty  Ltd   30  
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