>	  Suncorp	  Analy.cs	  <	     Smart	  data	  driven	  marke-ng	  
>	  Short	  but	  sharp	  history	  §  Datalicious	  was	  founded	  late	  2007	  §  Strong	  Omniture	  web	  analy-cs...
>	  Clients	  across	  all	  industries	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     3	  
>	  Wide	  range	  of	  data	  services	       Data	                                         Insights	                    ...
>	  Smart	  data	  driven	  marke.ng	                         Media	  AKribu.on                           	               ...
15 tools are proposed largely centred on Adobe                Once implemented these tools will deliver the capability for...
10101101001001001010111101001001010101010000101111100101010101010010101100110001010010100110110100110100101010011100101001...
>	  Campaign	  flow	  and	  calls	  to	  ac.on	  	          =	  Paid	  media	                                              ...
Exercise:	  Campaign	  flow	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     9	  
>	  Duplica.on	  across	  channels	  	                          Paid	  	                  Bid	  	                         ...
>	  Cookie	  expira.on	  impact	                                  Paid	  	                                            Bid	...
>	  De-­‐duplica.on	  across	  channels	  	                          Paid	  	                         Search	             ...
Exercise:	  Duplica.on	  impact	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     13	  
>	  Exercise:	  Duplica.on	  impact	  	  §  Double-­‐coun-ng	  of	  conversions	  across	  channels	  can	      have	  a	...
Quick	  win:	  Central	  pla>orm	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     15	  
>	  Reach	  and	  channel	  overlap	  	                                      TV/Print	  	                                 ...
>	  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	  	                                                   Or...
>	  First	  and	  last	  click	  aKribu.on	  	                                                                            ...
>	  Adobe	  stacking/par.cipa.on	                                    Adobe	  can	  only	  stack	                          ...
>	  Full	  path	  to	  purchase	       Introducer	       Influencer	           Influencer	                     Closer	      ...
>	  Where	  to	  collect	  the	  data	  	                   Ad	  Server	                                                  ...
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	  §  1	  unique	  phone	  number	  	          –  Phone	  number	  is	  considered	  part	  o...
>	  Unique	  phone	  numbers	  §  10+	  unique	  phone	  numbers	          –  Different	  numbers	  for	  different	  media...
>	  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	...
>	  Media	  aKribu.on	  phases	  	  §  Phase	  1:	  De-­‐duplica-on	          –  Conversion	  de-­‐duplica-on	  across	  ...
>	  Combining	  data	  sources	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     38	  
>	  Single	  source	  of	  truth	  repor.ng	   Insights	                                                 Repor.ng   	  Nov...
>	  Understanding	  channel	  mix	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     40	  
>	  Website	  entry	  survey	  	   De-­‐duped	  Campaign	  Report	                                                      Gr...
>	  Adjus.ng	  for	  offline	  impact	                         -­‐5	                               -­‐15	     -­‐10	        ...
>	  Ad	  server	  exposure	  test	                                 Banner	               TV/Print	                      Se...
>	  Full	  path	  to	  purchase	       Introducer	       Influencer	           Influencer	                     Closer	      ...
>	  Cross-­‐channel	  impact	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     45	  
>	  Offline	  sales	  driven	  by	  online	       Adver.sing	  	     Phone	                                                 ...
>	  Tracking	  offline	  conversions	  	  §  Email	  click-­‐through	  aoer	  purchase	  §  First	  online	  login	  aoer	...
>	  Success	  aKribu.on	  models	  	       Introducer	      Influencer	           Influencer	                    Closer	    ...
>	  Path	  across	  different	  segments	       Introducer	       Influencer	              Influencer	                      C...
>	  Paths	  across	  business	  units	       Introducer	      Influencer	             Influencer	                     Closer...
Exercise:	  AKribu.on	  model	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     51	  
>	  Exercise:	  AKribu.on	  models	  	       Introducer	      Influencer	           Influencer	                    Closer	  ...
>	  Common	  aKribu.on	  models	  §  Allocate	  more	  conversion	  credits	  to	  more	      recent	  touch	  points	  f...
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	  survey	  responses	  do	  you	  need	  	                               if	  you	  have	  10,000	  customers?	...
>	  Addi.onal	  success	  metrics	  	          Click	        Through	                                                     ...
Quick	  win:	  Addi.onal	  metrics	   November	  2010	     ©	  Datalicious	  Pty	  Ltd	     58	  
>	  Importance	  of	  calendar	  events	  	     Traffic	  spikes	  or	  other	  data	  anomalies	  without	  context	  are	 ...
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	  ...
10101101001001001010111101001001010101010000101111100101010101010010101100110001010010100110110100110100101010011100101001...
>	  Increase	  revenue	  by	  10-­‐20%	  	     Capture	  internet	  traffic	     Capture	  50-­‐100%	  of	  fair	  market	  ...
>	  New	  consumer	  decision	  journey	   The	  consumer	  decision	  process	  is	  changing	  from	  linear	  to	  circ...
>	  New	  consumer	  decision	  journey	   The	  consumer	  decision	  process	  is	  changing	  from	  linear	  to	  circ...
>	  The	  consumer	  data	  journey	  	    To	  transac.onal	  data	                                                To	  r...
>	  The	  consumer	  data	  journey	  	    To	  transac.onal	  data	                                                To	  r...
>	  Coordina.on	  across	  channels	  	  	  	                  Genera.ng	                   Crea.ng	                      ...
>	  Combining	  targe.ng	  pla>orms	  	                                         Off-­‐site	                                ...
November	  2010	     ©	  Datalicious	  Pty	  Ltd	     71	  
Take	  a	  closer	                                                              look	  at	  our	                          ...
November	  2010	     ©	  Datalicious	  Pty	  Ltd	     73	  
+	  Add	  website	  behaviour	  to	  submiKed	  contact	  form	  data	  	  November	  2010	                               ...
Take	  a	  closer	                                                              look	  at	  our	                          ...
Save	  .me	  and	  get	  your	                                                              business	  insurance	  Novembe...
Our	  Flexi-­‐Premium	  car	                                                              insurance	  can	  help	  you	  N...
Save	  with	  our	  combine	                                                              Our	  Flexi-­‐Premium	  car	    ...
November	  2010	     ©	  Datalicious	  Pty	  Ltd	                                                              79	  
November	  2010	     ©	  Datalicious	  Pty	  Ltd	     80	  
It’s	  no	  accident	  	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	                         81	                   ...
>	  Combining	  technology	  	                          On-­‐site	  	                                           Off-­‐site	...
>	  Extended	  targe.ng	  pla>orm	  	                           Publishers	                              Partners	        ...
>	  SuperTag	  code	  architecture	  	                                           §  Central	  JavaScript	  container	  ta...
>	  Combining	  data	  sets	  	           Website	  behavioural	  data	             Campaign	  response	  data	           ...
>	  Behaviours	  plus	  transac.ons	  	           Site	  Behaviour	                                                       ...
>	  Unique	  visitor	  overes.ma.on	  	  The	  study	  examined	  	  data	  from	  two	  of	  	  the	  UK’s	  busiest	  	 ...
Datalicious	  SuperCookie	          Persistent	  Flash	  cookie	  that	  cannot	  be	  deleted	  November	  2010	         ...
>	  Maximise	  iden.fica.on	  points	  	  160%	  140%	  120%	  100%	    80%	    60%	                                       ...
Quick	  win:	  Iden.fying	  users	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     90	  
>	  Maximise	  iden.fica.on	  points	                 Mobile	              Home	                                 Work	     ...
>	  Sample	  customer	  level	  data	  	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     92	  
>	  Facebook	  Connect	  single	  sign	  on	  	   Facebook	  Connect	  gives	  your	   company	  the	  following	  data	  ...
Appending	  social	  data	  to	  customer	  profiles	         Name,	  age,	  gender,	  occupa.on,	  loca.on,	  social	  	  ...
>	  Sample	  site	  visitor	  composi.on	  	    30%	  new	  visitors	  with	  no	                    30%	  repeat	  visito...
>	  Poten.al	  home	  page	  layout	  	                                                                                   ...
>	  Prospect	  targe.ng	  parameters	  	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     97	  
>	  Affinity	  re-­‐targe.ng	  in	  ac.on	  	                                                                               ...
>	  Ad-­‐sequencing	  in	  ac.on	                                                                                   Marke-...
Quick	  win:	  Basic	  re-­‐targe.ng	   November	  2010	     ©	  Datalicious	  Pty	  Ltd	     100	  
>	  Poten.al	  newsleKer	  layout	  	                                                                                     ...
>	  Customer	  profiling	  in	  ac.on	  	                          Using	  website	  and	  email	  responses	              ...
>	  Poten.al	  landing	  page	  layout	  	                                                                                ...
November	  2010	     ©	  Datalicious	  Pty	  Ltd	     104	  
>	  Poten.al	  call	  center	  interface	                                                                                 ...
Exercise:	  Targe.ng	  matrix	  November	  2010	     ©	  Datalicious	  Pty	  Ltd	     106	  
Segment	  A	       Segment	  B	     Purchase	  	                                                                 Media	   ...
Segment	  A	                Segment	  B	     Purchase	  	                                                                 ...
>	  Quality	  content	  is	  key	  	                                    Avinash	  Kaushik:	  	            “The	  principle...
>	  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...
>	  Simplified	  AIDA	  funnel	              Reach	            Engagement	                                      Conversion	...
>	  Standardised	  global	  metrics	                  Media	  and	  search	  data	                                        ...
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	...
>	  Quick	  wins	  and	  geung	  started	  §  Iden-fica-on	  of	  individual	  users	  §  Simple	  home	  page	  re-­‐tar...
10101101001001001010111101001001010101010000101111100101010101010010101100110001010010100110110100110100101010011100101001...
>	  The	  consumer	  data	  journey	  	    To	  transac.onal	  data	                                                To	  r...
>	  The	  consumer	  data	  journey	  	    To	  transac.onal	  data	                                                To	  r...
>	  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	                                    	                      ...
Data	  >	  Insights	  >	  Ac.on	  
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SunCorp Analytics

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

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SunCorp Analytics

  1. 1. >  Suncorp  Analy.cs  <   Smart  data  driven  marke-ng  
  2. 2. >  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  
  3. 3. >  Clients  across  all  industries  November  2010   ©  Datalicious  Pty  Ltd   3  
  4. 4. >  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  
  5. 5. >  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  
  6. 6. 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
  7. 7. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  aKribu.on  November  2010   ©  Datalicious  Pty  Ltd   7  
  8. 8. >  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  
  9. 9. Exercise:  Campaign  flow  November  2010   ©  Datalicious  Pty  Ltd   9  
  10. 10. >  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  
  11. 11. >  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  
  12. 12. >  De-­‐duplica.on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy.cs   Pla>orm   Email     Blast   $   Organic   Search   $  November  2010   ©  Datalicious  Pty  Ltd   12  
  13. 13. Exercise:  Duplica.on  impact  November  2010   ©  Datalicious  Pty  Ltd   13  
  14. 14. >  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  
  15. 15. Quick  win:  Central  pla>orm  November  2010   ©  Datalicious  Pty  Ltd   15  
  16. 16. >  Reach  and  channel  overlap     TV/Print     audience   Banner   Search   audience   audience  November  2010   ©  Datalicious  Pty  Ltd   16  
  17. 17. >  Indirect  display  impact    November  2010   ©  Datalicious  Pty  Ltd   17  
  18. 18. >  Indirect  display  impact    November  2010   ©  Datalicious  Pty  Ltd   18  
  19. 19. >  Indirect  display  impact    November  2010   ©  Datalicious  Pty  Ltd   19  
  20. 20. >  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  
  21. 21. >  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  
  22. 22. >  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  
  23. 23. >  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  
  24. 24. >  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  
  25. 25. Quick  win:  Data  into  ad  server   November  2010   ©  Datalicious  Pty  Ltd   25  
  26. 26. >  Search  call  to  ac.on  for  offline    November  2010   ©  Datalicious  Pty  Ltd   26  
  27. 27. Offline  response  tracking  and  improved  experience   November  2010   ©  Datalicious  Pty  Ltd   27  
  28. 28. Quick  win:  Search  call  to  ac.on   November  2010   ©  Datalicious  Pty  Ltd   28  
  29. 29. November  2010   ©  Datalicious  Pty  Ltd   29  
  30. 30. hKp://www.suncorp.com.au?campaign=workshop   November  2010   ©  Datalicious  Pty  Ltd   30  
  31. 31. >  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  
  32. 32. >  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  
  33. 33. >  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  
  34. 34. >  Jet  Interac.ve  phone  call  data  November  2010   ©  Datalicious  Pty  Ltd   34  
  35. 35. Quick  win:  Unique  numbers  November  2010   ©  Datalicious  Pty  Ltd   35  
  36. 36. >  PURLs  boos.ng  DM  response  rates   Text  November  2010   ©  Datalicious  Pty  Ltd   36  
  37. 37. >  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  
  38. 38. >  Combining  data  sources  November  2010   ©  Datalicious  Pty  Ltd   38  
  39. 39. >  Single  source  of  truth  repor.ng   Insights   Repor.ng  November  2010   ©  Datalicious  Pty  Ltd   39  
  40. 40. >  Understanding  channel  mix  November  2010   ©  Datalicious  Pty  Ltd   40  
  41. 41. >  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  
  42. 42. >  Adjus.ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10  November  2010   ©  Datalicious  Pty  Ltd   42  
  43. 43. >  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  
  44. 44. >  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  
  45. 45. >  Cross-­‐channel  impact  November  2010   ©  Datalicious  Pty  Ltd   45  
  46. 46. >  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  
  47. 47. >  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  
  48. 48. >  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  
  49. 49. >  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  
  50. 50. >  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  
  51. 51. Exercise:  AKribu.on  model  November  2010   ©  Datalicious  Pty  Ltd   51  
  52. 52. >  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  
  53. 53. >  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  
  54. 54. Exercise:  Sta.s.cal  significance  November  2010   ©  Datalicious  Pty  Ltd   54  
  55. 55. 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”  
  56. 56. 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”  
  57. 57. >  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  
  58. 58. Quick  win:  Addi.onal  metrics   November  2010   ©  Datalicious  Pty  Ltd   58  
  59. 59. >  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  
  60. 60. Calendar  events  to  add  context  November  2010   ©  Datalicious  Pty  Ltd   60  
  61. 61. Quick  win:  Event  calendar  November  2010   ©  Datalicious  Pty  Ltd   61  
  62. 62. >  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  
  63. 63. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Targe.ng  November  2010   ©  Datalicious  Pty  Ltd   63  
  64. 64. >  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  
  65. 65. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  November  2010   ©  Datalicious  Pty  Ltd   65  
  66. 66. >  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  
  67. 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   67  
  68. 68. >  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  
  69. 69. >  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  
  70. 70. >  Combining  targe.ng  pla>orms     Off-­‐site   targe-ng   Profile   On-­‐site   targe-ng   targe-ng  November  2010   ©  Datalicious  Pty  Ltd   70  
  71. 71. November  2010   ©  Datalicious  Pty  Ltd   71  
  72. 72. Take  a  closer   look  at  our   cash  flow   solu.ons  November  2010   ©  Datalicious  Pty  Ltd   72  
  73. 73. November  2010   ©  Datalicious  Pty  Ltd   73  
  74. 74. +  Add  website  behaviour  to  submiKed  contact  form  data    November  2010   ©  Datalicious  Pty  Ltd   74  
  75. 75. Take  a  closer   look  at  our   cash  flow   solu.ons  November  2010   ©  Datalicious  Pty  Ltd   75  
  76. 76. Save  .me  and  get  your   business  insurance  November  2010   ©  Datalicious  Pty  Ltd   online.   76  
  77. 77. Our  Flexi-­‐Premium  car   insurance  can  help  you  November  2010   ©  Datalicious  Pty  Ltd   save.   77  
  78. 78. 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  
  79. 79. November  2010   ©  Datalicious  Pty  Ltd   79  
  80. 80. November  2010   ©  Datalicious  Pty  Ltd   80  
  81. 81. It’s  no  accident    November  2010   ©  Datalicious  Pty  Ltd   81   we’re  cheaper  
  82. 82. >  Combining  technology     On-­‐site     Off-­‐site   segments   segments   CRM  November  2010   ©  Datalicious  Pty  Ltd   82  
  83. 83. >  Extended  targe.ng  pla>orm     Publishers   Partners   Network   Brand  November  2010   ©  Datalicious  Pty  Ltd   83  
  84. 84. >  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  
  85. 85. >  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  
  86. 86. >  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  
  87. 87. >  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  
  88. 88. Datalicious  SuperCookie   Persistent  Flash  cookie  that  cannot  be  deleted  November  2010   ©  Datalicious  Pty  Ltd   88  
  89. 89. >  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  
  90. 90. Quick  win:  Iden.fying  users  November  2010   ©  Datalicious  Pty  Ltd   90  
  91. 91. >  Maximise  iden.fica.on  points   Mobile   Home   Work   Online   Phone   Branch  November  2010   ©  Datalicious  Pty  Ltd   91  
  92. 92. >  Sample  customer  level  data    November  2010   ©  Datalicious  Pty  Ltd   92  
  93. 93. >  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  
  94. 94. 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  
  95. 95. >  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  
  96. 96. >  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  
  97. 97. >  Prospect  targe.ng  parameters    November  2010   ©  Datalicious  Pty  Ltd   97  
  98. 98. >  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  
  99. 99. >  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  
  100. 100. Quick  win:  Basic  re-­‐targe.ng   November  2010   ©  Datalicious  Pty  Ltd   100  
  101. 101. >  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  
  102. 102. >  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  
  103. 103. >  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  
  104. 104. November  2010   ©  Datalicious  Pty  Ltd   104  
  105. 105. >  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  
  106. 106. Exercise:  Targe.ng  matrix  November  2010   ©  Datalicious  Pty  Ltd   106  
  107. 107. 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  
  108. 108. 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  
  109. 109. >  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  
  110. 110. >  ClickTale  tes.ng  case  study    November  2010   ©  Datalicious  Pty  Ltd   110  
  111. 111. >  Bad  campaign  worse  than  none    November  2010   ©  Datalicious  Pty  Ltd   111  
  112. 112. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac.on   Sa.sfac.on   Social  media  November  2010   ©  Datalicious  Pty  Ltd   112  
  113. 113. >  Simplified  AIDA  funnel   Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)  November  2010   ©  Datalicious  Pty  Ltd   113  
  114. 114. >  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  
  115. 115. Quick  win:  Global  metrics  November  2010   ©  Datalicious  Pty  Ltd   115  
  116. 116. >  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  
  117. 117. >  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  
  118. 118. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Resources  November  2010   ©  Datalicious  Pty  Ltd   118  
  119. 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   119  
  120. 120. >  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  
  121. 121. >  Forrester  on  web  analy.cs  November  2010   ©  Datalicious  Pty  Ltd   121  
  122. 122. >  Forrester  on  tes.ng/targe.ng  November  2010   ©  Datalicious  Pty  Ltd   122  
  123. 123. >  Forrester  on  media  aKribu.on  November  2010   ©  Datalicious  Pty  Ltd   123  
  124. 124. >  Es.mate  resource  costs  November  2010   ©  Datalicious  Pty  Ltd   124  
  125. 125. Exercise:  Return  on  investment   November  2010   ©  Datalicious  Pty  Ltd   125  
  126. 126. Contact  us   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twiKer.com/datalicious    November  2010   ©  Datalicious  Pty  Ltd   126  
  127. 127. Data  >  Insights  >  Ac.on  
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