Data Driven Marketing

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

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Data Driven Marketing

  1. 1. Data  driven  marke-ng   Increasing  campaign  response  rates   through  data  driven  targe3ng  
  2. 2. Datalicious  company  history  •  Datalicious  was  founded  in  2007  •  Strong  Omniture  web  analy3cs  history,  now  •  One-­‐stop  data  agency  with  specialist  team  •  Combina3on  of  analysts  and  developers  •  Making  data  accessible  and  ac3onable  •  Driving  industry  best  prac3ce  •  Evangelizing  use  of  data  August  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   $$$  August  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://bit.ly/cAtSUN  August  2010   ©  Datalicious  Pty  Ltd   4   Source:  McKinsey  Quarterly,  2010  
  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  August  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  August  2010   ©  Datalicious  Pty  Ltd   6  
  7. 7. Combining  targe-ng  plaXorms   Off-­‐site   targe3ng   Profile   On-­‐site   targe3ng   targe3ng  August  2010   ©  Datalicious  Pty  Ltd   7  
  8. 8. Combining  technology  plaXorms   On-­‐site     Off-­‐site   segments   segments   On  and  off-­‐site  targe-ng  plaXorms  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   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  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. 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  August  2010   ©  Datalicious  Pty  Ltd   13  
  14. 14. Flowtown  social  profiling   Name,  age,  gender,  occupa-on,  loca-on,  social     profiles  and  influencer  ranking  based  on  email   (influencers  only)   (all  contacts)  August  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://bit.ly/cszp2o       Source:  White  Paper,  RedEye,  2007  
  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  
  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. Affinity  targe-ng  in  ac-on   Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe3ng,     response  rates  are     liied  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://bit.ly/de70b7   12  Month  Caps   - + - +June  2010   ©  Datalicious  Pty  Ltd   20  
  21. 21. Poten-al  newsle8er  layout   Using  data  on   Rule  based  header  theme   website  behaviour   imported  into  the   Data  verifica-on   NPS   email  delivery   plajorm  to  build   business  rules  to   Rule  based  offer   customise  content   Closest     stores,     delivery.   Profile  based  offer   offers     etc  August  2010   ©  Datalicious  Pty  Ltd   21  
  22. 22. Poten-al  landing  page  layout   Passing  data  on  user   Branded  header   preferences  through   to  the  website  via   Email  or  campaign  message  match   parameters  in  email   click-­‐through  URLs     to  customise   content  delivery.   Targeted  offers   Call  to  ac-on  August  2010   ©  Datalicious  Pty  Ltd   22  
  23. 23. 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.”  
  24. 24. Tes-ng  case  study   Google:  “change  one  word  double  conversion”     or  h8p://bit.ly/bpyqFp  August  2010   ©  Datalicious  Pty  Ltd   24  
  25. 25. 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   25  
  26. 26. ADMA  short  course   “Analyse  to  op-mise”     In  Melbourne  &  Sydney   October/November   By  Datalicious  August  2010   ©  Datalicious  Pty  Ltd   26  
  27. 27. Email  me   cbartens@datalicious.com     Follow  us   twi8er.com/datalicious     Learn  more   blog.datalicious.com    August  2010   ©  Datalicious  Pty  Ltd   27  

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