P&O Analytics

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P&O Analytics

  1. 1. >  P&O  Analy+cs  Workshop  <   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  §  Driving  industry  best  prac-ce  (ADMA)  §  Turning  data  into  ac-onable  insights  §  Execu-ng  smart  data  driven  campaigns  June  2011   ©  Datalicious  Pty  Ltd   2  
  3. 3. >  Smart  data  driven  marke+ng   Media  A@ribu+on  &  Modeling   Op+mise  channel  mix,  predict  sales   Targeted  Direct  Marke+ng     Increase  relevance,  reduce  churn   Tes+ng  &  Op+misa+on   Remove  barriers,  drive  sales   Boost  ROMI  June  2011   ©  Datalicious  Pty  Ltd   3  
  4. 4. >  Wide  range  of  data  services   Data   Insights   Ac+on   PlaIorms   Analy+cs   Campaigns         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   Tableau,  SpoIire,  SPSS,  etc   Alterian,  SiteCore,  Inxmail,  etc         Tag-­‐less  online  data  capture   Media  a@ribu+on  models   Targe+ng  and  merchandising         End-­‐to-­‐end  data  plaIorms   Market  and  compe+tor  trends   Internal  search  op+misa+on         IVR  and  call  center  repor+ng   Social  media  monitoring   CRM  strategy  and  execu+on         Single  customer  view   Customer  profiling   Tes+ng  programs    June  2011   ©  Datalicious  Pty  Ltd   4  
  5. 5. >  Clients  across  all  industries  June  2011   ©  Datalicious  Pty  Ltd   5  
  6. 6. >  Today  §  Data  Roadmap  Prerequisites:   1.  How  do  you  want  to  differen-ate  your   promo-on  ac-vity  to  different  segments  of   consumers/web  users/customers?     (What  would  these  segments  be?)       OUTPUT:  Dra[  Targe-ng  Matrix   2.  What  metrics  are  available  at  different  points   in  the  consumer  path  to  purchase?   OUTPUT:  Dra[  Metrics  Framework    June  2011   ©  Datalicious  Pty  Ltd   6  
  7. 7. Clive  Humby:  Data  is  the  new  oil   June  2011   ©  Datalicious  Pty  Ltd   7  
  8. 8. >  Corporate  data  journey     Stage  1   Stage  2     Stage  3 Data   Insights   Ac+on   “Leaders”   Data  is  fully  owned     “Followers”     Sophis-ca-on in-­‐house,  advanced   Data  is  being  brought     predic-ve  modelling   “Laggards”   in-­‐house,  shi[  towards   and  trigger  based   Third  par-es  control   insights  genera-on  and   marke-ng,  i.e.  what     data  mining,  i.e.  why   will  happen  and     most  data,  ad  hoc   did  it  happen?   making  it  happen!   repor-ng  only,  i.e.     what  happened?   Time,  Control  June  2011   ©  Datalicious  Pty  Ltd   8  
  9. 9. Oil  and  data  come  at  a  price  June  2011   ©  Datalicious  Pty  Ltd   9  
  10. 10. >  Google  Ngram:  Privacy    June  2011   ©  Datalicious  Pty  Ltd   10  
  11. 11. Collec+ng  data     for  the  sake  of  it   or  to  add  value   to  customers?  June  2011   ©  Datalicious  Pty  Ltd   11  
  12. 12. >  Data  driven  marke+ng  to  …  §  Improve  media  planning  and  targe-ng  §  Op-mise  media  placements  across  channels  §  Increase  campaign/content  engagement  §  Increase  website/call  center  conversion  §  Iden-fy  profitable  product  bundles/prices  §  Improve  targe-ng  and  increase  up/cross-­‐sell    §  Improve  travel  agent  engagement/training  §  And  much  more  …  June  2011   ©  Datalicious  Pty  Ltd   12  
  13. 13. Product   Partners   Price   Marke+ng  Process   Mix   Place   People   Promo+on   Physical   Evidence  
  14. 14. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Targe+ng  matrix  November  12   ©  Datalicious  Pty  Ltd   14  
  15. 15. Targe+ng   The  right  message   Via  the  right  channel   To  the  right  person   At  the  right  -me  June  2011   ©  Datalicious  Pty  Ltd   15  
  16. 16. >  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  June  2011   ©  Datalicious  Pty  Ltd   16  
  17. 17. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  June  2011   ©  Datalicious  Pty  Ltd   17  
  18. 18. >  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.  June  2011   ©  Datalicious  Pty  Ltd   18  
  19. 19. June  2011   ©  Datalicious  Pty  Ltd   19  
  20. 20. Exercise:  Customer  journey  June  2011   ©  Datalicious  Pty  Ltd   20  
  21. 21. >  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  June  2011   ©  Datalicious  Pty  Ltd   21  
  22. 22. >  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  June  2011   ©  Datalicious  Pty  Ltd   22  
  23. 23. >  Combining  targe+ng  plaIorms     Off-­‐site   targe-ng   Profile   On-­‐site   targe-ng   targe-ng  June  2011   ©  Datalicious  Pty  Ltd   23  
  24. 24. Take  a  closer   look  at  our   cash  flow   solu+ons  November  2010   ©  Datalicious  Pty  Ltd   24  
  25. 25. 0  June  2011   ©  Datalicious  Pty  Ltd   25  
  26. 26. 0  November  2010   ©  Datalicious  Pty  Ltd   26  
  27. 27. >  Affinity  re-­‐targe+ng  in  ac+on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe-ng,     response  rates  are     li[ed  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  h@p://bit.ly/de70b7   12  Month  Caps   - + - +June  2011   ©  Datalicious  Pty  Ltd   27  
  28. 28. >  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  June  2011   ©  Datalicious  Pty  Ltd   28  
  29. 29. >  Prospect  targe+ng  parameters    June  2011   ©  Datalicious  Pty  Ltd   29  
  30. 30. >  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  June  2011   ©  Datalicious  Pty  Ltd   30  
  31. 31. >  Search  call  to  ac+on  for  offline    June  2011   ©  Datalicious  Pty  Ltd   31  
  32. 32. Include  in  press  June  2011   ©  Datalicious  Pty  Ltd   32  
  33. 33. >  PURLs  boos+ng  DM  response  rates   Text  June  2011   ©  Datalicious  Pty  Ltd   33  
  34. 34. >  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  June  2011   ©  Datalicious  Pty  Ltd   34  
  35. 35. >  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  June  2011   ©  Datalicious  Pty  Ltd   35  
  36. 36. >  Jet  Interac+ve  phone  call  data  June  2011   ©  Datalicious  Pty  Ltd   36  
  37. 37. >  Poten+al  calls  to  ac+on    §  Unique  click-­‐through  URLs   Calls  to  ac+on  §  Unique  vanity  domains  or  URLs   can  help  shape  §  Unique  phone  numbers   the  customer  §  Unique  search  terms   experience  not   just  evaluate  §  Unique  email  addresses   responses  §  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  June  2011   ©  Datalicious  Pty  Ltd   37  
  38. 38. >  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  June  2011   ©  Datalicious  Pty  Ltd   38  
  39. 39. >  Transac+ons  plus  behaviours   CRM  Profile   Site  Behaviour   one-­‐off  collec-on  of  demographical  data     tracking  of  purchase  funnel  stage   +   age,  gender,  address,  etc   browsing,  checkout,  etc   customer  lifecycle  metrics  and  key  dates   tracking  of  content  preferences   profitability,  expira+on,  etc   products,  brands,  features,  etc   predic-ve  models  based  on  data  mining   tracking  of  external  campaign  responses   propensity  to  buy,  churn,  etc   search  terms,  referrers,  etc   historical  data  from  previous  transac-ons   tracking  of  internal  promo-on  responses   average  order  value,  points,  etc   emails,  internal  search,  etc   Updated  Occasionally   Updated  Con+nuously  June  2011   ©  Datalicious  Pty  Ltd   39  
  40. 40. >  Customer  profiling  in  ac+on     Using  website  and  email  responses   to  learn  a  limle  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  June  2011   ©  Datalicious  Pty  Ltd   40  
  41. 41. >  Online  form  best  prac+ce   Maximise  data  integrity   Age  vs.  year  of  birth   Free  text  vs.  op-ons   Use  auto-­‐complete     wherever  possible  June  2011   ©  Datalicious  Pty  Ltd   41  
  42. 42. >  Enhancing  data  sources   Customer  profile  data   Geo-­‐demographic  data   +   The  whole  is  greater     than  the  sum  of  its  parts   3rd  party  data  June  2011   ©  Datalicious  Pty  Ltd   42  
  43. 43. >  Geo-­‐demographic  segments  June  2011   ©  Datalicious  Pty  Ltd   43  
  44. 44. >  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.”  June  2011   ©  Datalicious  Pty  Ltd   44  
  45. 45. Exercise:  Targe+ng  matrix  June  2011   ©  Datalicious  Pty  Ltd   45  
  46. 46. >  Exercise:  Targe+ng  matrix   Segments:  Colour,  price,   Purchase   product  affinity,  etc   Media   Data     Cycle   Channels   Points   X   Y   Default,   awareness   Research,   considera+on   Purchase   intent   Reten+on,   up/cross-­‐sell  June  2011   ©  Datalicious  Pty  Ltd   46  
  47. 47. >  Exercise:  Targe+ng  matrix   Segments:  Colour,  price,   Purchase   product  affinity,  etc   Media   Data     Cycle   Channels   Points   X   Y   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   prod  views   Purchase   A  delivers   B  delivers   Website,   Cart  adds,   intent   great  value!   great  value!   emails,  etc   checkouts   Reten+on,   Why  not   Why  not   Direct  mails,   Email  clicks,   up/cross-­‐sell   buy  B?   buy  A?   emails,  etc   logins,  etc  June  2011   ©  Datalicious  Pty  Ltd   47  
  48. 48. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Metrics  framework  November  12   ©  Datalicious  Pty  Ltd   48  
  49. 49. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac+on   Sa+sfac+on   Social  media  June  2011   ©  Datalicious  Pty  Ltd   49  
  50. 50. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)  June  2011   ©  Datalicious  Pty  Ltd   50  
  51. 51. >  Marke+ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  June  2011   ©  Datalicious  Pty  Ltd   51  
  52. 52. >  Addi+onal  funnel  breakdowns     Brand  vs.  direct  response  campaign   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   New  prospects  vs.  exis-ng  customers  June  2011   ©  Datalicious  Pty  Ltd   52  
  53. 53. New  vs.  returning  visitors  June  2011   ©  Datalicious  Pty  Ltd   53  
  54. 54. AU/NZ  vs.  rest  of  world  June  2011   ©  Datalicious  Pty  Ltd   54  
  55. 55. >  Poten+al  funnel  breakdowns    §  Brand  vs.  direct  response  campaign  §  New  prospects  vs.  exis-ng  customers  §  Baseline  vs.  incremental  conversions  §  Compe--ve  ac-vity,  i.e.  none,  a  lot,  etc  §  Segments,  i.e.  age,  loca-on,  influence,  etc  §  Channels,  i.e.  search,  display,  social,  etc  §  Campaigns,  i.e.  this/last  week,  month,  year,  etc  §  Products  and  brands,  i.e.  iphone,  htc,  etc  §  Offers,  i.e.  free  minutes,  free  handset,  etc  §  Devices,  i.e.  home,  office,  mobile,  tablet,  etc      June  2011   ©  Datalicious  Pty  Ltd   55  
  56. 56. Exercise:  Metrics  framework  June  2011   ©  Datalicious  Pty  Ltd   56  
  57. 57. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac+cal   Funnel   breakdowns  June  2011   ©  Datalicious  Pty  Ltd   57  
  58. 58. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   People   People   People   People   reached   engaged   converted   delighted   Level  2   Display   Strategic   impressions   ?   ?   ?   Level  3   Interac+on   Tac+cal   rate,  etc   ?   ?   ?   Funnel   Exis+ng  customers  vs.  new  prospects,  products,  etc   Breakdowns  June  2011   ©  Datalicious  Pty  Ltd   58  
  59. 59. >  Establishing  a  baseline   Switch  all  adver-sing  off  for  a  period   of  -me  (unlikely)  or  establish  a  smaller   control  group  that  is  representa-ve  of   the  en-re  popula-on  (i.e.  search  term,   geography,  etc)  and  switch  off  selected   channels  one  at  a  -me  to  minimise   impact  on  overall  conversions.  June  2011   ©  Datalicious  Pty  Ltd   59  
  60. 60. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  June  2011   ©  Datalicious  Pty  Ltd   60  
  61. 61. Don’t  wait     for  be@er  data,   get  started  now.  June  2011   ©  Datalicious  Pty  Ltd   61  
  62. 62. Contact  me   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  me   twi@er.com/datalicious    June  2011   ©  Datalicious  Pty  Ltd   62  
  63. 63. Data  >  Insights  >  Ac+on  

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