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ADMA Marketing Data Strategy

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The presentation discusses the concepts, principles and significance of data driven marketing.

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ADMA Marketing Data Strategy

  1. 1. >  Marke(ng  Data  Strategy  <   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  May  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  ROAS  May  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    May  2011   ©  Datalicious  Pty  Ltd   4  
  5. 5. >  Clients  across  all  industries  May  2011   ©  Datalicious  Pty  Ltd   5  
  6. 6. >  Data  driven  marke(ng  §  What  is  data  driven  marke-ng?  §  Self  assessment:  Your  capabili-es    §  Strategies  for  effec-ve  data  collec-on  §  Campaign  development  and  data  integrity  §  Effec-ve  mul--­‐channel  campaign  execu-on  §  Analysis  and  performance  measurement  §  In-­‐sourcing  or  outsourcing  May  2011   ©  Datalicious  Pty  Ltd   6  
  7. 7. Clive  Humby:  Data  is  the  new  oil   May  2011   ©  Datalicious  Pty  Ltd   7  
  8. 8. >  Major  data  categories   Campaign  data   TV,  print,  call  center,  search,   web  analy-cs,  ad  serving,  etc       Campaigns   Customers   Customer  data   Direct  mail,  call  center,  web   analy-cs,  emails,  surveys,  etc       Consumer  data   Geo-­‐demographics,  search,   Compe(tors   Consumers   social,  3rd  party  research,  etc       Compe(tor  data   Search,  social,  ad  spend,  3rd   party  research,  news,  etc    May  2011   ©  Datalicious  Pty  Ltd   8  
  9. 9. >  Corporate  data  journey     Stage  1   Stage  2     Stage  3 Data   Insights   Ac(on   Data  is  fully  owned       Sophis-ca-on in-­‐house,  advanced   Data  is  being  brought     predic-ve  modelling   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  May  2011   ©  Datalicious  Pty  Ltd   9  
  10. 10. May  2011   ©  Datalicious  Pty  Ltd   10  
  11. 11. Oil  and  data  come  at  a  price  May  2011   ©  Datalicious  Pty  Ltd   11  
  12. 12. >  Google  Ngram:  Privacy    May  2011   ©  Datalicious  Pty  Ltd   12  
  13. 13. Collec(ng  data     for  the  sake  of  it   or  to  add  value   to  customers?  May  2011   ©  Datalicious  Pty  Ltd   13  
  14. 14. >  Privacy  vs.  data  benefits  policy  §  Do  not  hide  behind  small  print  §  Use  plain  English  in  your  privacy  policy  §  Explain  exactly  what  data  you  are  recording  §  Explain  why  you  are  recording  the  data  §  Explain  the  benefits  for  the  consumer  §  Provide  opt-­‐out  and  feedback  op-ons  §  Make  opt-­‐outs  a  KPI  not  just  opt-­‐ins  =  Data  benefits  and  privacy  policy  May  2011   ©  Datalicious  Pty  Ltd   14  
  15. 15. Exercise:  Marke(ng  mix  May  2011   ©  Datalicious  Pty  Ltd   15  
  16. 16. Product   Partners   Price   Marke(ng  Process   Mix   Place   People   Promo(on   Physical   Evidence  
  17. 17. Targe(ng   The  right  message   Via  the  right  channel   To  the  right  person   At  the  right  -me  May  2011   ©  Datalicious  Pty  Ltd   17  
  18. 18. >  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  May  2011   ©  Datalicious  Pty  Ltd   18  
  19. 19. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  May  2011   ©  Datalicious  Pty  Ltd   19  
  20. 20. >  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.  May  2011   ©  Datalicious  Pty  Ltd   20  
  21. 21. May  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  May  2011   ©  Datalicious  Pty  Ltd   22  
  23. 23. >  Combining  targe(ng  plaIorms     Off-­‐site   targe-ng   Profile   On-­‐site   targe-ng   targe-ng  May  2011   ©  Datalicious  Pty  Ltd   23  
  24. 24. November  2010   ©  Datalicious  Pty  Ltd   24  
  25. 25. Take  a  closer   look  at  our   cash  flow   solu(ons  November  2010   ©  Datalicious  Pty  Ltd   25  
  26. 26. >  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   - + - +May  2011   ©  Datalicious  Pty  Ltd   26  
  27. 27. >  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  May  2011   ©  Datalicious  Pty  Ltd   27  
  28. 28. >  Prospect  targe(ng  parameters    May  2011   ©  Datalicious  Pty  Ltd   28  
  29. 29. November  2010   ©  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  May  2011   ©  Datalicious  Pty  Ltd   30  
  31. 31. >  Search  call  to  ac(on  for  offline    May  2011   ©  Datalicious  Pty  Ltd   31  
  32. 32. May  2011   ©  Datalicious  Pty  Ltd   32  
  33. 33. >  PURLs  boos(ng  DM  response  rates   Text  May  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  May  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  May  2011   ©  Datalicious  Pty  Ltd   35  
  36. 36. >  Jet  Interac(ve  phone  call  data  May  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  May  2011   ©  Datalicious  Pty  Ltd   37  
  38. 38. >  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  May  2011   ©  Datalicious  Pty  Ltd   38  
  39. 39. >  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  May  2011   ©  Datalicious  Pty  Ltd   39  
  40. 40. >  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  May  2011   ©  Datalicious  Pty  Ltd   40  
  41. 41. >  Customer  profiling  in  ac(on     Using  website  and  email  responses   to  learn  a  lille  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  May  2011   ©  Datalicious  Pty  Ltd   41  
  42. 42. >  Online  form  best  prac(ce   Maximise  data  integrity   Age  vs.  year  of  birth   Free  text  vs.  op-ons   Use  auto-­‐complete     wherever  possible  May  2011   ©  Datalicious  Pty  Ltd   42  
  43. 43. Exercise:  Enriching  profiles  May  2011   ©  Datalicious  Pty  Ltd   43  
  44. 44. >  Exercise:  Enriching  profiles   CRM  Profile   Site  Behaviour   ?   +   ?  May  2011   ©  Datalicious  Pty  Ltd   44  
  45. 45. Exercise:  Customer  IDs  May  2011   ©  Datalicious  Pty  Ltd   45  
  46. 46. >  Exercise:  Customer  IDs   To  transac(onal  data   To  reten(on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages  May  2011   ©  Datalicious  Pty  Ltd   46  
  47. 47. >  Enhancing  data  sources   Customer  profile  data   Geo-­‐demographic  data   +   The  whole  is  greater     than  the  sum  of  its  parts   3rd  party  data  May  2011   ©  Datalicious  Pty  Ltd   47  
  48. 48. >  Geo-­‐demographic  segments  May  2011   ©  Datalicious  Pty  Ltd   48  
  49. 49. May  2011   ©  Datalicious  Pty  Ltd   49  
  50. 50. Event  sponsor  presenta(on  May  2011   ©  Datalicious  Pty  Ltd   50  
  51. 51. transcape   data  solu,ons    
  52. 52. E-­‐commerce  customers  Mail  Order  Catalog  Buyers  Magazine  Subscribers  
  53. 53. transcape   Buyer  File       1   Buyer  File     Buyer  File     2   7   Buyer  File     Buyer  File     3   6   Buyer  File     Buyer  File     5   4   "IMP  have  been  working  with  Alliance  Data  ever  since  they  launched  and  have  using   their  Australian  &  NZ  data  with  great  success  across  a  range  of  products"   Victoria Coleman Media Manager International Masters Publishers
  54. 54. transcape     Selectable  by:   Recency   Frequency   Money   Recency Count Frequency Count Spend Count 0  to  6  mo. 371,012 1  x 734,436 Less  Than  $25 138,346 $25  -­‐  $50 131,671 6  to  12  mo. 269,457 2  x 206,257 $50  -­‐  $100 324,512 12  to  18  mo. 295,601 3x 110,751 $100  -­‐  $250 329,338 18  to  24  mo. 397,162 4+ 281,788 $250+ 409,365 Total 1,333,232 Total 1,333,232 Total 1,333,232
  55. 55. transcape     Selectable  by:   Income   Age   Gender   Female   Male  
  56. 56. RFM  Segmenta(on  (house  file)   0-­‐6  mo.   7-­‐12  mo.   13-­‐24  mo.   25-­‐36  mo.   37mo.+   <$10   1.20%   0.70%   0.50%   0.30%   0.10%  $10-­‐$24   1.50%   0.90%   0.70%   0.40%   0.20%  $25-­‐$49   1.80%   1.20%   1.00%   0.50%   0.30%  $50-­‐$99   2.00%   1.70%   1.20%   0.80%   0.40%  $100-­‐$249   2.50%   2.10%   1.50%   1.10%   0.50%   $250+   3.00%+   2.20%   2.00%   1.40%   0.70%     450,000  Buyers   50,000  
  57. 57. Last  bought  from  YOU  25-­‐36  mo.,  $25-­‐$49  Response  Rate  =  0.50%   transcape   35,000  50,000  Buyers   matches   1  .4  million  names  
  58. 58. Last  bought  from  you   Response  Rate  =   0.50%   0.90%  25-­‐36  mo.,  $25-­‐$49   Universe  =   50,000   35,000   20,000  Have  also  bought  elsewhere  Frequency  =     1x   1+   3x   2x   Recency  Value   0-­‐12  mo.   12-­‐24  mo.   25+  mo.   <$25   0.50%   0.30%   0.10%   $25-­‐49   0.70%   0.50%   0.30%   $50-­‐$99   0.90%   0.70%   0.50%   $100+   1.10%   0.90%   0.70%   Further  op(mise  your  house  file  segments  
  59. 59. Transac(onal  Data  Demographic   Geographic  
  60. 60. GeoSmart SegmentsGeodemographic Profile Standard Normalised # Description transcape % Client % Index Index 1 Prestige 1.41 5.45 387.36 6.46 2 High Status Urban 1.11 0.63 56.99 -1.00GeoSmart Groups 3 Desirable Suburban 2.36 7.35 310.86 8.82 4 Affluent Family 1.95 3.68 188.64 3.57 5 High Density Urban 1.07 0.44 41.39 -1.19 Standard Normalised 6 Urban Bohemian 1.25 0.32 25.28 -1.45 # Description transcape % Client % Index Index 7 Affluent Multicultural 1.23 3.11 252.98 3.52 1 High Status Stronger Family 15.11 34.28 226.88 44.39 8 High Status Suburban 2.39 7.48 313.22 8.99 2 High Status Weaker family 5.18 5.51 106.38 0.76 9 Coastal Emplty Nest & Retirement 1.92 1.77 92.26 -0.34 10 Desirable Urban 1.75 4.12 235.94 4.59 3 Mid Status Stronger Family 24.90 25.54 102.55 1.64 11 High Status Family 2.80 0.63 22.65 -3.22 4 Mid Status Weaker family 4.83 6.97 144.43 4.79 12 Mature Affluent Suburban 1.06 4.82 456.31 5.57 5 Low Status Stronger Family 25.02 10.46 41.79 -31.28 13 Aspiring Family 2.89 3.11 107.37 0.48 6 Low Status Weaker family 9.51 11.47 120.66 4.61 14 Mid Status Family Starter 1.69 1.65 97.77 -0.08 15 Affluent Seachange 1.64 1.20 73.50 -0.96 7 Disadvantaged 13.70 4.69 34.24 -16.88 16 Established Multicultural Suburban 2.70 1.90 70.43 -1.75 8 Unclassified 1.75 1.08 61.50 -1.44 17 Urban Lifestyle 0.68 0.70 102.68 0.04 18 Mid Status Suburban 3.01 5.32 176.69 4.90 19 Provincial Fringe 2.11 0.82 39.08 -2.39 20 Metro Fringe 0.87 1.90 218.14 2.02 21 Mid Status Urban 1.66 4.75 285.75 5.58 22 Mixed Multicultural Suburban 0.87 0.13 14.49 -0.89 23 Mining 1.41 1.52 107.92 0.25 24 Mid Status Young Family 3.21 1.39 43.40 -3.53 25 Mature Mid Status Family 4.50 6.59 146.45 4.65 26 Multicultural Urban Lifestyle 0.57 0.00 0.00 27 University Enclaves 0.36 0.51 139.57 0.31 28 Holiday Lifestyle 0.45 0.25 56.21 -0.41 29 Multicultural Mixed Urban 1.10 0.76 69.17 -0.74 30 Establishing Multicultural Family 1.98 0.82 41.66 -2.19 31 Elderly Enclaves 1.15 0.51 44.19 -1.24 32 Establishing Provincial family 2.91 1.20 41.44 -3.24 33 New Age Lifestyle 0.91 0.95 105.02 0.10 34 Mature Provincial Suburban 4.30 1.01 23.60 -5.01 35 Mixed Suburban 0.96 0.19 19.82 -1.07 36 Inland Rural Fringe 1.43 3.36 234.37 3.72 37 Established Multicultural family 1.31 0.13 9.69 -1.16 38 Provincial Mixed Urban 2.17 0.82 37.94 -2.48 39 Low Status Rural Fringe 1.93 0.51 26.25 -2.25 40 Family Achiever 1.91 0.51 26.59 -2.23 41 Old European Blue Collar 1.03 0.95 91.95 -0.19 42 Established Blue Collar Suburban 3.04 6.59 217.10 7.15 43 Blue Collar Family 3.10 2.60 83.72 -1.14 44 Provincial Blue Collar Suburban 5.65 1.77 31.43 -6.73 45 Middle Eastern Multicultural 0.77 0.00 0.00 46 Poor Mixed Urban 1.14 0.82 72.07 -0.70 47 Low Status Mixed Multicultural 1.39 0.70 50.00 -1.40 48 Small Town Blue Collar Suburban 4.39 1.58 36.10 -5.12 49 Established Asian 0.60 0.00 0.00 50 Mobile Holiday Accommodation 0.21 0.13 60.52 -0.17 51 Elderly Provincial Urban 2.04 0.70 34.12 -2.38 52 Provincial Battler 2.93 0.76 25.98 -3.43 53 High Density Welfare 0.16 0.00 0.00 54 Suburban Welfare 0.83 0.00 0.00 55 Indigenous & Remote 1.62 1.08 66.49 -1.17 56 Unclassified 0.13 0.00
  61. 61. transcape   data  solu,ons     Thank  you!  
  62. 62. Exercise:  Targe(ng  matrix  May  2011   ©  Datalicious  Pty  Ltd   62  
  63. 63. >  Exercise:  Targe(ng  matrix   Purchase   Segments:  Colour,  price,   Media   Data     Cycle   product  affinity,  etc   Channels   Points   Default,   awareness   Research,   considera(on   Purchase   intent   Reten(on,   up/cross-­‐sell  May  2011   ©  Datalicious  Pty  Ltd   63  
  64. 64. >  Exercise:  Targe(ng  matrix   Purchase   Segments:  Colour,  price,   Media   Data     Cycle   product  affinity,  etc   Channels   Points   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  May  2011   ©  Datalicious  Pty  Ltd   64  
  65. 65. May  2011   ©  Datalicious  Pty  Ltd   65  
  66. 66. May  2011   ©  Datalicious  Pty  Ltd   66  
  67. 67. May  2011   ©  Datalicious  Pty  Ltd   67  
  68. 68. May  2011   ©  Datalicious  Pty  Ltd   68  
  69. 69. Exercise:  Marke(ng  automa(on  May  2011   ©  Datalicious  Pty  Ltd   69  
  70. 70. May  2011   ©  Datalicious  Pty  Ltd   70  
  71. 71. >  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.”  May  2011   ©  Datalicious  Pty  Ltd   71  
  72. 72. Plan  to  fail  …   May  2011   ©  Datalicious  Pty  Ltd   72  
  73. 73. >  Develop  a  tes(ng  matrix   Test   Segment   Content   KPIs   Poten(al   Results  May  2011   ©  Datalicious  Pty  Ltd   73  
  74. 74. >  Develop  a  tes(ng  matrix   Test   Segment   Content   KPIs   Poten(al   Results   New   Conversion   Next  step,   Test  #1A     prospects   form  A   order,  etc   ?   ?   New   Conversion   Next  step,   Test  #1B   prospects   form  B   order,  etc   ?   ?   New   Conversion   Next  step,   Test  #1N   prospects   form  N   order,  etc   ?   ?   ?   ?   ?   ?   ?   ?  May  2011   ©  Datalicious  Pty  Ltd   74  
  75. 75. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac(on   Sa(sfac(on   Social  media  May  2011   ©  Datalicious  Pty  Ltd   75  
  76. 76. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)  May  2011   ©  Datalicious  Pty  Ltd   76  
  77. 77. >  Marke(ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  May  2011   ©  Datalicious  Pty  Ltd   77  
  78. 78. >  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  May  2011   ©  Datalicious  Pty  Ltd   78  
  79. 79. New  vs.  returning  visitors  May  2011   ©  Datalicious  Pty  Ltd   79  
  80. 80. AU/NZ  vs.  rest  of  world  May  2011   ©  Datalicious  Pty  Ltd   80  
  81. 81. >  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      May  2011   ©  Datalicious  Pty  Ltd   81  
  82. 82. >  Developing  a  metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac(cal   Funnel   breakdowns  May  2011   ©  Datalicious  Pty  Ltd   82  
  83. 83. >  Developing  a  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  May  2011   ©  Datalicious  Pty  Ltd   83  
  84. 84. >  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.  May  2011   ©  Datalicious  Pty  Ltd   84  
  85. 85. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  May  2011   ©  Datalicious  Pty  Ltd   85  
  86. 86. >  Out-­‐sourcing  or  in-­‐sourcing?   Year  1   Year  2     Year  3 PlaIorms   Training   Support     Degree  of  in-­‐house  control  and  sophis-ca-on Reduce  vendor  reliance   to  absolute  minimum   Start  taking  control  of   but  consider  the  value   technology  and  data,   of  support  agreements   shi]  vendor  focus  to   for  both  maintenance   Engage  third  par-es   enhancements  and  the   as  well  as  updates  on   with  more  experience   provision  of  training     market  innova-ons  and   to  get  started  and  to   implement  technology   for  internal  resources   new  features.   Time,  Control  May  2011   ©  Datalicious  Pty  Ltd   86  
  87. 87. Contact  me   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  me   twi;er.com/datalicious    May  2011   ©  Datalicious  Pty  Ltd   87  
  88. 88. Data  >  Insights  >  Ac(on  

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