SuoerIQ Analytics

315 views

Published on

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

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
315
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

SuoerIQ Analytics

  1. 1. >  SuperIQ  Analy/cs  <   Smart  data  driven  marke-ng  
  2. 2. >  Smart  data  driven  marke/ng   “Using  data  to  widen  the  funnel”   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   Boos/ng  ROI  June  2010   ©  Datalicious  Pty  Ltd   2  
  3. 3. Clive  Humby:  Data  is  the  new  oil   June  2010   ©  Datalicious  Pty  Ltd   3  
  4. 4. Oil  and  data  come  at  a  price  June  2010   ©  Datalicious  Pty  Ltd   4  
  5. 5. >  Google  Ngram:  Privacy    June  2010   ©  Datalicious  Pty  Ltd   5  
  6. 6. Collec/ng  data     for  the  sake  of  it   or  to  add  value   to  customers?  June  2010   ©  Datalicious  Pty  Ltd   6  
  7. 7. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Metrics  framework  June  2010   ©  Datalicious  Pty  Ltd   7  
  8. 8. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac/on   Sa/sfac/on   Social  media  June  2010   ©  Datalicious  Pty  Ltd   8  
  9. 9. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)  June  2010   ©  Datalicious  Pty  Ltd   9  
  10. 10. >  Marke/ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted  June  2010   ©  Datalicious  Pty  Ltd   10  
  11. 11. >  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  2010   ©  Datalicious  Pty  Ltd   11  
  12. 12. New  vs.  returning  visitors  June  2010   ©  Datalicious  Pty  Ltd   12  
  13. 13. AU/NZ  vs.  rest  of  world  June  2010   ©  Datalicious  Pty  Ltd   13  
  14. 14. >  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  2010   ©  Datalicious  Pty  Ltd   14  
  15. 15. Exercise:  Metrics  framework  June  2010   ©  Datalicious  Pty  Ltd   15  
  16. 16. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac/cal   Funnel   breakdowns  June  2010   ©  Datalicious  Pty  Ltd   16  
  17. 17. >  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  2010   ©  Datalicious  Pty  Ltd   17  
  18. 18. >  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  2010   ©  Datalicious  Pty  Ltd   18  
  19. 19. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless  June  2010   ©  Datalicious  Pty  Ltd   19  
  20. 20. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Media  a;ribu/on  June  2010   ©  Datalicious  Pty  Ltd   20  
  21. 21. >  Campaign  flows  are  complex   =  Paid  media   Organic     PR,  WOM,   search   events,  etc   =  Viral  elements   =  Sales  channels   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   CRM   Facebook   program   Twi;er,  etc   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc  June  2011   ©  Datalicious  Pty  Ltd   21  
  22. 22. >  Media  channels  overlap   TV/Print     audience   Banner   Search   audience   audience  June  2011   ©  Datalicious  Pty  Ltd   22  
  23. 23. >  Indirect  display  impact    June  2011   ©  Datalicious  Pty  Ltd   23  
  24. 24. >  Indirect  display  impact    June  2011   ©  Datalicious  Pty  Ltd   24  
  25. 25. >  Success  a;ribu/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  June  2011   ©  Datalicious  Pty  Ltd   25  
  26. 26. >  First  and  last  click  a;ribu/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    June  2011   ©  Datalicious  Pty  Ltd   26  
  27. 27. >  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  June  2011   ©  Datalicious  Pty  Ltd   27  
  28. 28. >  Search  call  to  ac/on  for  offline    June  2011   ©  Datalicious  Pty  Ltd   28  
  29. 29. >  PURLs  boos/ng  DM  response  rates   Text  June  2011   ©  Datalicious  Pty  Ltd   30  
  30. 30. >  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  June  2011   ©  Datalicious  Pty  Ltd   31  
  31. 31. >  Understanding  channel  mix  June  2011   ©  Datalicious  Pty  Ltd   32  
  32. 32. >  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  aeributed  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.    June  2011   ©  Datalicious  Pty  Ltd   34  
  33. 33. >  Adjus/ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10  June  2011   ©  Datalicious  Pty  Ltd   35  
  34. 34. 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  >  Smart  targe/ng  June  2010   ©  Datalicious  Pty  Ltd   36  
  35. 35. Targe/ng   The  right  message   Via  the  right  channel   To  the  right  person   At  the  right  -me  June  2010   ©  Datalicious  Pty  Ltd   37  
  36. 36. >  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  2010   ©  Datalicious  Pty  Ltd   38  
  37. 37. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  June  2010   ©  Datalicious  Pty  Ltd   39  
  38. 38. >  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  2010   ©  Datalicious  Pty  Ltd   40  
  39. 39. June  2010   ©  Datalicious  Pty  Ltd   41  
  40. 40. >  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  2010   ©  Datalicious  Pty  Ltd   42  
  41. 41. >  Integra/ng  targe/ng  planorms     Off-­‐site   targe-ng   Profile   On-­‐site   targe-ng   targe-ng  June  2010   ©  Datalicious  Pty  Ltd   43  
  42. 42. >  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data  June  2010   ©  Datalicious  Pty  Ltd   44  
  43. 43. >  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  2010   ©  Datalicious  Pty  Ltd   45  
  44. 44. >  Sample  customer  level  data    June  2010   ©  Datalicious  Pty  Ltd   46  
  45. 45. >  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.    June  2010   ©  Datalicious  Pty  Ltd   47   Source:  White  Paper,  RedEye,  2007  
  46. 46. >  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  June  2010   ©  Datalicious  Pty  Ltd   48  
  47. 47. >  Customer  profiling  in  ac/on     Using  website  and  email  responses   to  learn  a  liele  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  June  2010   ©  Datalicious  Pty  Ltd   49  
  48. 48. >  Enhancing  data  sources   Customer  profile  data   Geo-­‐demographic  data   +   The  whole  is  greater     than  the  sum  of  its  parts   3rd  party  data  June  2010   ©  Datalicious  Pty  Ltd   50  
  49. 49. >  Geo-­‐demographic  segments  June  2010   ©  Datalicious  Pty  Ltd   51  
  50. 50. June  2010   ©  Datalicious  Pty  Ltd   52  
  51. 51. >  Combining  ad  planorms   On-­‐site     Off-­‐site   segments   segments   CRM  June  2010   ©  Datalicious  Pty  Ltd   53  
  52. 52. >  The  Datalicious  SuperTag   Easily  implement  and  update   Ad     any  tag  on  any  websites  without   Servers   IT  involvement.   Media   Paid     A;ribu/on     Search     De-­‐duplicate  conversions  and   collect  media  aeribu-on  data  to   boost  return  on  ad  spend.   Web   Affiliate   Analy/cs   SuperTag   Programs     Implement  complex  re-­‐targe-ng   strategies  across  plakorms  to   increase  response  rates.   Live     Behavioral   Chat   Targe/ng     A/B  Tes/ng   Enable  advanced  features  such   Heat  Maps   heat  maps,  tes-ng  and  live  chat   to  op-mise  conversions.  June  2010   ©  Datalicious  Pty  Ltd   54  
  53. 53. June  2010   ©  Datalicious  Pty  Ltd   55  
  54. 54. Apple   iPhone  4  June  2010   ©  Datalicious  Pty  Ltd   56  
  55. 55. Apple  iPhone  4  June  2010   ©  Datalicious  Pty  Ltd   57  
  56. 56. >  Affinity  re-­‐targe/ng  in  ac/on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe-ng,     response  rates  are     liled  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  2010   ©  Datalicious  Pty  Ltd   58  
  57. 57. >  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  2010   ©  Datalicious  Pty  Ltd   59  
  58. 58. >  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  2010   ©  Datalicious  Pty  Ltd   60  
  59. 59. >  Search  call  to  ac/on  for  offline    June  2010   ©  Datalicious  Pty  Ltd   61  
  60. 60. >  PURLs  boos/ng  DM  response  rates   Text  June  2010   ©  Datalicious  Pty  Ltd   62  
  61. 61. >  Unique  phone  numbers   2  out  of  3  callers   hang  up  as  they   cannot  get  their     informa-on  fast   enough.     Unique  phone   numbers  can   help  improve   call  experience.  June  2010   ©  Datalicious  Pty  Ltd   63  
  62. 62. Exercise:  Client  data  journey  June  2010   ©  Datalicious  Pty  Ltd   64  
  63. 63. >  Exercise:  Client  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  2010   ©  Datalicious  Pty  Ltd   65  
  64. 64. June  2010   ©  Datalicious  Pty  Ltd   66  
  65. 65. Exercise:  Targe/ng  matrix  June  2010   ©  Datalicious  Pty  Ltd   67  
  66. 66. >  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  June  2010   ©  Datalicious  Pty  Ltd   68  
  67. 67. >  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  June  2010   ©  Datalicious  Pty  Ltd   69  
  68. 68. >  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  2010   ©  Datalicious  Pty  Ltd   70  
  69. 69. >  ClickTale  tes/ng  case  study    June  2010   ©  Datalicious  Pty  Ltd   71  
  70. 70. Exercise:  Tes/ng  matrix  June  2010   ©  Datalicious  Pty  Ltd   72  
  71. 71. >  Exercise:  Tes/ng  matrix   Test   Segment   Content   KPIs   Poten/al   Results  June  2010   ©  Datalicious  Pty  Ltd   73  
  72. 72. >  Exercise:  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   ?   ?   ?   ?   ?   ?   ?   ?  June  2010   ©  Datalicious  Pty  Ltd   74  
  73. 73. Don’t  wait     for  be;er  data,   get  started  now.  June  2010   ©  Datalicious  Pty  Ltd   75  
  74. 74. Contact  me   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  me   twi;er.com/datalicious    June  2010   ©  Datalicious  Pty  Ltd   76  
  75. 75. Data  >  Insights  >  Ac/on  June  2010   ©  Datalicious  Pty  Ltd   77  

×