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Digital Direct Marketing
 

Digital Direct Marketing

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The presentation discusses the application of smart targeting at different stages of the customer life cycle.

The presentation discusses the application of smart targeting at different stages of the customer life cycle.

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    Digital Direct Marketing Digital Direct Marketing Presentation Transcript

    • [  Digital  Direct  Marke.ng  ]   From  prospect  to  customer  –  smart       targe/ng  at  different  stages  of  the   customer  lifecycle  
    • Everyone  has  preferences.     That  is  human  nature.  Users  inform  us  of   their  preferences  through  online  behaviour.   The  ability  to  make  these  insights  ac.onable   and  to  deliver  more  relevant  content  creates   a  be@er  experience  for  users  as  well  as   be@er  results  for  businesses.    14/11/12   ©  Datalicious  Pty  Ltd   2  
    • [  Overview  ]  §  Targe/ng  basics   –  Targe/ng  applica/ons   –  Targe/ng  approaches   –  Affinity  vs.  one-­‐to-­‐one   –  Targe/ng  op/ons   –  AGribu/ng  success  §  Targe/ng  technology   –  Off-­‐site  providers   –  On-­‐site  providers   –  Technology  limita/ons   –  Integra/on  op/ons  §  Targe/ng  management   –  Strategy  development   –  Internal  processes   –  Poten/al  segments  14/11/12   ©  Datalicious  Pty  Ltd   3  
    • 14/11/12   ©  Datalicious  Pty  Ltd   4  
    • 14/11/12   ©  Datalicious  Pty  Ltd   5  
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Targe.ng  basics  ]  14/11/12   ©  Datalicious  Pty  Ltd   6  
    • [  Targe.ng  applica.ons  ]    §  Acquisi/on   –  Convert  prospects  §  Reten/on   –  Up-­‐sell  and  cross-­‐sell   –  Reduce  churn  §  Branding   –  Convert  prospects   –  Build  customer  loyalty  14/11/12   ©  Datalicious  Pty  Ltd   7  
    • [  Targe.ng  approaches  ]  §  Contextual  targe/ng   –  Ads  based  on  viewed  content   –  Anonymous  prospects  (and  customers)  §  Behavioural  targe/ng   –  Ads  based  on  past  behaviour   –  Anonymous  prospects  (and  customers)  §  Profile  targe/ng   –  Ads  based  on  user  profile  database   –  Iden/fied  customers  14/11/12   ©  Datalicious  Pty  Ltd   8  
    • 14/11/12   ©  Datalicious  Pty  Ltd   9  
    • [  Affinity  targe.ng  ]    §  Func/on  of  behavioural  targe/ng   –  Grouping  of  visitors  into  major  segments   –  Based  on  content  and  conversion  behaviour   –  Ease  of  use  vs.  reduced  targe/ng  ability  §  Most  common  affini/es  used   –  Brand  affinity   –  Image  preference   –  Price  sensi/vity   –  Product  affinity   –  Content  affinity  14/11/12   ©  Datalicious  Pty  Ltd   10  
    • [  Affinity  targe.ng  ]   Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe/ng,     response  rates  are     liaed  significantly     across  products.   CTR  By  Category  Affinity   Message   Postpay   Prepay   Broadb.   Business   Blackberry  Bold   - - - + 5GB  Mobile  Broadband   - - + - Blackberry  Storm   + - + + 12  Month  Caps   - + - +14/11/12   ©  Datalicious  Pty  Ltd   11  
    • [  Targe.ng  op.ons  ]  §  Off-­‐site   –  Contextual  targe/ng   –  behavioural  targe/ng   §  Based  on  generic  online  behaviour   §  Based  on  specific  site  behaviour  §  On-­‐site   –  Contextual  targe/ng   –  behavioural  targe/ng   §  Based  on  specific  site  behaviour   –  Profile  targe/ng  14/11/12   ©  Datalicious  Pty  Ltd   12  
    • [  A@ribu.ng  success  ]  §  View-­‐through  conversion   –  Ad  exposure  sufficient   §  All  ads  (or  last)  user  was  exposed  to  receive  conversion  credit   §  Use  in  combina/on  with  click-­‐through  conversion  tracking   §  Cookie  expira/on  sedngs  should  be  sensible  §  Click-­‐through  conversion   –  Ad  click-­‐through  required   §  Only  ads  user  responded  to  can  receive  conversion  credit   §  Define  what  ad  response  receives  credit   –  First,  last,  all  equally,  all  par/ally  §  Cookie  expira/on   –  Define  dura/on  in  days  ads  can  claim  conversion  credit   §  Survey  research  can  help  examine  ad  recollec/on  rate   §  Usually  different  for  on-­‐site  vs.  off-­‐site  ads  14/11/12   ©  Datalicious  Pty  Ltd   13  
    • [  Success  a@ribu.on  models  ]   AD  3   Last  ad  gets     AD  1   AD  2   $100   $100   all  credit   AD  1   First  ad  gets     $100   AD  2   AD  3   $100   all  credit   AD  1   AD  2   AD  3   All  ads  get   $100   $100   $100   $100   equal  credit   AD  1   AD  2   AD  3   All  ads  get   $33   $33   $33   $100   par.al  credit  14/11/12   ©  Datalicious  Pty  Ltd,  www.datalicious.com   14  
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Targe.ng  technology  ]  14/11/12   ©  Datalicious  Pty  Ltd   15  
    • [  Off-­‐site  targe.ng  plaTorms  ]  §  Ad  servers   §  Ad  Networks   –  Eyeblaster   –  Google   –  DoubleClick   –  Yahoo   –  Faciliate   –  ValueClick   –  Atlas   –  Adconian   –  Etc   –  Etc   hGp://en.wikipedia.org/wiki/Contextual_adver/sing,  hGp://hubpages.com/hub/101-­‐Google-­‐Adsense-­‐Alterna/ves,     hGp://en.wikipedia.org/wiki/Central_ad_server,  hGp://www.adopera/onsonline.com/2008/05/23/list-­‐of-­‐ad-­‐servers/,     hGp://lists.econsultant.com/top-­‐10-­‐adver/sing-­‐networks.html,  hGp://www.clickz.com/3633599,  hGp://en.wikipedia.org/wiki/ behavioural_targe/ng      14/11/12   ©  Datalicious  Pty  Ltd   16  
    • 14/11/12   ©  Datalicious  Pty  Ltd   17  
    • [  On-­‐site  targe.ng  plaTorms  ]  §  Test&Target  (Omniture,  Offerma/ca,  TouchClarity)  §  Memetrics  (Accenture)  §  Op/most  (Autonomy)  §  Keaa  (Acxiom)  §  AudienceScience  §  Maxymiser  §  Amadesa  §  Certona  §  SiteSpect  §  BTBuckets  (free,  targe/ng  only)  §  Google  Website  Op/mizer  (free,  tes/ng  only)      14/11/12   ©  Datalicious  Pty  Ltd   18  
    • [  Matching  segments  are  key  ]   On-­‐site     Off-­‐site   segments   segments   On  and  off-­‐site  targe/ng  plamorms  should  use     iden/cal  triggers  to  sort  visitors  into  segments  14/11/12   ©  Datalicious  Pty  Ltd   19  
    • [  Technology  limita.ons  ]  §  JavaScript   –  Relies  on  JavaScript  to  be  enabled  §  Cookies   –  Relies  on  cookies  for  iden/fica/on   §  hGp://blogs.omniture.com/2006/04/08/15-­‐reasons-­‐why-­‐all-­‐ unique-­‐visitors-­‐are-­‐not-­‐created-­‐equal/   §  Mul/ple  users  per  computer   §  Mul/ple  computers   §  Cookie  dele/on  §  Segments   –  Can’t  find  profitable  segments  §  Content   –  Can’t  produce  quality  content  14/11/12   ©  Datalicious  Pty  Ltd   20  
    • [  Integra.on  op.ons  ]    §  Web  analy/cs   –  Record  behavioural  segments  allocated  through  on-­‐site  targe/ng     plamorm  in  web  analy/cs  plamorm  as  well  for  each  visitor   –  Example:  break  down  site  traffic  and  campaign     responses  by  product  category  affinity  §  Ad  serving   –  Replicate  behavioural  segments  allocated  through  on-­‐site     targe/ng  plamorm  in  off-­‐site  ad  serving  environment     –  Example:  use  on-­‐site  targe/ng  plamorm  to  dynamically  write     ad  server  tags  into  each  page  if  visitor  is  in  specific  segment  §  Affiliates   –  Implement  on-­‐site  targe/ng  plamorm  tags  on  affiliate     sites  in  order  to  grow  targe/ng  cookie  pool  faster   –  Example:  display  customized  ads  to  first  /me  site  visitors     although  they  have  only  visited  affiliate  sites  so  far  14/11/12   ©  Datalicious  Pty  Ltd   21  
    • [  Integra.on  op.ons  ]    §  Email   –  Adjust  email  content  for  customers  based  on  behavioural     segments  allocated  through  on-­‐site  targe/ng  plamorm   –  Example:  email  customers  product  sugges/ons  based  on     their  current  content  affinity  and  posi/on  in  purchase  funnel  §  CRM   –  Add  customer  profile  data  to  on-­‐site  behavioural  parameters   –  Example:  record  customer’s  profitability  in  on-­‐site  targe/ng   plamorm  upon  login  on  email  click-­‐through  §  Offline   –  Adjust  on-­‐site  content  based  on  unique  offline  call  to  ac/on   –  Example:  visitors  using  a  specific  call  to  ac/on  see  on-­‐site     ads  matching  the  offline  ads  to  guarantee  consistency  14/11/12   ©  Datalicious  Pty  Ltd   22  
    • [  Maximise  profiling  data  ]   website   data   campaign   customer   data   data  14/11/12   ©  Datalicious  Pty  Ltd   23  
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  Targe.ng  management  ]  14/11/12   ©  Datalicious  Pty  Ltd   24  
    • [  Keys  to  effec.ve  targe.ng  ]   1.  Define  success   2.  Conduct  research   3.  Define  segments   4.  Validate  segments   5.  Define  content   6.  Test  content   7.  Business  rules   8.  Start  targe/ng   9.  Communicate  results  §     14/11/12   ©  Datalicious  Pty  Ltd   25  
    • [  Strategy  and  execu.on  ]   Content   Process   Success  defini/on   Resource  training   Consumer  research   Content  produc/on   Segment  defini/on   Ongoing   Plamorm  maintenance   Segment  valida/on   Targe.ng   Campaign  integra/on   Content  tes/ng   Success   Ongoing  repor/ng   Business  rules   Agency  processes   Segments   Resources  14/11/12   ©  Datalicious  Pty  Ltd   26  
    • [  Prospect  targe.ng  parameters  ]  14/11/12   ©  Datalicious  Pty  Ltd   27  
    • [  Customer  targe.ng  journey  ]   Reten/on   Customer  Profile   Customer  receives  email  with  customized  content,  upgrades  online   Customer  visits  website,  sees  messaging  emphasising  upgrade  benefits   Customer  frequently  visits  specific  product  pages   Customer  reads  news  online,  sees  banner  for  special  customer  offer   Customer  visits  online  help  site  instead  of  calling  call  center   Receives  welcome  email  with  product  FAQ   Prospect   Customer   -­‐12   -­‐11   -­‐10   -­‐9   -­‐8   -­‐7   -­‐6   -­‐5   -­‐4   -­‐3   -­‐2   -­‐1   0   Prospect  receives  reminder  email,  finishes  online  purchase   1   2   3   4   5   6   7   8   9   10   11   12   Prospects  clicks  on  paid  search,  starts  checkout  using  voucher  but  leaves   Prospect  visits  retail  store  for  demonstra/on,  receives  personalized  voucher   Referral  from  affiliate  site,  prospect  sees  customized  offers  on  site   Prospect  sees  print  ad,  executes  unique  search,  sees  customized  offers  on  site   Prospect  sees  banner  ad,  no  response   Considera/on   Visitor  Behaviour   Weeks  14/11/12   ©  Datalicious  Pty  Ltd   28  
    • [  Add  customer  parameters  ]   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  CONTINUOUSLY   UPDATED  OCCASIONALLY  14/11/12   ©  Datalicious  Pty  Ltd   29  
    • [  Mul.ply  iden.fica.on  points  ]   Probability  of  iden/fica/on  through  cookie  140%  120%  100%   80%   60%   40%   20%   0%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks  14/11/12   ©  Datalicious  Pty  Ltd   30  
    • [  Email  iden.fica.on  points  ]   @   Website     Phone   Online  Receipt   research   Conversion   Fulfilment   Confirma/on   @   Adver/sing   Website     Retail   Online  Receipt   Campaign   research   Conversion   Fulfilment   Confirma/on   @   Website     Online   Online  Order   Online  Receipt   research   Conversion   Confirma/on   Fulfilment   Confirma/on   Cookie  ID  14/11/12   ©  Datalicious  Pty  Ltd  &  Omniture  Inc   31  
    • [  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.”  14/11/12   ©  Datalicious  Pty  Ltd   32  
    • 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  [  About  us  ]  14/11/12   ©  Datalicious  Pty  Ltd   33  
    • [  Datalicious  services  ]   Data   Insights   Ac.on   Web  Analy.cs  Solu.ons   Keyword  Research   Search  Lead  Media   Marke.ng  System  Integra.on   Campaign  Repor.ng   Campaign  Op.misa.on   Cross  Channel  Media  Tracking   Segmenta.on/Data  Mining   Internal  Search  Op.misa.on   Online  Surveys/Panels   Quan.ta.ve  Research   Targe.ng/Merchandizing   Omniture  Specialists   Market/Consumer  Trends   A/B,  Mul.variate  Tes.ng   Google  Analy.cs  Specialists   Compe.tor  Analysis   Staff  Training/Workshops  14/11/12   ©  Datalicious  Pty  Ltd   34  
    • [  Datalicious  clients  ]  14/11/12   ©  Datalicious  Pty  Ltd   35  
    • insights@datalicious.com  14/11/12   ©  Datalicious  Pty  Ltd   36