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Effective Targeting
 

Effective Targeting

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

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

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    Effective Targeting Effective Targeting Presentation Transcript

    • >  Effecve  Targeng  <   Coordinate  the  user  experience     to  boost  conversions  
    • >  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  2010   ©  Datalicious  Pty  Ltd   2  
    • >  Smart  data  driven  markeng   “Using  data  to  widen  the  funnel”   Media  A>ribuon  &  Modeling   Opmise  channel  mix,  predict  sales   Targeted  Direct  Markeng     Increase  relevance,  reduce  churn   Tesng  &  Opmisaon   Remove  barriers,  drive  sales   Boosng  ROI  June  2010   ©  Datalicious  Pty  Ltd   3  
    • >  Wide  range  of  data  services   Data   Insights   Acon   PlaKorms   Analycs   Campaigns         Data  collecon  and  processing   Data  mining  and  modelling   Data  usage  and  applicaon         Web  analycs  soluons   Customised  dashboards   Markeng  automaon         Omniture,  Google  Analycs,  etc   Tableau,  SpoKire,  SPSS,  etc   Alterian,  SiteCore,  Inxmail,  etc         Tag-­‐less  online  data  capture   Media  a>ribuon  models   Targeng  and  merchandising         End-­‐to-­‐end  data  plaKorms   Market  and  competor  trends   Internal  search  opmisaon         IVR  and  call  center  reporng   Social  media  monitoring   CRM  strategy  and  execuon         Single  customer  view   Customer  profiling   Tesng  programs    June  2010   ©  Datalicious  Pty  Ltd   4  
    • >  Clients  across  all  industries  June  2010   ©  Datalicious  Pty  Ltd   5  
    • Quesons?   Tweet  @datalicious    June  2010   ©  Datalicious  Pty  Ltd   6  
    • Targeng   The  right  message   Via  the  right  channel   To  the  right  person   At  the  right  ?me  June  2010   ©  Datalicious  Pty  Ltd   7  
    • >  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   8  
    • >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.  June  2010   ©  Datalicious  Pty  Ltd   9  
    • >  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   10  
    • June  2010   ©  Datalicious  Pty  Ltd   11  
    • >  The  consumer  data  journey   To  transaconal  data   To  retenon  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages  June  2010   ©  Datalicious  Pty  Ltd   12  
    • >  Coordinaon  across  channels         Generang   Creang   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     targeng   targeng   targeng  June  2010   ©  Datalicious  Pty  Ltd   13  
    • >  Integrang  targeng  plaKorms     Off-­‐site   targe?ng   Profile   On-­‐site   targe?ng   targe?ng  June  2010   ©  Datalicious  Pty  Ltd   14  
    • >  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   15  
    • >  Transacons  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,  expiraon,  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  Connuously  June  2010   ©  Datalicious  Pty  Ltd   16  
    • >  Sample  customer  level  data    June  2010   ©  Datalicious  Pty  Ltd   17  
    • >  Unique  visitor  overesmaon    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  overesmated  visitors  by  up  to  2.3  mes.    June  2010   ©  Datalicious  Pty  Ltd   18   Source:  White  Paper,  RedEye,  2007  
    • >  Maximise  idenficaon  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   19  
    • >  Customer  profiling  in  acon     Using  website  and  email  responses   to  learn  a  lifle  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.  June  2010   ©  Datalicious  Pty  Ltd   20  
    • >  Combining  ad  plaKorms   On-­‐site     Off-­‐site   segments   segments   CRM  June  2010   ©  Datalicious  Pty  Ltd   21  
    • >  The  Datalicious  SuperTag   Easily  implement  and  update   Ad     any  tag  on  any  websites  without   Servers   IT  involvement.   Media   Paid     A>ribuon     Search     De-­‐duplicate  conversions  and   collect  media  afribu?on  data  to   boost  return  on  ad  spend.   Web   Affiliate   Analycs   SuperTag   Programs     Implement  complex  re-­‐targe?ng   strategies  across  plagorms  to   increase  response  rates.   Live     Behavioral   Chat   Targeng     A/B  Tesng   Enable  advanced  features  such   Heat  Maps   heat  maps,  tes?ng  and  live  chat   to  op?mise  conversions.  June  2010   ©  Datalicious  Pty  Ltd   22  
    • June  2010   ©  Datalicious  Pty  Ltd   23  
    • Apple   iPhone  4  June  2010   ©  Datalicious  Pty  Ltd   24  
    • Apple  iPhone  4  June  2010   ©  Datalicious  Pty  Ltd   25  
    • June  2010   ©  Datalicious  Pty  Ltd   26  
    • June  2010   ©  Datalicious  Pty  Ltd   27  
    • >  Affinity  re-­‐targeng  in  acon     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe?ng,     response  rates  are     lihed  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   28  
    • >  Ad-­‐sequencing  in  acon   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   29  
    • >  Sample  site  visitor  composion     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%  exisng  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   30  
    • >  Search  call  to  acon  for  offline    June  2010   ©  Datalicious  Pty  Ltd   31  
    • >  PURLs  boosng  DM  response  rates   Text  June  2010   ©  Datalicious  Pty  Ltd   32  
    • >  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   33  
    • >  Developing  a  targeng  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,   consideraon   features!   features!   website,  etc   prod  views   Purchase   A  delivers   B  delivers   Website,   Cart  adds,   intent   great  value!   great  value!   emails,  etc   checkouts   Retenon,   Why  not   Why  not   Direct  mails,   Email  clicks,   up/cross-­‐sell   buy  B?   buy  A?   emails,  etc   logins,  etc  June  2010   ©  Datalicious  Pty  Ltd   34  
    • >  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   35  
    • >  Google  Ngram:  Privacy    June  2010   ©  Datalicious  Pty  Ltd   36  
    • Collecng  data     for  the  sake  of  it   or  to  add  value   to  customers?  June  2010   ©  Datalicious  Pty  Ltd   37  
    • Contact  me   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  me   twi>er.com/datalicious    June  2010   ©  Datalicious  Pty  Ltd   38  
    • Data  >  Insights  >  Acon  June  2010   ©  Datalicious  Pty  Ltd   39