effective targeting

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> Effec’ve Targe’ng < Coordinate the user experience to boost conversions

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

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Page 1: Effective Targeting

>  Effec've  Targe'ng  <  Coordinate  the  user  experience    

to  boost  conversions  

Page 2: Effective Targeting

>  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  

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>  Smart  data  driven  marke'ng  

June  2010   ©  Datalicious  Pty  Ltd   3  

Media  A>ribu'on  &  Modeling  

Op'mise  channel  mix,  predict  sales  

Tes'ng  &  Op'misa'on  Remove  barriers,  drive  sales  

Boos'ng  ROI  

Targeted  Direct  Marke'ng    Increase  relevance,  reduce  churn  

“Using  data  to  widen  the  funnel”  

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>  Wide  range  of  data  services  

June  2010   ©  Datalicious  Pty  Ltd   4  

Data  PlaKorms    Data  collec'on  and  processing    Web  analy'cs  solu'ons    Omniture,  Google  Analy'cs,  etc    Tag-­‐less  online  data  capture    End-­‐to-­‐end  data  plaKorms    IVR  and  call  center  repor'ng    Single  customer  view  

Insights  Analy'cs    Data  mining  and  modelling    Customised  dashboards    Tableau,  SpoKire,  SPSS,  etc    Media  a>ribu'on  models    Market  and  compe'tor  trends    Social  media  monitoring    Customer  profiling  

Ac'on  Campaigns    Data  usage  and  applica'on    Marke'ng  automa'on    Alterian,  SiteCore,  Inxmail,  etc    Targe'ng  and  merchandising    Internal  search  op'misa'on    CRM  strategy  and  execu'on    Tes'ng  programs    

Page 5: Effective Targeting

>  Clients  across  all  industries  

June  2010   ©  Datalicious  Pty  Ltd   5  

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June  2010   ©  Datalicious  Pty  Ltd   6  

Ques'ons?  Tweet  @datalicious  

 

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The  right  message  Via  the  right  channel  To  the  right  person  At  the  right  ?me  

Targe'ng  

June  2010   ©  Datalicious  Pty  Ltd   7  

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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  

>  Increase  revenue  by  10-­‐20%    

June  2010   ©  Datalicious  Pty  Ltd   8  

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>  New  consumer  decision  journey  

June  2010   ©  Datalicious  Pty  Ltd   9  

The  consumer  decision  process  is  changing  from  linear  to  circular.  

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>  New  consumer  decision  journey  

June  2010   ©  Datalicious  Pty  Ltd   10  

The  consumer  decision  process  is  changing  from  linear  to  circular.  

Change  increases  the  importance  of  experience  during  research  phase.  

Online  research    

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June  2010   ©  Datalicious  Pty  Ltd   11  

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>  The  consumer  data  journey  

June  2010   ©  Datalicious  Pty  Ltd   12  

To  reten'on  messages  To  transac'onal  data  

From  suspect  to   To  customer  

From  behavioural  data   From  awareness  messages  

Time  Time  prospect  

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>  Coordina'on  across  channels        

June  2010   ©  Datalicious  Pty  Ltd   13  

Off-­‐site  targe'ng  

On-­‐site  targe'ng  

Profile    targe'ng  

Genera'ng  awareness  

Crea'ng  engagement  

Maximising  revenue  

TV,  radio,  print,  outdoor,  search  marke?ng,  display  ads,  performance  networks,  affiliates,  social  media,  etc  

Retail  stores,  in-­‐store  kiosks,  call  centers,  brochures,  websites,  mobile  apps,  online  chat,  social  media,  etc  

Outbound  calls,  direct  mail,  emails,  social  media,  SMS,  mobile  apps,  etc  

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Off-­‐site  targe?ng  

On-­‐site  targe?ng  

Profile  targe?ng  

>  Integra'ng  targe'ng  plaKorms    

June  2010   ©  Datalicious  Pty  Ltd   14  

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Campaign  response  data  

>  Combining  data  sources  

June  2010   ©  Datalicious  Pty  Ltd   15  

Customer  profile  data  

+   The  whole  is  greater    than  the  sum  of  its  parts  

Website  behavioural  data  

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>  Transac'ons  plus  behaviours  

June  2010   ©  Datalicious  Pty  Ltd   16  

+  one-­‐off  collec?on  of  demographical  data    age,  gender,  address,  etc  customer  lifecycle  metrics  and  key  dates  profitability,  expira'on,  etc  predic?ve  models  based  on  data  mining  

propensity  to  buy,  churn,  etc  historical  data  from  previous  transac?ons  

average  order  value,  points,  etc  

CRM  Profile  

Updated  Occasionally  

tracking  of  purchase  funnel  stage  

browsing,  checkout,  etc  tracking  of  content  preferences  

products,  brands,  features,  etc  tracking  of  external  campaign  responses  

search  terms,  referrers,  etc  tracking  of  internal  promo?on  responses  

emails,  internal  search,  etc  

Site  Behaviour  

Updated  Con'nuously  

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>  Sample  customer  level  data    

June  2010   ©  Datalicious  Pty  Ltd   17  

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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.    

>  Unique  visitor  overes'ma'on    

June  2010   ©  Datalicious  Pty  Ltd   18  

Source:  White  Paper,  RedEye,  2007  

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>  Maximise  iden'fica'on  points    

20%  

40%  

60%  

80%  

100%  

120%  

140%  

160%  

0   4   8   12   16   20   24   28   32   36   40   44   48  

Weeks  

−−−  Probability  of  iden?fica?on  through  Cookies  

June  2010   19  ©  Datalicious  Pty  Ltd  

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>  Customer  profiling  in  ac'on    

June  2010   ©  Datalicious  Pty  Ltd   20  

Using  website  and  email  responses  to  learn  a  lifle  bite  more  about  

subscribers  at  every    touch  point  to  keep  

 refining  profiles  and  messages.  

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On-­‐site    segments  

Off-­‐site  segments  

>  Combining  ad  plaKorms  

June  2010   ©  Datalicious  Pty  Ltd   21  

CRM  

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>  The  Datalicious  SuperTag  

June  2010   ©  Datalicious  Pty  Ltd   22  

SuperTag  

Ad    Servers  

Paid    Search  

Affiliate  Programs  

Behavioral  Targe'ng  

A/B  Tes'ng  Heat  Maps  

Live    Chat  

Web  Analy'cs  

Media  A>ribu'on    

Easily  implement  and  update  any  tag  on  any  websites  without  IT  involvement.    De-­‐duplicate  conversions  and  collect  media  afribu?on  data  to  boost  return  on  ad  spend.    Implement  complex  re-­‐targe?ng  strategies  across  plagorms  to  increase  response  rates.    Enable  advanced  features  such  heat  maps,  tes?ng  and  live  chat  to  op?mise  conversions.  

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June  2010   ©  Datalicious  Pty  Ltd   23  

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June  2010   ©  Datalicious  Pty  Ltd   24  

Apple  iPhone  4  

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June  2010   ©  Datalicious  Pty  Ltd   25  

Apple  iPhone  4  

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June  2010   ©  Datalicious  Pty  Ltd   26  

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June  2010   ©  Datalicious  Pty  Ltd   27  

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>  Affinity  re-­‐targe'ng  in  ac'on    

June  2010   ©  Datalicious  Pty  Ltd   28  

Different  type  of    visitors  respond  to    different  ads.  By  using  category  affinity  targe?ng,    response  rates  are    lihed  significantly    across  products.  

Message  CTR  By  Category  Affinity  

Postpay   Prepay   Broadb.   Business  

Blackberry  Bold   - - - + 5GB  Mobile  Broadband   - - + - Blackberry  Storm   + - + + 12  Month  Caps   - + - +

Google:  “vodafone  omniture  case  study”    or  h>p://bit.ly/de70b7  

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>  Ad-­‐sequencing  in  ac'on  

June  2010   ©  Datalicious  Pty  Ltd   29  

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  

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>  Sample  site  visitor  composi'on    

June  2010   ©  Datalicious  Pty  Ltd   30  

30%  exis'ng  customers  with  extensive  profile  including  transac?onal  history  of  which  maybe  50%  can  actually  be  iden?fied  as  individuals    

30%  new  visitors  with  no  previous  website  history  aside  from  campaign  or  referrer  data  of  which  maybe  50%  is  useful  

10%  serious  prospects  with  limited  profile  data  

30%  repeat  visitors  with  referral  data  and  some  website  history  allowing  50%  to  be  segmented  by  content  affinity  

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>  Search  call  to  ac'on  for  offline    

June  2010   ©  Datalicious  Pty  Ltd   31  

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>  PURLs  boos'ng  DM  response  rates  

June  2010   ©  Datalicious  Pty  Ltd   32  

Text  

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>  Unique  phone  numbers  

June  2010   ©  Datalicious  Pty  Ltd   33  

2  out  of  3  callers  hang  up  as  they  cannot  get  their    informa?on  fast  enough.    Unique  phone  numbers  can  help  improve  call  experience.  

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Purchase  Cycle  

Segments:  Colour,  price,  product  affinity,  etc  

Media  Channels  

Data    Points  

Default,  awareness  

Have  you    seen  A?  

Have  you    seen  B?  

Display,  search,  etc   Default  

Research,  considera'on  

A  has  great    features!  

B  has  great    features!  

Search,  website,  etc  

Ad  clicks,  prod  views  

Purchase  intent  

A  delivers  great  value!  

B  delivers  great  value!  

Website,  emails,  etc  

Cart  adds,  checkouts  

Reten'on,  up/cross-­‐sell  

Why  not  buy  B?  

Why  not  buy  A?  

Direct  mails,  emails,  etc  

Email  clicks,  logins,  etc  

>  Developing  a  targe'ng  matrix  

June  2010   ©  Datalicious  Pty  Ltd   34  

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>  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  

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>  Google  Ngram:  Privacy    

June  2010   ©  Datalicious  Pty  Ltd   36  

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June  2010   ©  Datalicious  Pty  Ltd  

Collec'ng  data    for  the  sake  of  it  or  to  add  value  to  customers?  

37  

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June  2010   ©  Datalicious  Pty  Ltd   38  

Contact  me  [email protected]  

 Learn  more  

blog.datalicious.com    

Follow  me  twi>er.com/datalicious  

 

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Data  >  Insights  >  Ac'on  

June  2010   ©  Datalicious  Pty  Ltd   39