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Data driven marke-ng Increasing campaign response rates through data driven targe3ng

Data Driven Marketing

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

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Page 1: Data Driven Marketing

Data  driven  marke-ng  Increasing  campaign  response  rates  

through  data  driven  targe3ng  

Page 2: Data Driven Marketing

Datalicious  company  history  •  Datalicious  was  founded  in  2007  •  Strong  Omniture  web  analy3cs  history,  now  •  One-­‐stop  data  agency  with  specialist  team  •  Combina3on  of  analysts  and  developers  •  Making  data  accessible  and  ac3onable  •  Driving  industry  best  prac3ce  •  Evangelizing  use  of  data  

August  2010   ©  Datalicious  Pty  Ltd   2  

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Data  driven  marke-ng  

August  2010   ©  Datalicious  Pty  Ltd   3  

Media  a8ribu-on  

Op-mising  channel  mix  

Tes-ng  Improving  usability  

$$$  

Targe-ng    Increasing  relevance  

Page 4: Data Driven Marketing

Increase  revenue  by  10-­‐20%  

August  2010   ©  Datalicious  Pty  Ltd   4  

By  coordina-ng  the  consumer’s  end-­‐to-­‐end  experience,  companies  could  enjoy  revenue  increases  of  10-­‐20%.  

Google:  “get  more  value  from  digital  marke-ng”    or  h8p://bit.ly/cAtSUN  

Source:  McKinsey  Quarterly,  2010  

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The  consumer  data  journey  

August  2010   ©  Datalicious  Pty  Ltd   5  

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      

August  2010   ©  Datalicious  Pty  Ltd   6  

Off-­‐site  targe-ng  

On-­‐site  targe-ng  

Profile    targe-ng  

Genera-ng  awareness  

Crea-ng  engagement  

Maximising  revenue  

TV,  radio,  print,  outdoor,  search  marke3ng,  display  ads,  performance  networks,  affiliates,  social  media,  etc  

Retail  stores,  call  centers,  brochures,  websites,  landing  pages,  mobile  apps,  online  chat,  etc  

Outbound  calls,  direct  mail,  emails,  SMS,  etc  

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Off-­‐site  targe3ng  

On-­‐site  targe3ng  

Profile  targe3ng  

Combining  targe-ng  plaXorms  

August  2010   ©  Datalicious  Pty  Ltd   7  

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On-­‐site    segments  

Off-­‐site  segments  

Combining  technology  plaXorms  

August  2010   ©  Datalicious  Pty  Ltd   8  

On  and  off-­‐site  targe-ng  plaXorms  should  use    iden-cal  triggers  to  sort  visitors  into  segments  

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August  2010   ©  Datalicious  Pty  Ltd   9  

Page 10: Data Driven Marketing

August  2010   ©  Datalicious  Pty  Ltd   10  

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Campaign  response  data  

Combining  data  sets  

August  2010   ©  Datalicious  Pty  Ltd   11  

Customer  profile  data  

+   The  whole  is  greater    than  the  sum  of  its  parts  

Website  behavioural  data  

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Behaviours  plus  transac-ons  

August  2010   ©  Datalicious  Pty  Ltd   12  

one-­‐off  collec3on  of  demographical  data    age,  gender,  address,  etc  customer  lifecycle  metrics  and  key  dates  profitability,  expira-on,  etc  predic3ve  models  based  on  data  mining  

propensity  to  buy,  churn,  etc  historical  data  from  previous  transac3ons  

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  promo3on  responses  

emails,  internal  search,  etc  

Site  Behaviour  

UPDATED  CONTINUOUSLY  

Page 13: Data Driven Marketing

Facebook  as  subscrip-on  op-on  

August  2010   ©  Datalicious  Pty  Ltd   13  

Facebook  Connect  gives  your  company  the  following  data  and  more  with  just  one  click!    Email  address,  first  name,  last  name,  middle  name,  picture,  affilia3ons,  last  profile  update,  3me  zone,  religion,  poli3cal  interests,  interests,  sex,  birthday,  a\racted  to  which  sex,  why  they  want  to  meet  someone,  home  town,  rela3onship  status,  current  loca3on,  ac3vi3es,  music  interests,  tv  show  interests,  educa3on  history,  work  history,  family  and  ID  

Page 14: Data Driven Marketing

August  2010   ©  Datalicious  Pty  Ltd   14  

Flowtown  social  profiling  Name,  age,  gender,  occupa-on,  loca-on,  social    profiles  and  influencer  ranking  based  on  email  

(influencers  only)  

(all  contacts)  

Page 15: Data Driven Marketing

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  overes3mated  visitors  by  up  to  7.6  3mes  whilst  a  cookie-­‐based  approach  overes-mated  visitors  by  up  to  2.3  -mes.    Google:  ”red  eye  cookie  report  pdf”  or  h8p://bit.ly/cszp2o      

Overes-ma-ng  unique  visitors  

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  iden3fica3on  through  Cookies  

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Sample  site  visitor  composi-on  

August  2010   ©  Datalicious  Pty  Ltd   17  

30%  exis-ng  customers  with  extensive  profile  including  transac3onal  history  of  which  maybe  50%  can  actually  be  iden3fied  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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Phase   Segment  A   Segment  B   Channels  

Awareness  

Considera-on  

Purchase  Intent  

Up/Cross-­‐Sell  

Developing  a  targe-ng  matrix  

Page 19: Data Driven Marketing

Phase   Segment  A   Segment  B   Channels  

Awareness   Seen  this?   Social,  display,  search,  etc  

Considera-on   Great  feature!   Social,  search,  website,  etc  

Purchase  Intent   Great  value!   Search,  site,  emails,  etc  

Up/Cross-­‐Sell   Add  this!   Direct  mail,  emails,  etc  

Developing  a  targe-ng  matrix  

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Affinity  targe-ng  in  ac-on  

June  2010   ©  Datalicious  Pty  Ltd   20  

Different  type  of    visitors  respond  to    different  ads.  By  using  category  affinity  targe3ng,    response  rates  are    liied  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  h8p://bit.ly/de70b7  

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Poten-al  newsle8er  layout  

August  2010   ©  Datalicious  Pty  Ltd   21  

Closest    stores,    offers    etc  

Rule  based  header  theme  

Data  verifica-on  

Rule  based  offer  

Profile  based  offer  

Using  data  on  website  behaviour  imported  into  the  email  delivery  plajorm  to  build  business  rules  to  customise  content  delivery.  

NPS  

Page 22: Data Driven Marketing

Poten-al  landing  page  layout  

August  2010   ©  Datalicious  Pty  Ltd   22  

Branded  header  

Email  or  campaign  message  match  

Targeted  offers  

Passing  data  on  user  preferences  through  to  the  website  via  parameters  in  email  click-­‐through  URLs    to  customise  content  delivery.  

Call  to  ac-on  

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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.”  

Quality  content  is  key  

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Google:  “change  one  word  double  conversion”    or  h8p://bit.ly/bpyqFp  

Tes-ng  case  study  

August  2010   ©  Datalicious  Pty  Ltd   24  

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1.  Define  success  metrics  2.  Define  and  validate  segments  3.  Develop  targe3ng  and  message  matrix    4.  Transform  matrix  into  business  rules  5.  Develop  and  test  content  6.  Start  targe3ng  and  automate  7.  Keep  tes3ng  and  refining  8.  Communicate  results  

Keys  to  effec-ve  targe-ng  

August  2010   ©  Datalicious  Pty  Ltd   25  

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August  2010   ©  Datalicious  Pty  Ltd   26  

ADMA  short  course  “Analyse  to  op-mise”    

In  Melbourne  &  Sydney  October/November  

By  Datalicious  

Page 27: Data Driven Marketing

August  2010   ©  Datalicious  Pty  Ltd   27  

Email  me  [email protected]  

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