data driven marketing - the key to an effective marketing campaign

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

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[  Data  driven  marke.ng  ]  Data  to  help  create  highly  targeted  

and  engaging  campaigns  

[  Quick  company  history  ]  

§  Datalicious  was  founded  in  2007  §  Strong  Omniture  web  analy<cs  history  §  1  of  4  global  Omniture  Preferred  Partners  §  Now  360  data  agency  with  specialist  team  §  Combina<on  of  analysts  and  developers  §  Evangelizing  smart  data  driven  marke<ng  § Making  data  accessible  and  ac<onable  §  Driving  industry  best  prac<ce  (ADMA)  

September  2010   ©  Datalicious  Pty  Ltd   2  

[  Clients  across  all  industries  ]  

September  2010   ©  Datalicious  Pty  Ltd   3  

[  Using  data  to  reduce  waste  ]  

September  2010   ©  Datalicious  Pty  Ltd   4  

Media  a>ribu.on  

Op.mising  channel  mix  

Tes.ng  Improving  usability  

$$$  

Targe.ng    Increasing  relevance  

[  The  consumer  data  journey  ]  

September  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  

[  Coordina.on  across  channels  ]      

September  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  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  

Off-­‐site  targe<ng  

On-­‐site  targe<ng  

Profile  targe<ng  

[  Combining  targe.ng  plaKorms  ]  

September  2010   ©  Datalicious  Pty  Ltd   7  

September  2010   ©  Datalicious  Pty  Ltd   8  h>p://ww.wesKield.com?data=zimbio,promo.on  

[  Search  and  media  planning  ]  

September  2010   ©  Datalicious  Pty  Ltd   9  

September  2010   ©  Datalicious  Pty  Ltd   10  cookie:  zimbio,  promo.on,  chris.ne,  fashion  

[  Affinity  targe.ng  in  ac.on  ]  

September  2010   ©  Datalicious  Pty  Ltd   11  

Different  type  of    visitors  respond  to    different  ads.  By  using  category  affinity  targe<ng,    response  rates  are    li\ed  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  

September  2010   ©  Datalicious  Pty  Ltd   12  h>p://ww.wesKield.com?data=chris.ne,promo.on  

[  Customer  profiling  in  ac.on  ]  

September  2010   ©  Datalicious  Pty  Ltd   13  

Using  website  and  email  responses  to  learn  a  li_le  bite  more  about  customers  at  every  touch  point  in  order  to  keep  refining  customer  profiles  and  customising  future  communica<ons.  

Phase   Segment  A/B   Channels   Data  Points  

Awareness   Seen  this?   Social,  display,  search,  etc   Default  

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

Download,  product  view  

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

Cart  add,  checkout,  etc  

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

Email  response,  login,  etc  

[  Developing  a  targe.ng  matrix  ]  

September  2010   14  ©  Datalicious  Pty  Ltd  

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  key  to  success  ]  

September  2010   15  ©  Datalicious  Pty  Ltd  

Campaign  response  data  

[  Combining  data  sets  ]  

September  2010   ©  Datalicious  Pty  Ltd   16  

Customer  profile  data  

+   The  whole  is  greater    than  the  sum  of  its  parts  

Website  behavioural  data  

[  Behaviours  plus  transac.ons  ]  

September  2010   ©  Datalicious  Pty  Ltd   17  

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  

[  Sample  customer  level  data  ]  

September  2010   ©  Datalicious  Pty  Ltd   18  

[  Social  media  as  data  source  ]  

September  2010   ©  Datalicious  Pty  Ltd   19  

Facebook  Connect  gives  your  company  the  following  data  and  more  with  just  one  click    Email  address,  first  name,  last  name,  gender,  birthday,  interests,  picture,  affilia<ons,  last  profile  update,  <me  zone,  religion,  poli<cal  interests,  a_racted  to  which  sex,  why  they  want  to  meet  someone,  home  town,  rela<onship  status,  current  loca<on,  ac<vi<es,  music  interests,  tv  show  interests,  educa<on  history,  work  history,  family,  etc   Need  anything  else?  

September  2010   ©  Datalicious  Pty  Ltd   20  

(influencers  only)  

(all  contacts)  

September  2010   21  ©  Datalicious  Pty  Ltd  

Appending  social  data  to  customer  profiles  Name,  age,  gender,  occupa.on,  loca.on,  social    profiles  and  influencer  ranking  based  on  email  

[  Social  media  data  poten.al  ]  

§  Large  Australian  consumer  brand  §  20%  of  customer  emails  had  social  profiles  §  Each  profile  had  an  average  of  8  friends  §  2%  of  profiles  had  an  influencer  score  §  0.5%  of  social  had  a  score  of  over  10  §  For  a  database  of  500,000  that  would  mean  §  Poten<al  addi<onal  reach  of  100,000  friends  §  Includes  2,500  influen<al  individuals  September  2010   ©  Datalicious  Pty  Ltd   22  

[  Overall  volume  and  influence  ]  

September  2010   ©  Datalicious  Pty  Ltd   23  

Data  from  

[  Influence  and  media  value  ]  

September  2010   ©  Datalicious  Pty  Ltd   24  

US  

UK  

AU/NZ  

Data  from  

[  Google  data  in  Australia  ]  

September  2010   ©  Datalicious  Pty  Ltd   25  

Source:  h_p://www.hitwise.com/au/datacentre  

[  Search  at  all  stages  ]  

September  2010   ©  Datalicious  Pty  Ltd   26  

Source:  Inside  the  Mind  of  the  Searcher,  Enquiro  2004  

[  Search  and  brand  strength  ]  

September  2010   ©  Datalicious  Pty  Ltd   27  

[  Search  and  the  product  lifecycle  ]  

September  2010   ©  Datalicious  Pty  Ltd   28  

Nokia  N-­‐Series  

Apple  iPhone  

[  Search  driving  offline  crea.ve  ]  

September  2010   ©  Datalicious  Pty  Ltd   29  

Direct  mail,    email,  etc  

Facebook  Twi>er,  etc  

[  Mapping  out  campaign  flows  ]  

September  2010   ©  Datalicious  Pty  Ltd   30  

POS  kiosks,  loyalty  cards,  etc  

CRM  program  

Home  pages,  portals,  etc  

YouTube,    blog,  etc  

Paid    search  

Organic    search  

Landing  pages,  offers,  etc  

PR,  WOM,  events,  etc  

TV,  print,    radio,  etc  

C2  

C3  

=  Paid  media  

=  Viral  elements  

Call  center,    retail  stores,  etc  

=  Coupons,  surveys  

Display  ads,  affiliates,  etc  

C1  

People  Reached  

People  Engaged  

People  Converted  

People  Delighted  

[  Developing  a  metrics  framework  ]  

September  2010   ©  Datalicious  Pty  Ltd   31  

40%   10%   1%  

Quan<ta<ve  and  qualita<ve  research  data  

Website,  call  center  and  retail  data  

Social  media  data  

Media  and  search  data  

Social  media  

[  De-­‐duplica.on  across  channels  ]  

September  2010   ©  Datalicious  Pty  Ltd   32  

Banner    Ads  

Email    Blast  

Paid    Search  

Organic  Search  

$  Bid    Mgmt  

Ad    Server  

Email  PlaKorm  

Google  Analy.cs  

$  

$  

$  

Central  Analy.cs  PlaKorm  

$  

$  

$  

[  Success  a>ribu.on  models  ]  

Banner    Ad  $100  

Email    Blast  

Paid    Search  $100  

Banner    Ad  $100  

Affiliate    Referral  $100  

Success  $100  

Success  $100  

Banner    Ad  

Paid    Search  

Organic  Search  $100  

Success  $100  

Last  channel  gets  all  credit  

First  channel  gets  all  credit  

All  channels  get  equal  credit  

Print    Ad  $33  

Social    Media  $33  

Paid    Search  $33  

Success  $100  

All  channels  get  par.al  credit  

Paid    Search  

September  2010   33  ©  Datalicious  Pty  Ltd  

[  Search  call  to  ac.on  for  offline  ]  

September  2010   ©  Datalicious  Pty  Ltd   34  

September  2010   ©  Datalicious  Pty  Ltd   35  

[  Understanding  channel  mix  ]  

September  2010   ©  Datalicious  Pty  Ltd   36  

[  Target  Denim  ]  

September  2010   ©  Datalicious  Pty  Ltd   37  

§  51,737  Visitors  §  521,857  Unique  Page  

Views    §  11,402  people  shared  

on  Facebook  (Most  from  emails  or  Facebook)  

§  6,821  TVC  Views  §  82%  New  Visits  (Target  

average  73%)  §  2,005  Wins  §  Average  Time  on  site  

is  2.25  minutes  (Target  average  1.07  minutes)  

©  Datalicious  Pty  Ltd  

[  Key  traffic  drivers  ]  

September  2010   38  

NB:  Removed  data  from  Friday  Feb  11th  as  due  to  extreme  skew  

§  The  campaign  had  a  huge  first  day  before  paid  media  began  which  built  momentum  early  

[  YouTube  ]  §  13,084  YouTube  views,  70  

comments,  636  ra<ngs  (490  bad,  136  good)    –  Silvia  Pfeiffer  from  Vquence  found  that  

males  aged  15  –  25  were  more  likely  to  comment  than  any  other  demographic  

September  2010   ©  Datalicious  Pty  Ltd   39  

Engagement  compared  to  videos  of  similar  length  

§  Higher  than  average  engagement  from  viewers  compared  to  videos  of  a  similar  length  

§  Honourable  men<ons  for  the  week  ending  the  Feb  21st    

[  Campaign  comparison  ]  

September  2010   ©  Datalicious  Pty  Ltd   40  

§  Campaign  traffic  was  almost  that  of  Christmas  and  much  higher  than  the  very  successful  ‘Colours’  campaign  

56%  of  Visits  occur  in  the  first  4  days    Site  Visits  

Christmas  =  Nov  20  to  Dec  30,  2009  Colours  =  Aug  7  –  Sep  16,  2009  

§  Looking  only  at  campaign  incep<on,  it  did  drive  higher  daily  average  traffic;  34k  to  32k  respec<vely  

September  2010   ©  Datalicious  Pty  Ltd   41  

Email  me  cbartens@datalicious.com  

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