Why Data is Sexy

Post on 29-Nov-2014

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description

The presentation discusses the four main data categories in correlation with marketing and analytics strategies.

Transcript of Why Data is Sexy

[  Why  data  is  sexy  ]  No  really,  it  is!  

Campaign  data  

Customer  data  

Compe5tor  data  

Consumer  data  

[  Main  data  categories  ]  

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Campaign  data  

Awareness   Interest   Desire   Ac5on   Sa5sfac5on  

Online  surveys,  site  polls  

Web  analy5cs  

[  Defining  analy:cs  strategy  ]  

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Social  media  

Search,  display  ads  

Social  media  

[  Single  source  of  truth  ]  

data  

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[  De-­‐duplica:on  across  channels  ]  

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Banner    Ads  

Email    Blast  

Paid    Search  

Organic  Search  

$  Bid    Mgmt  

Ad    Server  

Email  PlaGorm  

Google  Analy:cs  

$  

$  

$  

Omniture  PlaGorm  

$  

$  

$  

[  De-­‐duplica:on  across  channels  ]  

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[  Success  aIribu: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  

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[  Campaign  stacking  by  channel  ]  

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Chart  shows  percentage  of  channel  touch  points  that  lead  to  a  conversion.  

Neither  first    nor  last-­‐click  measurement  would  provide  true  picture    

[  Search  call  to  ac:on  ]  

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[  Analyse  channel  overlap  ]  

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Total  Ac:ons:  2,023  

Display  Media  1669   (61.2%)  

$125  -­‐  $200  CPA  

Paid  Search  301  /  11.1%  

$200  -­‐  $500  CPA  

189  (62%)  

112  (38%)  

Straight  to  Site    1208  (44.3%)  

621  (52%)  

587  (48%)  

DM  &  EDMs    

$200  -­‐  $400  CTA  

Radio  &  

Other  ATL  

Organic  Search  493  (18%)  

179  (37%)  

314  (63%)  

Partners  

Call  Centre  

[  Cross-­‐channel  impact  ]  

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[  Email  iden:fica:on  points  ]  

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Adver5sing  Campaign  

Cookie  ID  

Vodafone.com.au  Research  

Credit  Check  Fulfilment  

Phone  Conversion  

Retail  Conversion  

Online  Conversion  

Credit  Check  Fulfilment  

Credit  Check  Fulfilment  

Vodafone.com.au  Research  

Vodafone.com.au  Research  

Online  Order  Confirma5on  

Online  Receipt  Confirma5on  

Online  Receipt  Confirma5on  

Online  Receipt  Confirma5on  

@  

@  

@  

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Customer  data  

14  

[  Store  locator  searches  ]  

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[  Reasons  for  store  drop-­‐out  ]  

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About  a  third  of  those  dropping  out  intend  to  come  back  again.  1  in  5  will  go  away  and  think    more  about  their  purchase.  Only  10%  have  inten5ons  purely  to  use  the  store  for  research.  

[  Targe:ng  framework  ]  

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-­‐12   -­‐11   -­‐10   -­‐9   -­‐8   -­‐7   -­‐6   -­‐5   -­‐4   -­‐3   -­‐2   -­‐1   0   1   2   3   4   5   6   7   8   9   10   11   12  

Weeks  

Reten5on  

Considera5on  

Customer  Profile  

Prospect  

Visitor  Behaviour  

Customer  

[  Prospect  targe:ng  parameters  ]  

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[  Affinity  targe:ng  in  ac:on  ]  Different  types  of    visitors  respond  to    different  ads.  By  using  category  affinity  targe5ng,    response  rates  are    liced  significantly    across  products.  

Message  Click-­‐Through  Rate  By  Category  Affinity  

Postpay   Prepay   Broadb.   Business  

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

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On-­‐site    segments  

Off-­‐site  segments  

[  Matching  segments  are  key  ]  

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On  and  off-­‐site  targe5ng  plaeorms  should  use    iden5cal  triggers  to  sort  visitors  into  segments  

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[  Combining  data  sets  ]  

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one-­‐off  collec5on  of  demographical  data    age,  gender,  address,  etc  customer  lifecycle  metrics  and  key  dates  profitability,  expira:on,  etc  predic5ve  models  based  on  data  mining  

propensity  to  buy,  churn,  etc  historical  data  from  previous  transac5ons  

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  promo5on  responses  

emails,  internal  search,  etc  

Site  Behaviour  

UPDATED  CONTINUOUSLY  

Omniture    Test&Target  

Google    Analy5cs  

Omniture    DataWarehouse  

Vodafone    Terradata  

Omniture  DataWarehouse  

Eyeblaster  

Omniture    Discover  OnPremise  

Online  behavioural  data  stored    in  Omniture  for    anonymous  prospects  and  known  customers  

Customer  status  and  product  affinity  for  prospects  and  customers  passed  into  Test&Target  as  well  as  Eyeblaster  for  advanced  on  and  off-­‐site  targe5ng  (e.g.  help/support  messages  for  

exis5ng  customers,  acquisi5on  messages  with  customized  plans  for  prospects,  etc)  

Data  on  MyVodafone  registra5on  status  to  iden5fy  non-­‐users  in  order  to  encourage  online  self-­‐service  

Data  on  MyVodafone  self-­‐service  usage  to  iden5fy  users  with    poten5al  issues  in  order  to  increase  customer  sa5sfac5on  

Data  on  campaign  performance  to  iden5fy  most  suitable    message  content  and  5ming  for  each  customer  or  segment  

Data  on  product  preferences  and  research  behaviour  to    customize  product  offering  and  feed  into  churn  modelling  

Customer  data  on  self-­‐service  usage,  campaign  responses  and  product  preferences  are  filtered  against  Terradata  in  Discover  OnDemand,  contact  lists  are  generated  and  customized  messages  delivered  through  the    most  suitable  channels  

[  Where  is  the  money  ]  

[  october  2007  ]  

$  

$  

$  $  

$  

$  

$  

$  

$  

$  

[  datalicious.com.au  ]  

[  Mosaic  ]  

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Compe:tor  data  

[  Brand  search  volume  ]  

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[  Market  share  trends  ]  

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[  Australia  vs.  New  Zealand  ]  

[  september  2007  ]   [  datalicious.com.au  ]  

australia.com  vs.  newzealand.com  

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Consumer  data  

[  Search  term  volumes  ]  

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[  Media  planning  ]  

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[  Leading  lifestyles,  45+  ]  

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Campaign  data  

Customer  data  

Compe5tor  data  

Consumer  data  

[  Main  data  categories  ]  

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