201306 aimia big data beyond the hype v1
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201306 aimia big data beyond the hype v1

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The AIMIA Big data event took place on the 25th of June and it addressed the issue of big data hype. Here are some points to take away from the event.

The AIMIA Big data event took place on the 25th of June and it addressed the issue of big data hype. Here are some points to take away from the event.

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201306 aimia big data beyond the hype v1 201306 aimia big data beyond the hype v1 Presentation Transcript

  • >  Big  data  in  marke.ng  <   What  the  heck?  What  does  it  all   mean  and  how  does  it  help  me?  
  • >  Using  data  to  widen  the  funnel   Media  A:ribu.on  &  Modeling   Maximise  reach,  awareness  &  increase  ROI   Tes.ng  &  Op.misa.on   Remove  barriers,  drive  sales   Boos.ng  ROMI   Targe.ng  &  Merchandising   Improve  engagement,  boost  loyalty   “Turning  data  into  ac.onable  insights  to  widen  the  conversion  funnel”   June  2013   ©  Datalicious  Pty  Ltd   2  
  • >  Clients  across  all  industries   June  2013   ©  Datalicious  Pty  Ltd   3  
  • >  Wikipedia:  Big  data   In  informaAon  technology,  big  data  consists  of   datasets  that  grow  so  large  that  they  become   awkward  to  work  with  using  on-­‐hand  database   management  tools.  DifficulAes  include  capture,   storage,  search,  sharing,  analyAcs,  and  visualizing.       Big  data  are  high  volume,  high  velocity,  and/or   high  variety  informa.on  assets  that  require  new   forms  of  processing  to  enable  enhanced  decision   making,  insight  discovery  and  process   opAmizaAon.   June  2013   ©  Datalicious  Pty  Ltd   4  
  • June  2013   ©  Datalicious  Pty  Ltd   5   Big  data  =  Bo:lenecks  
  • >  Big  data  analy.cs  bo:lenecks   June  2013   ©  Datalicious  Pty  Ltd   6   Fast  laptops  now  have  up  to  8GB   of  RAM,  that  means  you  can   compute  up  to  6GB  of  raw  data   very  fast  in  memory  thus  bypassing   the  biggest  boTleneck:  I/O  
  • >  Power  vs.  distributed  compu.ng   June  2013   ©  Datalicious  Pty  Ltd   7   Adding  more  supercomputers  is   difficult  as  they  are  complex  and   expensive  but  adding  machines  to   a  distributed  compuAng  network     is  fairly  cheap  and  ‘easy’.    
  • June  2013   ©  Datalicious  Pty  Ltd   8   Big  data  =  Structure?  
  • >  Does  big  data  need  structure?   June  2013   ©  Datalicious  Pty  Ltd   9   Volume,  velocity,  variety,  sexy   Structure,  maintenance,  boring  
  • >  Big  data  s.ll  needs  structure     June  2013   ©  Datalicious  Pty  Ltd   10   Volume,  velocity,  variety,  sexy   Structure,  maintenance,  boring  
  • June  2013   ©  Datalicious  Pty  Ltd   11   Big  data  =  Hype?  
  • >  Importance  of  research  experience   June  2013   ©  Datalicious  Pty  Ltd   12   The  consumer  decision  process  is  changing  from  linear  to  circular.   Considera.on     set  now  grows   during  (online)   research  phase   which  increases   importance  of   user  experience   during  that  phase   (Online)  Research    
  • Offer   Issue   Offer   >  Design  and  test  experiences   June  2013   ©  Datalicious  Pty  Ltd   13   Email   Live  chat   Phone  call   Phone  call   Le:er   Email   Issue   All  customers   Segment  A,  B,  C     Segment  D,  E   Influencers   High  valu   Display   Postcard   Display   FAQs  
  • >  The  consumer  data  journey   June  2013   ©  Datalicious  Pty  Ltd   14   To  reten.on  messages  To  transac.onal  data   From  suspect  to   To  customer   From  behavioural  data   From  awareness  messages   Time  Time   prospect  
  • Transac.onal  data   >  Combining  data  sources  is  key   June  2013   ©  Datalicious  Pty  Ltd   15   3rd  party  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Behavioural  data  
  • June  2013   ©  Datalicious  Pty  Ltd   16   Example:  Phone  call  data  
  • June  2013   ©  Datalicious  Pty  Ltd   17   Example:  Website  data  
  • June  2013   ©  Datalicious  Pty  Ltd   18   Example:  Social  media  data  
  • >  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  idenAficaAon  through  Cookies   June  2013   21  ©  Datalicious  Pty  Ltd  
  • Customer  data  exposed  in  page  or  URL  on  login  and  logout       CustomerID=12345&   Demographics=M|25&   CustomerSegment=A1&   CustomerValue=High&   ProductHistory=A6&   NextProduct=A7&   ChurnRisk=High&   [...]   >  Registra.on  and  login  pages   June  2013   ©  Datalicious  Pty  Ltd   22  
  • hTp://www.acme.com/email-­‐landing-­‐page.html?     CampaignID=12345&   CustomerID=12345&   Demographics=M|25&   CustomerSegment=A1&   CustomerValue=High&   ProductHistory=A6&   NextProduct=A7&   ChurnRisk=High&   [...]   >  Email  click-­‐through  iden.fica.on   June  2013   ©  Datalicious  Pty  Ltd   23  
  • acme.com/chris.anbartens  redirects  to  amp.com.au?     CampaignID=12345&   CustomerID=12345&   Demographics=M|25&   CustomerSegment=A1&   CustomerValue=High&   ProductHistory=A6&   NextProduct=A7&   ChurnRisk=High&   [...]   >  Personalised  URLs  for  direct  mail   June  2013   ©  Datalicious  Pty  Ltd   24   Catch  on   acme.com   404  error  page  
  • >  Combine  data  across  devices   June  2013   ©  Datalicious  Pty  Ltd   25   Mobile   Home   Work   Tablet   Media   Etc  
  • >  Indirect  combina.on  of  data   June  2013   ©  Datalicious  Pty  Ltd   26   Social   IDs   Client     ID   Web   data   Address   Geo   segment   Roy     Morgan   Etc   MOSAIC   Hitwise   Social   data  
  • June  2013   ©  Datalicious  Pty  Ltd   29   Contact  us   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twi:er.com/datalicious    
  • Smart  data  driven  marke.ng   June  2013   ©  Datalicious  Pty  Ltd   30