>	
  Big	
  data	
  in	
  marke.ng	
  <	
  
What	
  the	
  heck?	
  What	
  does	
  it	
  all	
  
mean	
  and	
  how	
  do...
>	
  Using	
  data	
  to	
  widen	
  the	
  funnel	
  
Media	
  A:ribu.on	
  &	
  Modeling	
  
Maximise	
  reach,	
  aware...
>	
  Clients	
  across	
  all	
  industries	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   3	
  
>	
  Wikipedia:	
  Big	
  data	
  
In	
  informaAon	
  technology,	
  big	
  data	
  consists	
  of	
  
datasets	
  that	
...
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   5	
  
Big	
  data	
  =	
  Bo:lenecks	
  
>	
  Big	
  data	
  analy.cs	
  bo:lenecks	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   6	
  
Fast	
  laptops	
...
>	
  Power	
  vs.	
  distributed	
  compu.ng	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   7	
  
Adding	
  more	...
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   8	
  
Big	
  data	
  =	
  Structure?	
  
>	
  Does	
  big	
  data	
  need	
  structure?	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   9	
  
Volume,	
  ve...
>	
  Big	
  data	
  s.ll	
  needs	
  structure	
  	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   10	
  
Volume,	...
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   11	
  
Big	
  data	
  =	
  Hype?	
  
>	
  Importance	
  of	
  research	
  experience	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   12	
  
The	
  cons...
Offer	
  
Issue	
  
Offer	
  
>	
  Design	
  and	
  test	
  experiences	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	...
>	
  The	
  consumer	
  data	
  journey	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   14	
  
To	
  reten.on	
  m...
Transac.onal	
  data	
  
>	
  Combining	
  data	
  sources	
  is	
  key	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Lt...
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...
Customer	
  data	
  exposed	
  in	
  page	
  or	
  URL	
  on	
  login	
  and	
  logout	
  	
  
	
  
CustomerID=12345&	
  
...
hTp://www.acme.com/email-­‐landing-­‐page.html?	
  
	
  
CampaignID=12345&	
  
CustomerID=12345&	
  
Demographics=M|25&	
 ...
acme.com/chris.anbartens	
  redirects	
  to	
  amp.com.au?	
  
	
  
CampaignID=12345&	
  
CustomerID=12345&	
  
Demographi...
>	
  Combine	
  data	
  across	
  devices	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   25	
  
Mobile	
   Home	
...
>	
  Indirect	
  combina.on	
  of	
  data	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   26	
  
Social	
  
IDs	
 ...
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   29	
  
Contact	
  us	
  
cbartens@datalicious.com	
  
	
  
Learn	
  mo...
Smart	
  data	
  driven	
  marke.ng	
  
June	
  2013	
   ©	
  Datalicious	
  Pty	
  Ltd	
   30	
  
201306 aimia big data beyond the hype v1
201306 aimia big data beyond the hype v1
201306 aimia big data beyond the hype v1
201306 aimia big data beyond the hype v1
Upcoming SlideShare
Loading in...5
×

201306 aimia big data beyond the hype v1

836

Published on

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.

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
836
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
9
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

201306 aimia big data beyond the hype v1

  1. 1. >  Big  data  in  marke.ng  <   What  the  heck?  What  does  it  all   mean  and  how  does  it  help  me?  
  2. 2. >  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  
  3. 3. >  Clients  across  all  industries   June  2013   ©  Datalicious  Pty  Ltd   3  
  4. 4. >  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  
  5. 5. June  2013   ©  Datalicious  Pty  Ltd   5   Big  data  =  Bo:lenecks  
  6. 6. >  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  
  7. 7. >  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’.    
  8. 8. June  2013   ©  Datalicious  Pty  Ltd   8   Big  data  =  Structure?  
  9. 9. >  Does  big  data  need  structure?   June  2013   ©  Datalicious  Pty  Ltd   9   Volume,  velocity,  variety,  sexy   Structure,  maintenance,  boring  
  10. 10. >  Big  data  s.ll  needs  structure     June  2013   ©  Datalicious  Pty  Ltd   10   Volume,  velocity,  variety,  sexy   Structure,  maintenance,  boring  
  11. 11. June  2013   ©  Datalicious  Pty  Ltd   11   Big  data  =  Hype?  
  12. 12. >  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    
  13. 13. 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  
  14. 14. >  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  
  15. 15. 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  
  16. 16. June  2013   ©  Datalicious  Pty  Ltd   16   Example:  Phone  call  data  
  17. 17. June  2013   ©  Datalicious  Pty  Ltd   17   Example:  Website  data  
  18. 18. June  2013   ©  Datalicious  Pty  Ltd   18   Example:  Social  media  data  
  19. 19. >  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  
  20. 20. 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  
  21. 21. 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  
  22. 22. 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  
  23. 23. >  Combine  data  across  devices   June  2013   ©  Datalicious  Pty  Ltd   25   Mobile   Home   Work   Tablet   Media   Etc  
  24. 24. >  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  
  25. 25. June  2013   ©  Datalicious  Pty  Ltd   29   Contact  us   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twi:er.com/datalicious    
  26. 26. Smart  data  driven  marke.ng   June  2013   ©  Datalicious  Pty  Ltd   30  
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×