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Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   1	
  
Tag	
  Management:	
  
The	
  Next	
  Steps	
  
Why	
  Tag	
  
Management?	
  
site-­‐speed	
  
markeEng	
  
velocity	
  
independence	
  
Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   2	
  
What	
  are	
  the	
  
possibili9es?	
  manage	
  tags	
  
assure	
  
data-­‐quality	
  
cookie	
  
consent	
  
The	
  Swiss	
  Knife	
  of	
  
your	
  website	
  
•  Manage	
  tags	
  (of	
  course)	
  
•  Collect	
  Data	
  
•  OpEmize	
  
•  Personalize	
  
Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   3	
  
A/B-­‐tests	
  with	
  Tag	
  
Management	
  
•  Simple	
  A/B-­‐tests	
  
•  Analyze	
  in	
  web-­‐analyEcs	
  
•  Tests	
  based	
  on	
  random	
  groups	
  
Missing	
  link?	
  
Data!	
  
•  Collect	
  data	
  through	
  the	
  TMS	
  
•  Use	
  data	
  instead	
  of	
  staEc	
  content	
  
(e.g.	
  “last	
  booked	
  10	
  seconds	
  ago”)	
  
•  OpEmize	
  the	
  site	
  for	
  a	
  general	
  audience	
  
Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   4	
  
Case:	
  
Vrij	
  Uit	
  
•  Collect	
  booking	
  data	
  
•  Display	
  persuasion	
  elements	
  
•  Conversion	
  rate:	
  up	
  by	
  7%	
  
Personaliza9on	
  
Through	
  TMS	
  
•  Next	
  step	
  aZer	
  opEmizaEon	
  
•  Visitor	
  segmentaEon	
  
•  Machine	
  learning	
  to	
  determine	
  the	
  
right	
  content	
  for	
  each	
  visitor	
  
(e.g.	
  “This	
  fits	
  your	
  taste”)	
  
Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   5	
  
Case:	
  
Neckermann	
  
•  Calculate	
  personalized	
  
recommendaEons	
  
•  Use	
  current	
  context	
  (product)	
  +	
  
user	
  profile	
  (history)	
  
Case:	
  
CRM	
  Data	
  Sync	
  
- Online behavior data
- User identifiers
- Owned products
- Customer status
Profile
Time	
  
Anonymous
Visitor
Identification
(e.g. email link)
CRM	
  
System	
  
+	
  
Prospect	
  
Message	
  
Customer	
  
Specific	
  
Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   6	
  
Case:	
  
Offline	
  Data	
  
Profile •  Views & Recommendations
•  Channels used
•  Feedback by agent
Time	
  
Visitor
Online behavior data
•  Personalized	
  recommendaEons	
  
•  Combined	
  with	
  dynamic	
  
opEmizaEon	
  elements	
  
•  12%	
  increased	
  conversion	
  rate	
  
Case:	
  
Thomas	
  Cook	
  
Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   7	
  
Behind	
  The	
  
Scences	
  
Tag	
  Management	
  
Data	
  Pla^orm	
  
website	
  
Raw	
  
Data	
  
Learning	
  Algorithms	
  IntegraEon	
  Center	
  
User	
  
Profiles	
  
Others	
  
Back-­‐end	
  
Systems	
  
Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   8	
  
The	
  future	
  of	
  
Tag	
  Management	
  
•  A	
  Tag	
  Management	
  System	
  is	
  
much	
  more	
  than	
  a	
  simple	
  
container	
  for	
  tags	
  
•  Valuable	
  data	
  can	
  be	
  at	
  your	
  
fingerEps	
  
•  Dynamic	
  opEmizaEon	
  and	
  
personalizaEon	
  are	
  the	
  next	
  
A/B-­‐tesEng	
  
Key	
  takeaways	
  
•  The	
  Tag	
  Management	
  System	
  	
  is	
  
a	
  mulE-­‐tool	
  for	
  the	
  website	
  
•  TMS	
  performance	
  is	
  essenEal	
  for	
  
personalizaEon	
  
•  Work	
  towards	
  a	
  direct	
  
integraEon	
  between	
  tags	
  and	
  
data	
  
Tomas	
  Salfischberger	
  -­‐	
  #DDTT	
  Tag	
  
Management	
  
May	
  6th	
  2014	
  
Digital	
  Data	
  Tips	
  Tuesday	
  -­‐	
  Amsterdam	
  
hAp://digitaldata.Eps	
   9	
  

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Digital Data Tips Tuesday #1 - Tag Management: Tomas Salfischberger - Relay42

  • 1. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   1   Tag  Management:   The  Next  Steps   Why  Tag   Management?   site-­‐speed   markeEng   velocity   independence  
  • 2. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   2   What  are  the   possibili9es?  manage  tags   assure   data-­‐quality   cookie   consent   The  Swiss  Knife  of   your  website   •  Manage  tags  (of  course)   •  Collect  Data   •  OpEmize   •  Personalize  
  • 3. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   3   A/B-­‐tests  with  Tag   Management   •  Simple  A/B-­‐tests   •  Analyze  in  web-­‐analyEcs   •  Tests  based  on  random  groups   Missing  link?   Data!   •  Collect  data  through  the  TMS   •  Use  data  instead  of  staEc  content   (e.g.  “last  booked  10  seconds  ago”)   •  OpEmize  the  site  for  a  general  audience  
  • 4. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   4   Case:   Vrij  Uit   •  Collect  booking  data   •  Display  persuasion  elements   •  Conversion  rate:  up  by  7%   Personaliza9on   Through  TMS   •  Next  step  aZer  opEmizaEon   •  Visitor  segmentaEon   •  Machine  learning  to  determine  the   right  content  for  each  visitor   (e.g.  “This  fits  your  taste”)  
  • 5. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   5   Case:   Neckermann   •  Calculate  personalized   recommendaEons   •  Use  current  context  (product)  +   user  profile  (history)   Case:   CRM  Data  Sync   - Online behavior data - User identifiers - Owned products - Customer status Profile Time   Anonymous Visitor Identification (e.g. email link) CRM   System   +   Prospect   Message   Customer   Specific  
  • 6. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   6   Case:   Offline  Data   Profile •  Views & Recommendations •  Channels used •  Feedback by agent Time   Visitor Online behavior data •  Personalized  recommendaEons   •  Combined  with  dynamic   opEmizaEon  elements   •  12%  increased  conversion  rate   Case:   Thomas  Cook  
  • 7. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   7   Behind  The   Scences   Tag  Management   Data  Pla^orm   website   Raw   Data   Learning  Algorithms  IntegraEon  Center   User   Profiles   Others   Back-­‐end   Systems  
  • 8. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   8   The  future  of   Tag  Management   •  A  Tag  Management  System  is   much  more  than  a  simple   container  for  tags   •  Valuable  data  can  be  at  your   fingerEps   •  Dynamic  opEmizaEon  and   personalizaEon  are  the  next   A/B-­‐tesEng   Key  takeaways   •  The  Tag  Management  System    is   a  mulE-­‐tool  for  the  website   •  TMS  performance  is  essenEal  for   personalizaEon   •  Work  towards  a  direct   integraEon  between  tags  and   data  
  • 9. Tomas  Salfischberger  -­‐  #DDTT  Tag   Management   May  6th  2014   Digital  Data  Tips  Tuesday  -­‐  Amsterdam   hAp://digitaldata.Eps   9