ROCKET FUEL
Artificial Inteligence, Big Data
& RTB Copenhagen
WHO AM I? – VP/Managing Director EMEA
DOMINIC TRIGG
•  VP Global sales & Marketing, TradeDoubler
•  Ad Operations Dir, Yah...
WHAT IS ROCKET FUEL?
TECHNOLOGY COMPANYARTIFICIAL INTELLIGENCEBIG DATADIGITAL ADVERTISINGTRUE IMPACT
EXPLOSIVE GROWTH PATH
GLOBAL	
  SCALE	
  &	
  REACH:	
  
20	
  OFFICES	
  WORLDWIDE	
  
670	
  EMPLOYEES	
  
2009 2010 201...
Programma>c	
  
buying	
  
Bidding	
  on	
  
individual	
  ad	
  
impressions	
  
In	
  real	
  >me	
  
For	
  the	
  
opp...
Effec>veness	
  
Buy	
  only	
  
consumers	
  
that	
  you	
  
want	
  
Only	
  in	
  
contexts	
  that	
  
generate	
  
im...
Branding	
  
Direct	
  
Response	
  
Loyalty	
  
Marke>ng	
  
•  Reach	
  &	
  Frequency	
  
•  Brand	
  equity	
  liX	
  ...
A	
  pla+orm	
  that	
  
enables	
  1:1	
  
Marke9ng	
  @	
  Scale	
  
Context	
  
3rd	
  
party	
  
data	
  
1st	
  
part...
The	
  Evolu9on	
  of	
  Digital	
  Ad	
  buying	
  
Big	
  sites	
  big	
  reach!	
  
Where	
  does	
  UU	
  
come	
  fro...
HOUSEHOLD INCOMEAGE
Let’s look at Optimisation
Possible	
  Combina9ons	
  
GENDER	
  	
  
(7	
  Buckets)	
   (8	
  Buckets...
CITIES
TRADITIONAL	
  OPTIMIZATION	
  
There	
  are	
  85	
  ci9es	
  in	
  Sweden.	
  	
  
When	
  combined	
  with	
  ou...
=	
  504,216,244,224,000,000,000,000,000	
  Segments	
  
Data segments on an Exchange
an opportunity + a problem
ATribute	...
SOLVING THE KNOWLEDGE PARADOX
Data
Ability
to Make
Decisions
Ideal
Actual
Opportunity
INTRODUCING A.I. TO THE MIX
=
500k queries
per second
8.64 million Analysts
(5,000 decisions per day)
à	
  AI	
  +	
  BIG	
  DATA	
  
What is AI?
ARTIFICIAL
INTELLIGENCE
=
AUTONOMOUS
LEARNING
ARTIFICIAL
INTELLIGENCE
=
ART
THAT
LEARNS
ARTIFICIAL
INTELLIGENCE
=
THE BRAIN
RECREATED
ARTIFICIAL
INTELLIGENCE
=
AUTONOMOUS
LEARNING
5 YEARS LATER
&	
  
What	
  do	
  we	
  
mean	
  by	
  	
  
BIG	
  DATA?	
  
“From	
  the	
  dawn	
  of	
  civiliza4on	
  un4l	
  2003,	...
ACROSS
MULTIPLE
INDUSTRIES
“The	
  prac4cal	
  conclusion	
  is	
  that	
  we	
  should	
  
turn	
  many	
  of	
  our	
  decisions,	
  predic4ons,	
 ...
Facebook likes per year 1 Trillion
Google searches per year 2.2 Trillion
Est. sand grains in West Texas desert 2.8 Trillio...
THE
MARKETER’S
DILEMMA
“There	
  is	
  no	
  point	
  in	
  
collec.ng	
  and	
  storing	
  
all	
  this	
  data	
  if	
  ...
The Past
The	
  Future	
  	
  
In	
  addi>on	
  to	
  being	
  able	
  to	
  process	
  more	
  data	
  in	
  a	
  smaller	
  >me	
...
Making this stuff matter
àSUCCESS for Customers by combining
BIG DATA with ARTIFICIAL INTELLIGENCE
IN THE AGE OF
TARGETING…OPTIMISATION…
DEMOGRAPHIC A
BEHAVIOURAL
SEGMENT B
CONTENT
CATEGORY C
AI …
A DAY IN THE LIFE OF THE ADDRESSABLE CONSUMER
7:35 AM 9:20 AM 11:30 AM 12:05 PM
2:15 PM5:30 PM11:00 PM 8:00 PM
CONTEXT
IS ...
PURCHASE	
  INTENT	
  
AWARENESS	
  
FAVORABILITY	
  
CONSIDERATION	
  
CUSTOMERS	
  
LOYALISTS	
  
Full Funnel + Cross-Ch...
AUTOMATED SELF-LEARNING
Age/Gender	
  
Occupa>on	
  
Income	
  Ethnicity	
  
Purchase	
  Intent	
  
Online	
  
Purchases	
...
[	
  	
  	
  +	
  	
  	
  ]	
  
FLOW OF AVAILABLE
IMPRESSIONS ON EXCHANGES
IMPRESSION PROPENSITY SCORE
Likelihood to drive...
INSIGHTS	
  INTERFACE	
  
Giving access to campaign
insights in real-time, including:
» Personal login details
» Supportin...
Agency/Client	
  
Then	
  adding	
  a	
  real	
  world	
  Support	
  Structure	
  	
  
Dedicated,	
  Named	
  Account	
  M...
Autonomous Learning: Unintuitive Results
ONE PIECE OF THE BRAIN: MODEL COEFFICIENTS FOR LUXURY CAR LEADS
A	
  CLOSING	
  THOUGHT	
  
39	
  
Further Suggested Reading
RTB Update 4 - Dominic Trigg, RocketFuel
RTB Update 4 - Dominic Trigg, RocketFuel
RTB Update 4 - Dominic Trigg, RocketFuel
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RTB Update 4 - Dominic Trigg, RocketFuel

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RTB Update 4 - Dominic Trigg, RocketFuel

  1. 1. ROCKET FUEL Artificial Inteligence, Big Data & RTB Copenhagen
  2. 2. WHO AM I? – VP/Managing Director EMEA DOMINIC TRIGG •  VP Global sales & Marketing, TradeDoubler •  Ad Operations Dir, Yahoo •  Ad Director, Microsoft MSN •  Advertising head BT Internet •  6 years Press Advertising 18 YEARS INTERNET ADVERTISING
  3. 3. WHAT IS ROCKET FUEL? TECHNOLOGY COMPANYARTIFICIAL INTELLIGENCEBIG DATADIGITAL ADVERTISINGTRUE IMPACT
  4. 4. EXPLOSIVE GROWTH PATH GLOBAL  SCALE  &  REACH:   20  OFFICES  WORLDWIDE   670  EMPLOYEES   2009 2010 2011 2012 REVENUE   $2.3M $16M $45M $107M 2013 $239M
  5. 5. Programma>c   buying   Bidding  on   individual  ad   impressions   In  real  >me   For  the   opportunity…   To  show  one   specific  ad   To  one  specific   consumer   In  one  specific   context   What  is  RTB  Programma>c  Buying?     5  key  Ques>ons:  
  6. 6. Effec>veness   Buy  only   consumers   that  you   want   Only  in   contexts  that   generate   impact   And  scale  up   massively   Why  is  Programma>c  so  effec>ve?   5  key  Ques>ons:  
  7. 7. Branding   Direct   Response   Loyalty   Marke>ng   •  Reach  &  Frequency   •  Brand  equity  liX   •  Purchase  intent   •  Prospec>ng   •  Retarge>ng   •  Offline  impact   •  Up  selling   •  Cross  selling   •  Referrals   When  can   Programma>c   be  used?   5  key  Ques>ons:  
  8. 8. A  pla+orm  that   enables  1:1   Marke9ng  @  Scale   Context   3rd   party   data   1st   party   data   How  do  I  reach  my   tailored  audience   with  RTB   Programma>c?   5  key  Ques>ons:  
  9. 9. The  Evolu9on  of  Digital  Ad  buying   Big  sites  big  reach!   Where  does  UU   come  from    What  do  we  know?   What  is  the  Objec>ve?   Results  set  the  algorithm   and  they  must  adapt       AGE OF DELIVERY AGE OF TARGETING AGE OF OPTIMISATION Ad  Effec>veness  
  10. 10. HOUSEHOLD INCOMEAGE Let’s look at Optimisation Possible  Combina9ons   GENDER     (7  Buckets)   (8  Buckets)   x x (2  Buckets)   112 Combinations=
  11. 11. CITIES TRADITIONAL  OPTIMIZATION   There  are  85  ci9es  in  Sweden.     When  combined  with  our  other  metrics  and  available  channels,  that’s  38,080  possible   combina>ons.  (112  x  85  x  4)   38,080 Combinations= (85  Buckets)   x112Combinations CHANNELS (4  Plagorms)   x
  12. 12. =  504,216,244,224,000,000,000,000,000  Segments   Data segments on an Exchange an opportunity + a problem ATribute   #  of  Segments   Age   18   Gender   2   HHI   16   Geo     43,000   Lifestyles   100   Interests   800   ATribute   #  of  Segments   Psychographics   42   Past  Purchases   990   Age  of  Children   17   Contextual   100,000   Time  of  Day   720   Ad  Size   5   =  145,710  Segments   A  Combina9onal  Explosion!  
  13. 13. SOLVING THE KNOWLEDGE PARADOX Data Ability to Make Decisions Ideal Actual Opportunity
  14. 14. INTRODUCING A.I. TO THE MIX = 500k queries per second 8.64 million Analysts (5,000 decisions per day)
  15. 15. à  AI  +  BIG  DATA  
  16. 16. What is AI? ARTIFICIAL INTELLIGENCE = AUTONOMOUS LEARNING
  17. 17. ARTIFICIAL INTELLIGENCE = ART THAT LEARNS
  18. 18. ARTIFICIAL INTELLIGENCE = THE BRAIN RECREATED
  19. 19. ARTIFICIAL INTELLIGENCE = AUTONOMOUS LEARNING 5 YEARS LATER
  20. 20. &   What  do  we   mean  by     BIG  DATA?   “From  the  dawn  of  civiliza4on  un4l  2003,  humankind   generated  five  exabytes  of  data.    Now  we  produce  five   exabytes  every  two  days…  and  the  pace  is  accelera4ng.”       -­‐-­‐  Eric  Schmidt,  Chairman,  Google  
  21. 21. ACROSS MULTIPLE INDUSTRIES
  22. 22. “The  prac4cal  conclusion  is  that  we  should   turn  many  of  our  decisions,  predic4ons,   diagnoses,  and  judgments—both  the  trivial   and  the  consequen4al—over  to  the   algorithms.  There’s  just  no  controversy  any   more  about  whether  doing  so  will  give  us   beKer  results.”           Andrew  McAfee   Principal  Research  Scien4st,  MIT  Sloan   December,  2013   Big  Data’s  Biggest  Challenge?  Convincing   People  NOT  to  Trust  Their  Judgment  
  23. 23. Facebook likes per year 1 Trillion Google searches per year 2.2 Trillion Est. sand grains in West Texas desert 2.8 Trillion Rocket Fuel consumer data points 3.5 Quadrillion THE EXPLOSION OF CONSUMER DATA
  24. 24. THE MARKETER’S DILEMMA “There  is  no  point  in   collec.ng  and  storing   all  this  data  if  the   algorithms  are  not  able   to  find  useful  pa7erns   and  insights  in  the   data….”  
  25. 25. The Past
  26. 26. The  Future     In  addi>on  to  being  able  to  process  more  data  in  a  smaller  >me  frame,  AI-­‐powered  solu>ons  can  quickly  iden>fy   which  data  points  are  significant  to  performance,  and  eliminate  the  ones  that  don’t  maker.  
  27. 27. Making this stuff matter àSUCCESS for Customers by combining BIG DATA with ARTIFICIAL INTELLIGENCE
  28. 28. IN THE AGE OF TARGETING…OPTIMISATION… DEMOGRAPHIC A BEHAVIOURAL SEGMENT B CONTENT CATEGORY C AI …
  29. 29. A DAY IN THE LIFE OF THE ADDRESSABLE CONSUMER 7:35 AM 9:20 AM 11:30 AM 12:05 PM 2:15 PM5:30 PM11:00 PM 8:00 PM CONTEXT IS CRITICAL
  30. 30. PURCHASE  INTENT   AWARENESS   FAVORABILITY   CONSIDERATION   CUSTOMERS   LOYALISTS   Full Funnel + Cross-Channel Campaign What  makers  is  the  UU   and  their  rela>onship  to   the  campaign  
  31. 31. AUTOMATED SELF-LEARNING Age/Gender   Occupa>on   Income  Ethnicity   Purchase  Intent   Online   Purchases   Offline   Purchases   Browsing   Behavior   Site  Ac>ons   Zip  Code  City/DMA   Search   Sites   Search   Categories   Recency   Search   Keywords   Web  Site/Page   Referral  URL   Site   Category   Bizographics   Social   Interests   Lifestyle   ROCKET FUEL x   +   -­‐   -­‐7   +17   X   -­‐2   +8   +14   X   -­‐9   -­‐13   -­‐12   X   +19   +13   X   +11   X   X   X   +25   +6   X   -­‐7   +17   -­‐2   +28   X   +11   X   X   -­‐9   +14   +17   +19   +8   +11   X   X   +17   -­‐23   +6   X   +17   -­‐7   X   -­‐2   -­‐13   -­‐12   X   +13   +6   X   X   X   -­‐9   X   +17   X   +19   +8   +14   +18   -­‐23   +17   -­‐12   +11   -­‐9   +8   +14   X   +11   -­‐13   -­‐12   +11   X   X   -­‐7   +17   +8   +18  X   +11   X   -­‐12  -­‐10   +6   +14   X   +8   +11   -­‐10  +13   +28   +6   +13   +19   X   +11   -­‐10   +13   -­‐12   +17   X   -­‐7   +8   X   60   11MM+   Features   Posi9ve  Lii   Marginal  Lii   Nega9ve  Lii   +8  +13  +11  -­‐9  +11  
  32. 32. [      +      ]   FLOW OF AVAILABLE IMPRESSIONS ON EXCHANGES IMPRESSION PROPENSITY SCORE Likelihood to drive desired objective -2.42 1.25 2.11 1.26 -2.78 1.256 -1.809 -2.42 1.25 2.11 -1.26 2.78 0.586 -2.009 1.25 2.11 -1.26 2.78 1.56 0.00 IDENTIFYING  MOMENTS  OF  INFLUENCE   +  Applying  learnings  at  the  impression  level   [      +      ]  
  33. 33. INSIGHTS  INTERFACE   Giving access to campaign insights in real-time, including: » Personal login details » Supporting multiple client campaigns » Quick overview across campaigns » All key metrics and trends at a glance » Insights updated every 10 minutes » Insights across 1000’s of data points » Compare two metrics interactively » Live calculation of top customers
  34. 34. Agency/Client   Then  adding  a  real  world  Support  Structure     Dedicated,  Named  Account  Manager   Analysts  Team  Opera9ons  Team   Account   Mgmt   Engineering  and  Research   Corp   Mgmt   Day  to  Day   Campaign   Management   Performance   Review   Escala>on   Support  Structure  Availability   24/7/365  
  35. 35. Autonomous Learning: Unintuitive Results ONE PIECE OF THE BRAIN: MODEL COEFFICIENTS FOR LUXURY CAR LEADS
  36. 36. A  CLOSING  THOUGHT  
  37. 37. 39  
  38. 38. Further Suggested Reading

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