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, Yahoo •  Ad Director, Microsoft MSN •  Advertising head BT Internet •  6 years Press Advertising

18 YEARS INTERNET ADVERTISING

WHAT IS ROCKET FUEL?

TECHNOLOGY COMPANY ARTIFICIAL INTELLIGENCE BIG DATA DIGITAL ADVERTISING TRUE IMPACT

EXPLOSIVE GROWTH PATH

GLOBAL  SCALE  &  REACH:  20  OFFICES  WORLDWIDE  670  EMPLOYEES  

2009 2010 2011 2012 REVENUE  

$2.3M

$16M

$45M

$107M

2013

$239M

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:  

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:  

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:  

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:  

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  Effe

c>vene

ss  

HOUSEHOLD INCOME AGE

Let’s look at Optimisation

Possible  Combina9ons  

GENDER    

(7  Buckets)   (8  Buckets)  

x x (2  Buckets)  

112 Combinations =

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)  

x 112 Combinations

CHANNELS

(4  Plagorms)  

x

=  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!  

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,  humankind  generated  five  exabytes  of  data.    Now  we  produce  five  

exabytes  every  two  days…  and  the  pace  is  accelera4ng.”      -­‐-­‐  Eric  Schmidt,  Chairman,  Google  

ACROSS MULTIPLE

INDUSTRIES

“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  

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

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….”  

The Past

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.  

Making this stuff matter

àSUCCESS 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 PM 5:30 PM 11:00 PM 8:00 PM

CONTEXT IS CRITICAL

PURCHASE  INTENT  

AWARENESS  

FAVORABILITY  

CONSIDERATION  

CUSTOMERS  

LOYALISTS  

Full Funnel + Cross-Channel Campaign

What  makers  is  the  UU  and  their  rela>onship  to  

the  campaign  

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  

[      +      ]  

FLOW OF AVAILABLE IMPRESSIONS ON EXCHANGES

IMPRESSION PROPENSITY SCORE Likelihood to drive desired objective

5.669

-3.7234 1.842165 -1.78964 -1.6782 1.7234 0.809 -2.42 1.25 2.11 1.26

-2.178 2.056 -0.809 -2.42 1.25 2.11 -1.26 2.78 -1.56 1.809 2.42 -1.25 2.11 1.26 2.78 -0.56 2.42 -1.25 -2.11 1.26 -2.78 0.756 0.809 -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  

[      +      ]  

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

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  

Autonomous Learning: Unintuitive Results ONE PIECE OF THE BRAIN: MODEL COEFFICIENTS FOR LUXURY CAR LEADS

A  CLOSING  THOUGHT  

39  

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