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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