1. AI + BIG DATA Rocket Fuel Inc. The Big Data Breakfast

2. AGENDA
9.00 Registration & Networking Breakfast
9.45 Welcome: Dominic Trigg, MD EU, Rocket Fuel
9.50 Vincent Potier, Former MD, Vonage
10.15 George John PhD AI, CEO & Founder, Rocket Fuel
11.00 Q&A Panel (all of the above)
11.15 Summary & Close

3. BIG DATA in 2013 – Is it relevant?
“There is no doubt that 2012 was a big year for big data. …it is digital’s DNA and it
matters a great deal.”
Monty Munford, Technology Columnist, The Telegraph
“It’s no longer about big data, it’s about what you can do with the data. It’s about
the apps that layer on top of data stored, and the insights these apps can provide.”
Leena Rao, Senior Editor, TechCrunch
“Collectively, this data is enabling humanity to sense, measure, understand, and
affect aspects of our existence in ways our ancestors could never have imagined.”
Rick Smolan, Author of ‘The Human Face of Big Data’

4. BIG DATA – A press Hot Potato!

5. » Founded: 2008: Last year we grew from $45m- $107m
» People: 303  126% growth YOY
» Offices: Currently 15 ….
» Data Centers: 5 (US, Europe, Asia)
» Customers: 1,193 ….
» Campaigns: 3,320 ….
» Daily Bid Impressions: 26.237 Billion
» Strategic Alliance with Dentsu in Japan for PerformanceX
152
11
4
26
3
2941
5
2
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6
2
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A few words about Rocket Fuel…
A TECHNOLOGY COMPANY
Making sense of BIG DATA

6. ADVERTISER ROCKET FUEL
145
RTB advertising
supply partners
21.1MM
Websites
26.2Bn
Daily impressions
800MM WW PROFILES
91,999 DEVICES

7. Today’s Data Dilemma
There is so much data in the
current market place…….

8. = 504,216,244,224,000,000,000,000,000 Segments
Data segments on an Exchange
an opportunity + a problem
Attribute # of Segments
Age 18
Gender 2
HHI 16
Geo 43,000
Lifestyles 100
Interests 800
Attribute # 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 Combinational Explosion!

9. So our stance is to let machines weigh in ….
Technology purpose-built to harness big data
computing and simplify marketing complexity

10. PRODUCT EVOLUTION
Transition from tactical Media Vendor to Strategic Solutions Provider
DR Booster ‘09
Core Technology ‘08
Audience Accelerator ’09-’10
Brand Booster ‘10
Social Booster (Facebook) ’11Mobile ‘10
Video ‘10
Vertical Solutions ’11 – ‘12
Rocket Fuel Connect ‘11
Expanding product footprint
to go deeper and broader
Dynamic Creative ‘12
Insights Booster ‘12
Boosted FBX ‘12

11. A TRUSTED PARTNER
Top brands find success with us

12. Introducing our speakers
today…

13. GEORGE JOHN
Geek + Trekkie as a kid…
BS, MS, and PhD Specialising in
Computer Science Stanford, and AI
and statistics…
Won the National Science
Foundation fellowship NASA… on
Mars Rover!
Prior to RF he led groups at IBM,
Epiphany, salesforce.com and Yahoo!
George ?

14. VINCENT POTIER
Vincent used carear to travel world.
LATAM , France Asia and UK
20yrs+ global senior marketing/
commercial, Inc. Lycos Vizzavi,
Vonage
Now own boss advising agencies and
ad networks how to build business.
Vincent changed a EU directive.
Learnt Portuguese in 45 days and
Spanish in 60 .
But..as a Frenchman his musical
taste is quite poor…. film knowledge
is obsessive.
Vincent?

15. Vincent Potier,
Former Managing Director, Vonage

16. Big data: the future of marketing
as you don’t know it
Vincent Potier
Rocket Fuel breakfast,
London, January 22nd 2013

17. First, who am I ?
1999
2005 2012
2001

18. Big data

19. One fact…
• “Every 2 days we
create as much
information as we did
up to 2003” Eric
Schmidt, 2010
• That’s about 5
exabytes of data
• Today the number
would be higher
• Big data is not big, it
is exponential
Library of Alexandria

20. And a few numbers…
230 million tweets a day
100 terabytes of data uploaded daily
294 billion emails sent a day (2011)
Source: IBM study, 2012
4.8 trillion online ad impressions
2.7 zettabytes of data exist in the digital universe
35 zettabytes of data generated
annually by 2020

21. Why big data?
• Digital universe
• Processing, computing power, hardware, servers &
data centres proliferation: Internet world
• User generated digital activity
• Digital advertising
• Ability to store, share and analyse

22. What is big data?
• A collection of data sets
• So large and complex that it becomes impossible to
use traditional data-processing techniques
• Has to be captured, curated, stored, analysed,
processed, shared, visualised
• The result though is amazing: be able to spot trends,
answers, correlations invisible to humans

23. Applications are everywhere
Physics
Biology
Climate change
Health
Demographics
Space

24. Some sectors have a lot to gain
Source: McKinsey

25. And what about marketing?

26. There are obvious applications
RTB
Segments
clustering
Overlays
of data
sources
Social
monitoring
Attribution
modelling
Multi-channel
budget
optimisation
Data analytics
& visualisation
“Biddability”
of media

27. It is not entirely new…
And big data…

28. The obvious application is adTech

29. Big data changes everything?
Big data
Performance
Intelligence
“Numerisation” of
marketing

30. It changes advertising…
From To
•Handshake
•Trading pools
•Set pricing
•Arbitrary
•Inefficient
•Assumptive
•Campaign-based
•“50% is wasted…”
•Anonymous
•Programmatic
•Bid
•Transparent
•Efficient
•Cookie-based (ID
based)
•Personalised
•“Less than x%
wasted”

31. With Big data, media-buying will be…
• Efficient
• Transparent
• Biddable
• Real-time
• Scalable
• Technology-led
Like…

32. …a stock market

33. So more maths, less magic?
More maths Less magic?

34. One very important quote…
“We tend to overestimate the effect of a
technology in the short term and
underestimate the effect in the long term”
Nicholas Negroponte

35. Human intelligence is…different
Kasparov vs Deep Blue

36. So what does it mean for big data?
• It is not “a technology”
• It is an effect of technology
• It is not a yes or no
• It is here
• It is an enabler… for better judgement
• It is not a replacement for human intelligence

37. Hard evidence: remember this?

38. Big data without human intelligence is
nothing…
• Because marketing is about
predicting and influencing
certain human behaviours
• Bid data analytics without
humans is…nothing
• At mass level, businesses
will be able to spot trends,
use segmentation more
efficiently…
• At segment level, it will be
really complicated

39. Those changes will have an impact on
us, all of us…
I’m a planner, I’m good
at finding insights
I’m a media buyer: I understand
RTB, data sets, predictive
modelling…
I’m a media-buyer, I have a set
of preferred partners
I’m a marketing manager, I base my
decisions on knowledge of my
consumer
I’m a marketing manager, I have
studied maths, statistical and
probability theory; and I can code…
I’m a marketing director, I understand technology,
programmatic buying, attribution modelling, and
those new budget allocation software algorithms:
what do you have to tell me?

40. So, what will change in marketing?
• The definition of media will become murky
• The difference between advertising and
marketing will be blurred
• Agency ecosystem will evolve
• Skill requirements will change
• Marketing will be less of a right brain
discipline
• Technology is here to stay

41. The end of best practice
“As the cable and wiring powering marketing
become more automated, data and algorithm-
based, good marketing will have to be less
formulaic: better data will make intuitive and
creative marketers priceless”

42. In a world of cookies, the end of
cookie-cutter marketing
Market
research
Qual
research
Quant
research
Agency
brief
Creative
concept
Creative
testing
Ad
testing
Producti
on
Media
buy
Media
plan
Launch Results

43. Welcome, brave new marketing!
Social monitoring Big data
Real life
observation
Judgement &
experience
Research
Insights
Integrated
Strategy
Testing
and
trialling
Progressive, integrated, segmented multi channel multi platform
implementation founded on a distribution of content through
multiplicity of interacting channels
Tools
Channels Media
Content
CRM data Third part data
Product
innovation

44. Thank you!
vfpotier@yahoo.co.uk

45. BIG BREAKFAST, BIG DATA
GEORGE JOHN
CEO, ROCKET FUEL
LONDON
JANUARY 2013

47. AGENDA
1.
2.
3.
Big Data
Big Analytics
Rocket Fuel’s Big Data and AI for Media

48. BIG DATA
01

49. BIG DATA, ACCORDING TO MCKINSEY
Credit: McKinsey Global Institute June 2011 “Big Data: The next frontier for innovation, competition, and productivity”

50. BIG DATA DEFINED
1.
2.
3.
4.
Petabyte scale, now or soon
Data is gathered boldly – not “what is
the minimum that I need” but “what is
the maximum I can handle”
Stored and processed flexibly and
cheaply, to support both planned and
unplanned exploration
Delivers results

51. ENCODING DATA
WOULD A ROSE IN BINARY NOT SMELL AS
SWEET?
Try coding yourself at www.codeskulptor.org

52. MACBETH WORD CLOUD
Try wordle.net , a free online tool

53. PETA / HOW TO BORE YOUR FRIENDS
Credit: Wikipedia
Prefix Scale EG Bytes EG Real World Greek Origin
Kilo 1000 email “thousand”
Mega 1000^2 photo “great”
Giga 1000^3 movie Earth 150Gm to Sun “giant”
Tera 1000^4 1yr card transactions World uses 15TW “monster”
Peta 1000^5 Rocket Fuel Inc. Human Brain 2.5PB ~penta/”five”
Exa 1000^6 All internet mail Universe .43Es old ~hexa/”six”
Zetta 1000^7 Ocean is 1.4 ZL Zeta – 7th letter
Yotta 1000^8 Sun’s output 385 YW “eight”

54. STORAGE: DISRUPTION EVERY 4 YEARS
Walmart famous
for largest data
warehouse: 6TB
I have 6TB at
home, 6PB at
Rocket Fuel
Credit: www.mkomo.com/cost-per-gigabyte

55. DATABASES: THEN AND NOW
One giant pricey
machine, database
carefully
guarded
against
growth or
improvement
“BIG IRON”
DATABSES
HADOOP
DATA GRIDS
Hundreds of cheap
machines flexibly
managing
all the data
that could
be
imagined
to be useful, already

56. LOOKING RETRO: RELATIONAL DATABASES
WHAT’S A RELATION
You learned this in maths.
A relation is a set of tuples.
EG, “less than” relation
includes :
[1,2]
[-5,18]
but not
[1,0]
WHAT’S A RELATION IN A DATABASE
Uses tuples (records, rows) to store
descriptions of things or events,
where keys (names) allow links
between concepts.
Customer relation might include
[John, 29, single, heavy user]
[Sara, 54, married, light user]
Responses relation might include
[John, 2012-01-12, lead, funnel3a]

57. ASKING QUESTIONS OF SQL
SELECT COUNT(*)
FROM RESPONSES
WHERE
TYPE = “lead”
and
date >= “2012-01-01”
and
date <= “2012-01-31”
HOW MANY LEADS
HAVE BEEN
CAPTURED THIS
MONTH?
SELECT DISTINCT(C.NAME)
FROM CUSTOMERS C,
RESPONSES R
WHERE
C.name = R.name
and
R.TYPE = “lead”
and
R.date >= “2012-01-01”
and
R.date <= “2012-01-31”
LIST THE NAMES OF
THIS MONTH’S
LEADS
Note: pseudo-SQL used for readability
Uh…
BUILD A MODEL
THAT PREDICTS THE
EFFECTIVENESS OF
A CAMPAIGN

59. HADOOP IS READY FOR ANY OFFLINE
ANALYTICS
Flexible and fault-tolerant
Jobs assigned (mapped) to
workers
Results assembled (reduced)
as workers complete
Can write jobs in SQL or
programming languages
Buy a few more servers
when need scale/speed

60. EVOLUTION OF DATA ENTRY/CAPTURE
50 people x
50 words/min x
6 letters/word x
240 min/day =
3.6MB/day
5mm web visitors x
5 page views x
30 elements/page x
512 bytes/element =
384 GB/day
Clerical Data Entry
Data Capture,
Machine Logs,
“Data Exhaust”
10 cameras x
15 frames per second x
1Megapixel x
256 colors/pixel =
13 TB/day
Ambient Sensors
Credit: Wikipedia Credit: Wikipedia Credit: Koozoo

61. AN INTERNET OF BIG DATA
“From the dawn of civilization until 2003, humankind generated five
exabytes of data. Now we produce five exabytes every two days… and
the pace is accelerating.” -- Eric Schmidt, Chairman, Google

62. ACROSS MULTIPLE INDUSTRIES

63. BIG ANALYTICS
02

64. FROM DATA TO DECISIONS
“There is no point in collecting and
storing all this data if the algorithms
are not able to find useful patterns
and insights in the data….”
Jon Kleinberg, Cornell
Source: Tech’s New Wave, Driven by Data, by Steve Lohr 2012-09-08

65. WHAT TO DO…

66. ANALYTICS: THEN AND NOW
BUSINESS
INTELLIGENCE
(REPORTING)
MODELS
AUTONOMOUSLY
DEPLOYED

67. FAST SCIENCE
In questions of science, the authority of a
thousand is not worth the humble
reasoning of a single individual.
Galileo

68. Hollerith
Tabulating
Machine
Salesforce.com
Watson
Wins
Jeopardy
Mars Rover
Curiosity
Lands Itself
1890’s
ARTIFICIAL INTELLIGENCE HAS COME OF AGE
BUSINESS AUTOMATION ISNT JUST A DATABASE ANYMORE
2012
2011
2000’s
2013 Rocket Fuel runs
campaigns by itself

69. MODELS
Customer >
23years old?
Increasing
Spend?
Nottingham?
Target Exclude Target
Y N
Y N Y N

70. MODELS
Response score =
3.2891 * #days since last use of
account
1.08 if male
1.92 if female
Logit(past response rate)
.29 * city response rate
.02 * hour of day response rate
7.42 if in data segment “tech savvy”

71. “NoSQL” DATABASES SUPPORT REAL-
TIME SYSTEMS
High-volume and low-
latency services require giant
in-memory databases
Examples in media:
Storing delivery vs budget
information,
Storing user profiles
Syncing across datacenters

72. BIG DATA AND AI AT
ROCKET FUEL03

73. 2.4x 2011’s Revenue of $45 million

74. WE RUN DIGITAL CAMPAIGNS FOR GREAT BRANDS

75. HOW WE WORK WITH ADVERTISERS
“We need 20,000 new subscribers next
quarter”
“Run these ads, €1mm € 3.33 CPM, and
hit a target CPA of € 50”
300MM+ people, 26+ Bln ads, Every day
30ksubscribers
CPA = € 33
ADVERTISER
AGENCY

76. WHAT’S DIFFERENT
Untargeted/
Frequency Caps
Demo & Context
Segments
Multi-Dimensional
Targeting &
Optimization
Behavioral
Segments &
Geo-targeting
Artificial Intelligence
/ Autopilot
Effectiveness
The Online Ad Evolution
Age of Measurement Age of Targeting Age of Artificial Intelligence

77. RE-IMAGINED INTERFACE
“THE SKILL SHOULD BE IN THE MACHINE”
Drives campaigns completely autonomously
Needs only description of goal and budget
THEN.. AND NOW...
• Guess-driven targeting, bidding, and budgets
• Manual report-driven optimization

78. 26+ BILLION TIMES A DAY
Goal:
Leads
& sales
Goal:
Coupon
downloads
Goal:
Leads
& Sales
Site/PageGeo/WeatherTime of DayBrand AffinityDemo
Impression Scorecard
Demo
Brand Affinity
Time of Day
Geo/Weather
Site/Page
Ad Position
In-market
Behavior
Response

Impression Scorecard
Demo
Brand Affinity
Time of Day
Geo/Weather
Site/Page
Ad Position
In-Market
Behavior
Response
X
Impression Scorecard
Demo
Brand Affinity
Time of Day
Geo/Weather
Site/Page
Ad Position
In-Market
Behavior
Response
X
+100
+40
-20
+20
+15
+10
+40
+35
+9.7%
+40
-70
-20
+10
+15
-25
-40
-18
+0.7%
+10
-10
-20
+20
+10
-35
-25
+10
+1.4%

79. INSIDE THE BRAIN
SITES THAT DRIVE LUXURY CAR SALES

80. BRAND AND OFFLINE PURCHASE

81. -
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
MAN OR MACHINE?

82. AUTOMATED SELF-LEARNING
Continuous Adaptation With Real-Time Profile & Campaign Data
Age/Gender
Occupation
IncomeEthnicity
Purchase Intent
Online
Purchases
Offline
Purchases
Browsing
Behavior
Site Actions
Zip CodeCity/DMA
Search
Sites
Search
Categories
Recency
Search
Keywords
Web Site/Page
Referral URL
Site
Category
Bizographics
Social
Interests Lifestyle
Positive Lift
Marginal Impact
Negative Lift
x
+
-
-7
+17
X
-2
+8
+14
X
-9
-13
-12
X
+19
+13
+11
X
+11
X
X
X
+25
+6
X
-7 +17
-2
+28
X
+11
X
X
-9
+14
+17 +19
+8 +11
X
X
-9
+17
-23
+6
X
+17
-7
X
-2
-13
-12
X
+13
+6
+11
X
X
X
-9 X
+17
X
+19
+8
+14
+18
-23
+17
-12
+11
-9
+8 +14
X
+11
-13
-12
+13
+11
X
X
-7
+17 +8
+18X
+11
X -12-10
+6
+14
X
+8
+11
-10+13
+28 +6
+13
+19
X
+8
+11
-10
+13
-12
+17
X
-7
+8
X
60

83. AI MAKES NON-INTUITIVE CONNECTIONS

84. AI MAKES NON-INTUITIVE CONNECTIONS

85. AI CREATES BEAUTIFUL INTELLIGENCE

87. RECOMMENDED READING
Rick Smolan
The Human
Face of Big
Data
Nate Silver
The Signal
and the
Noise
Michael
Lewis
Moneyball
Chris Steiner
Automate
This: How
Algorithms
Came to Rule
Our World

88. Thank you!
Panel Q&A