origins
of
the
marke&ng
intelligence
engine
#SXSW
#MKTEngineMarch
14,
2015@paulroetzer

what’s
possible
when
the
art
and
science
of
markeFng
collide?

“Determining
the
next
field
to
be
invaded
by
bots
is
the
sum
of
two
simple
funcFons:
the
poten&al
to
disrupt
plus
the
reward
for
disrup&on."
@paulroetzer www.pr2020.com

poten&al
to
disrupt
+
reward
for
disrup&on

of
marketers
think
markeFng
has
changed
more
in
the
past
two
years
than
the
past
50
!
source:
Adobe
Digital
Distress
76%
@paulroetzer www.pr2020.com

the
consumer
is
the
true
change
catalyst
@paulroetzer www.pr2020.com

90% of
daily
media
interac&ons
are
screen
based
source:
Google,
The
New
MulF-­‐Screen
World
@paulroetzer

B2B
buyers
may
be
up
to
90%
through
their
journey
before
contacFng
a
vendor.
!
source:
Forrester
image:
Jayneandd

Source:
Google
Every
trackable
consumer
acFon
creates
a
data
point,
and
every
data
point
tells
a
piece
of
the
customer's
story
@paulroetzer www.pr2020.com

Image:
Chiefmartec.com
the
customer
journey
does
not
follow
a
linear
path
defined
by
marketers
@paulroetzer www.pr2020.com

Define
FoundaFon
Projects
blog
posts
podcasts
website
video
email
webinars
mobile
apps
tailored
markeFng
through
a
deep
understanding
of
buyer
persona
needs
+
the
ability
to
deliver
personalized
messages
Image:
HubSpot
we
have
entered
the
age
content,
context
and
the
customer
experience
@paulroetzer www.pr2020.com

Define
FoundaFon
Projects
create
more
value,
for
more
people,
more
oAen,
so
when
it’s
Fme
to
choose,
they
choose
you
new marketing imperative

We
need
markeFng
automaFon
tools
to
reach,
engage,
convert
and
delight
customers.
Source:HubSpot

Source:
Marketo
Understand
buyers,
idenFfy
opportuniFes,
track
campaign
performance,
and
link
marke&ng
ac&vi&es
to
business
outcomes.

Source:
Oracle
Capture
lead
intelligence
and
improve
lead-­‐to-­‐sale
conversion
rates.

Source:
Pardot
Drive
repeat
purchasing
and
enhance
the
overall
experience
throughout
the
customer
journey.

ExactTarget
IPO
(Mar
'12)
Eloqua
IPO
(Aug
'12)
ExactTarget
buys
Pardot
(Oct
'12)
HubSpot
raises
(Nov
'12)
Oracle
buys
Eloqua
(Dec
'12)
Marketo
IPO
(May
'13)
SF
buys
ExactTarget
(Jun
'13)
0 5 10 15 20 25
$161.5M
$92
M
$95.5M
$100
M
$871
M
$79
M
$2.5
B
venture
funding,
mergers,
acquisiFons
and
IPOs
fuel
the
marke&ng
automa&on
space
@paulroetzer www.pr2020.com

the marketing automation we
see today is elementary
when we consider what
comes next . . .
@paulroetzer www.pr2020.com

marketing automation
platforms save time, improve
efficiency and increase
productivity . . .
@paulroetzer www.pr2020.com

but they do NOT provide
deep insights into data . . .
@paulroetzer www.pr2020.com

data
>
intelligence
>
acFon
>
outcomes
@paulroetzer www.pr2020.com

We
create
2.5
quin&llion
bytes
of
data
every
day
(that’s
18
zeros)
!
90%
of
all
data
in
the
world
has
been
created
in
the
last
2
years
!
Source:
IBM
Infographic:
Domo

on
average,
marketers
depend
on
data
for
just
11%
of
customer-­‐related
decisions.
!
source:
CEB
@paulroetzer www.pr2020.com

B2B
marketers
say
just
9%
of
CEOs
and
6%
of
CFOs
use
markeFng
data
to
help
set
corporate
direcFon.
source:
ITSMA,
VisionEdge
and
Forrester
@paulroetzer www.pr2020.com

marketing automation
platforms generally do NOT
recommend actions or
predict outcomes.
@paulroetzer www.pr2020.com

marketers
remain
limited
by
biases,
beliefs,
educa.on,
experiences,
knowledge
and
brainpower.

We
have
a
finite
ability
to
process
informaFon,
build
strategies,
and
achieve
performance
poten&al.
@paulroetzer

Algorithms,
in
contrast,
have
an
almost
infinite
ability
to
process
informa&on.
They
possess
the
power
to
understand
natural
language
queries,
idenFfy
panerns
and
anomalies,
and
parse
massive
data
sets
to
deliver
recommendaFons
bener,
faster,
and
cheaper
than
people
can.
Image:
Wikimedia
Commons@paulroetzer www.pr2020.com

Turning
data
into
intelligence,
intelligence
into
strategy,
and
strategy
into
ac&on
remains
largely
human
powered.
@paulroetzer www.pr2020.com

What
inevitably
comes
next
are
marke&ng
intelligence
engines
that
process
data
and
recommend
acFons
to
improve
performance
based
on
probabiliFes
of
success.
@paulroetzer www.pr2020.com

There
is
a
relaFvely
untapped
technology
that
possesses
the
power
to
change
everything:
ar&ficial
intelligence.
@paulroetzer www.pr2020.com

consumer behavior + big data + human limitations =
potential to disrupt
@paulroetzer www.pr2020.com

the
disrup&on
of
industries

@paulroetzer www.pr2020.com
60%
of
all
trades
are
executed
by
computers
with
linle
or
no
real-­‐Fme
oversight
from
humans.
!
Source:
Christopher
Steiner,
Automate
This

avg
120
stops/day

what
is
the
possible
number
of
alterna&ves
for
ordering
those
stops?
@paulroetzer www.pr2020.com

6,689,502,913,449,135,000,000,000,
000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,
000,000
Source: Wall Street Journal

“Can
a
human
really
think
of
the
best
way
to
deliver
120
stops?
This
is
where
the
algorithm
will
come
in.
It
will
explore
paths
of
doing
things
you
would
not,
because
there
are
just
too
many
combinaFons.”
!
Jack
Levis
Senior
director
of
process
management,
UPS
Source: Wall Street Journal

NETFLIX
uses
algorithms
to
suggest
content
and
manufacture
shows
based
on
subscriber
viewing
habits
and
preferences.
Source:
Neqlix
Tech
Blog

75%
of
what
people
watch
on
NeXlix
is
from
some
sort
of
algorithm-­‐generated
recommenda&on
Source:
Neqlix
Tech
Blog

Epagogix
algorithms
analyze
movie
scripts
to
predict
how
much
money
they
will
make
at
the
box
office
and
offer
recommenda&ons
on
how
to
make
them
more
marketable
and
profitable,
including
through
changes
to
plot
lines,
se[ngs,
character
roles
and
actors.

Source:
NASA
Instagram

Source:
NASA
Instagram
“enlisFng
the
help
of
machines
to
sort
through
thousands
of
stars
in
our
galaxy
and
learn
their
sizes,
composiFons
and
other
basic
traits.
.
.
.computers
learn
from
large
data
sets,
finding
paerns
that
humans
might
not
otherwise
see.”

Image:
Franck
Calzada/YouTube
The AP “writes”
10x more earnings
reports using
Automated Insights
technology

Source: Social Media Frontiers
Source:
vicarious.com
“We
are
building
a
unified
algorithmic
architecture
to
achieve
human-­‐level
intelligence
in
vision,
language,
and
motor
control.
.
.
.
our
system
requires
orders
of
magnitude
less
training
data
than
tradi&onal
machine
learning
techniques.”

Source: Social Media Frontiers
Source:
vicarious.com

Source: Social Media Frontiers
$70
million
in
funding
from:
!
Elon
Musk,
Mark
Zuckerberg,
Peter
Thiel,
Jeff
Bezos,
Jerry
Yang,
Marc
Benioff,
Janus
Friis,
Ashton
Kutcher,
Aaron
Levie,
DusFn
Moskovitz
.
.
.
Source:
Wall
Street
Journal,
TechCrunch
and
Vicarious

Source: Social Media Frontiers
Facebook
uses
“deep
learning,”
an
A.I.
subfield,
to
filter
your
Newsfeed
and
recognize
faces
in
photos
you
upload,
but
that’s
only
the
beginning
.
.
.

Source: Social Media Frontiers
hnps://research.facebook.com/ai

Source: Social Media Frontiers
hnps://research.facebook.com/ai
“We’re
commined
to
advancing
the
field
of
machine
intelligence
and
developing
technologies
that
give
people
beer
ways
to
communicate.
In
the
long
term,
we
seek
to
understand
intelligence
and
make
intelligent
machines.”

The
DeepMind
team
at
Google
has
built
a
machine
that
taught
itself
how
to
play
and
win
over
49
Atari
2600
games
from
the
1980s
Image:
NML32/YouTube Source:
The
New
Yorker,
ArFficial
Intelligence
Goes
To
The
Arcade

“It
is
programmed
to
find
a
score
rewarding,
but
is
given
no
instruc&on
in
how
to
obtain
that
reward.
!
“Its
first
moves
are
random,
made
in
ignorance
of
the
game’s
underlying
logic.
Some
are
rewarded
with
a
treat—a
score—and
some
are
not.
!
“Buried
in
the
DeepMind
code,
however,
is
an
algorithm
that
allows
the
juvenile
A.I.
to
analyze
its
previous
performance,
decipher
which
ac&ons
led
to
beer
scores,
and
change
its
future
behavior
accordingly.”
Source:
The
New
Yorker,
ArFficial
Intelligence
Goes
To
The
Arcade

“It
is
programmed
to
find
a
score
rewarding,
but
is
given
no
instruc&on
in
how
to
obtain
that
reward.
!
“Its
first
moves
are
random,
made
in
ignorance
of
the
game’s
underlying
logic.
Some
are
rewarded
with
a
treat—a
score—and
some
are
not.
!
“Buried
in
the
DeepMind
code,
however,
is
an
algorithm
that
allows
the
juvenile
A.I.
to
analyze
its
previous
performance,
decipher
which
ac&ons
led
to
beer
scores,
and
change
its
future
behavior
accordingly.”
Source:
The
New
Yorker,
ArFficial
Intelligence
Goes
To
The
Arcade

search,
voice
recogni&on,
language
transla&on,
robots,
driverless
cars
.
.
.

the
marke&ng
machine
age

“At
the
heart
of
all
of
these
algorithm-­‐enabled
revoluFons
on
Wall
Street
and
elsewhere,
there
exists
one
persistent
goal:
predic&on—to
be
more
exact,
predicFon
of
what
other
humans
will
do.”
@paulroetzer www.pr2020.com

“Imagine
a
world
where
you
can
predict
with
above
85%
accuracy
who
will
buy,
what
they
will
buy,
how
much,
what
channel
will
reach
them,
what
message
will
resonate.”
—
Amanda
Kahlow,
6sense
founder
and
CEO
Source:
VentureBeat

“a
predic&ve
intelligence
engine
for
markeFng
and
sales”

“We
then
apply
machine
learning
and
predic&ve
algorithms
to
profile
your
customers
and
predict
behaviors
such
as
likelihood
to
purchase,
churn,
and
lifeFme
value.”
Source:
RetenFon
Science

turn
data
into
(ar&ficial)
intelligence

turn
data
into
(ar&ficial)
intelligence

Source:
NarraFve
Science

Source:
NarraFve
Science

Source:
MarkeFng
Land

$143.8 M
$76.6 M*
$36.0 M
$32.4 M
$36.0 M
$20.0 M
$15.4 M
$10.8 M*
$9.5 M
$2.5 MSource:
Crunchbase
Artificial Intelligence + Marketing
$383 M

“We
expect
technology
spend
by
CMOs
to
increase
10x
in
10
years,
from
$12
billion
to
$120
billion,
unlocking
a
huge
opportunity
for
markeFng
technology
companies
and
opening
the
door
to
the
decade
of
the
CMO.”
!
—
Ashu
Garg,
general
partner,
FoundaFon
Capital
Source:
ChiefMartec.com
Image:
Tracy
Olson,
Flickr

6
classes,
43
categories,
1,876
companies

$49
billion
in
investment
across
537
markeFng
technology
products
that
received
major
funding
Source:
VentureBeat

consumer behavior + big data + human limitations =
potential to disrupt
@paulroetzer www.pr2020.com

capital + funding velocity + innovator advantage =
reward for disruption
@paulroetzer www.pr2020.com

potential to disrupt + reward for disruption =
MARKETING
@paulroetzer www.pr2020.com

“We’re
in
an
AI
spring.
For
our
company,
and
I
think
for
every
company,
the
revoluFon
in
data
science
will
fundamentally
change
how
we
run
our
business
because
we’re
going
to
have
computers
aiding
us
in
how
we’re
interacFng
with
our
customers.”
!
—
Marc
Benioff
Source:
FortuneImage:
Wikipedia

acquired
by
Salesforce
in
2014
for
$390
million
!
“Salesforce.com
Inc.
has
started
working
to
integrate
ar&ficial-­‐intelligence
technology
from
acquisiFon
RelateIQ
Inc.
into
its
sozware,
seeking
to
add
predic&ve
capabili&es
that
will
help
it
compete
with
younger
startups.”
Source:
Bloomberg
Business

is
IBM’s
Watson
the
future
of
marke&ng?

Image:
Wikimedia
Commons
The
story
of
arFficial
intelligence
can’t
be
told
without
IBM
,
which
possesses
an
es&mated
500
AI-­‐related
patents.
Source:
Business
Insider

Source:
IBM

hnp://www.ibm.com/analyFcs/watson-­‐analyFcs/

hnp://www.ibm.com/analyFcs/watson-­‐analyFcs/

hnp://www.ibm.com/analyFcs/watson-­‐analyFcs/

hnp://www.ibm.com/analyFcs/watson-­‐analyFcs/

Image:
Wikimedia
Commons
“There
is
a
science
and
an
art
to
every
profession.
Soon,
Watson
will
know
the
science
bener
than
a
human.
Humans
will
need
to
focus
on
the
art
of
their
profession—
the
creaFve
elements
only
they
can
provide.
!
—
Daniel
Burrus,
author,
Burrus
Research
founder
and
CEO
Source:
Wired

so,
what’s
possible?

reviewing
analy&cs
crea&ng
performance
reports
&
data
visualiza&ons
publishing
social
media
updates
planning
blog
post
topics
copywri&ng
cura&ng
content
building
strategy
alloca&ng
resources

Imagine
if
a
marketer’s
primary
role
was
to
curate
and
enhance
algorithm-­‐based
recommenda&ons
and
content,
rather
than
devise
them.

Rather
than
simply
automaFng
manual
tasks,
arFficial
intelligence
adds
a
cogniFve
layer
that
infinitely
expands
marketers’
ability
to
process
data,
idenFfy
panerns,
and
build
intelligent
strategies
and
content
faster,
cheaper
and
more
effec&vely
than
humans.

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
historical
performance
data

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
real-­‐Fme
analy&cs

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
industry
and
company
benchmarks

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
subjecFve
human
inputs

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
business
and
campaign
goals

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
create
content

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
enhance
experiences

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
recommend
acFons

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
predict
outcomes

Algorithm-­‐based
intelligence
engine
for
all
major
markeFng
acFviFes
and
strategies.
data
>
intelligence
>
ac&ons
>
outcomes

“The
ability
to
create
algorithms
that
imitate,
beer,
and
eventually
replace
humans
is
the
paramount
skill
of
the
next
one
hundred
years.
As
the
people
who
can
do
this
mulFply,
jobs
will
disappear,
lives
will
change,
and
industries
will
be
reborn.”
!
Christopher
Steiner,
Automate
This

The future may be
closer than you think.
@paulroetzer

“MarkeFng
is
now,
as
it
has
always
been,
an
art
form.
But
the
next
generaFon
of
marketers
understands
it
can
be
so
much
more.
These
innovators
are
rewriFng
what
is
possible
when
the
art
and
science
of
marke&ng
collide.”
@paulroetzer www.pr2020.com

paul
roetzer,
@paulroetzer
!
founder
&
CEO
|
PR
20/20
author
|
The
Marke.ng
Performance
Blueprint
(Wiley,
2014)
&
The
Marke.ng
Agency
Blueprint
(Wiley,
2012)
creator
|
MarkeFng
Score
&
MarkeFng
Agency
Insider
www.pr2020.com
bit.ly/roetzer-­‐sxsw15