Origins of the Marketing Intelligence Engine (SXSW 2015)

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%

the
consumer
is
the
true
change
catalyst

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

Image:
Chiefmartec.com
the
customer
journey
does
not
follow

a
linear
path
deﬁned
by
marketers

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

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


the marketing automation we
see today is elementary
when we consider what
comes next . . .

marketing automation
platforms save time, improve
efficiency and increase
productivity . . .

but they do NOT provide
deep insights into data . . .

data
>
intelligence
>
acFon
>
outcomes

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


B2B
marketers
say
just
9%
of
CEOs
and
6%
of
CFOs
use

markeFng
data
to
help
set
corporate
direcFon.

source:
ITSMA,
VisionEdge
and
Forrester

marketing automation
platforms generally do NOT
recommend actions or
predict outcomes.

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.


What
inevitably
comes
next
are

marke&ng
intelligence
engines

that
process
data
and
recommend
acFons

to
improve
performance
based
on

probabiliFes
of
success.

There
is
a
relaFvely
untapped

technology
that
possesses
the

power
to
change
everything:

ar&ﬁcial
intelligence.

consumer behavior + big data + human limitations =
potential to disrupt

the
disrup&on
of
industries

60%
of
all
trades
are
executed
by
computers

with
linle
or
no
real-‐Fme
oversight
from
humans.

!
Source:
Christopher
Steiner,
Automate
This

what
is
the
possible
number
of

alterna&ves
for
ordering
those
stops?

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:
vicarious.com

$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

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
.
.
.

hnps://research.facebook.com/ai

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

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.”


“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

Source:
NarraFve
Science

$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

capital + funding velocity + innovator advantage =
reward for disruption

potential to disrupt + reward for disruption =
MARKETING

“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

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.”

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

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