Marketing automation platforms save time, improve efficiency and increase productivity. They give companies an unprecedented ability to understand buyers, identify opportunities, track campaign performance and link marketing activities to business outcomes.
But, they do not provide insight into the billions of bits of data being created as consumers move from screen to screen and interact online and offline with brands, and they do not recommend actions to improve performance.
Humans are limited by their biases, beliefs, education, experiences, knowledge and brainpower. All of these things contribute to our finite ability to process information, build strategies and achieve performance potential.
Algorithms, in contrast, have an almost infinite ability to process information. They possess the power to understand natural language queries, identify patterns and anomalies, and parse massive data sets to deliver recommendations better, faster and cheaper than people can.
What inevitably comes next are marketing intelligence engines that process data and recommend actions to improve performance based on probabilities of success.
3. “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
5. 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
6. the
consumer
is
the
true
change
catalyst
@paulroetzer www.pr2020.com
7. 90% of
daily
media
interac&ons
are
screen
based
source:
Google,
The
New
MulF-‐Screen
World
@paulroetzer
8. B2B
buyers
may
be
up
to
90%
through
their
journey
before
contacFng
a
vendor.
!
source:
Forrester
image:
Jayneandd
9. 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
11. 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
12. Define
FoundaFon
Projects
create
more
value,
for
more
people,
more
oAen,
so
when
it’s
Fme
to
choose,
they
choose
you
new marketing imperative
13. We
need
markeFng
automaFon
tools
to
reach,
engage,
convert
and
delight
customers.
Source:HubSpot
22. 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
23. on
average,
marketers
depend
on
data
for
just
11%
of
customer-‐related
decisions.
!
source:
CEB
@paulroetzer www.pr2020.com
24. 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
27. We
have
a
finite
ability
to
process
informaFon,
build
strategies,
and
achieve
performance
poten&al.
@paulroetzer
28. 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
29. Turning
data
into
intelligence,
intelligence
into
strategy,
and
strategy
into
ac&on
remains
largely
human
powered.
@paulroetzer www.pr2020.com
30. 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
31. There
is
a
relaFvely
untapped
technology
that
possesses
the
power
to
change
everything:
ar&ficial
intelligence.
@paulroetzer www.pr2020.com
32. consumer behavior + big data + human limitations =
potential to disrupt
@paulroetzer www.pr2020.com
34. @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
38. “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
39. NETFLIX
uses
algorithms
to
suggest
content
and
manufacture
shows
based
on
subscriber
viewing
habits
and
preferences.
Source:
Neqlix
Tech
Blog
40. 75%
of
what
people
watch
on
NeXlix
is
from
some
sort
of
algorithm-‐generated
recommenda&on
Source:
Neqlix
Tech
Blog
41. 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.
43. 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.”
46. 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.”
48. 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
49. 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
.
.
.
51. 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.”
52. 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
53. “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
54. “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
57. “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
58. “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
61. “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
67. $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
68. “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
70. $49
billion
in
investment
across
537
markeFng
technology
products
that
received
major
funding
Source:
VentureBeat
71. consumer behavior + big data + human limitations =
potential to disrupt
@paulroetzer www.pr2020.com
72. capital + funding velocity + innovator advantage =
reward for disruption
@paulroetzer www.pr2020.com
73. potential to disrupt + reward for disruption =
MARKETING
@paulroetzer www.pr2020.com
74. “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
75. 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
79. 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
85. 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
87. 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
88. Imagine
if
a
marketer’s
primary
role
was
to
curate
and
enhance
algorithm-‐based
recommenda&ons
and
content,
rather
than
devise
them.
89. 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.
101. “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
103. “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
104. 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