origins of the marketing intelligence engine (sxsw 2015)
TRANSCRIPT
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 basedsource: 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
“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 pa\erns that humans might not otherwise see.”
Image: Franck Calzada/YouTube
The AP “writes” 10x more earnings reports using Automated Insights technology
Source: Social Media FrontiersSource: 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 FrontiersSource: 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 Frontiershnps://research.facebook.com/ai
Source: Social Media Frontiershnps://research.facebook.com/ai
“We’re commined to advancing the field of machine intelligence and developing technologies that give people be\er 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 be\er 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 be\er 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: 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
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, be\er, 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