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ORIGINS OF THE MARKETING INTELLIGENCE ENGINE
Paul Roetzer, Founder & CEO, PR 20/20
Copyright 2016 PR 20/20. All rights reserved.
@PaulRoetzer
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Consider how much !me your marke2ng team spends . . .
crea2ng ad copy managing digital ad campaigns
tes2ng headlines, landing pages, ads scheduling/publishing social shares predic2ng opens, clicks, conversions reviewing analy!cs
wri2ng performance reports recommending strategies alloca2ng resources
dra;ing social media updates discovering keywords
planning blog post topics wri!ng content op!mizing content cura!ng content
personalizing content automa!ng content building email workflows
Copyright 2016 PR 20/20. All rights reserved.
Now imagine if machines performed the majority of those activities,
and a marketer’s primary role was to enhance rather than create.
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“The science of making machines smart.” — Demis Hassabis, Co-‐Founder & CEO of DeepMind
what is ar!ficial intelligence?
(which in turn augments human knowledge and capabili5es)
Source: Rolling Stone
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a set of instruc!ons that tells the machine what to do.
what is an algorithm?
(except with AI the machine can create its own algorithms, determine new paths, and unlock unlimited poten5al to advance marke5ng, and mankind.)
@paulroetzer www.pr2020.com
60% of all trades are executed by computers with liKle or no real-‐2me oversight from humans. Source: Christopher Steiner, Automate This
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 combina2ons.”
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: NeUlix Tech Blog
75% of what people watch on NeSlix is from some sort of algorithm-‐generated recommenda!on
Source: NeUlix 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, seVngs, character roles and actors.
ExactTarget IPO (Mar '12)
Oracle buys Eloqua (Dec '12)
SF buys ExactTarget (Jun '13)
IBM buys Silverpop (Apr '14)
Marketo market cap (8-‐30-‐16)
HubSpot market cap (8-‐30-‐16)
0 5 10 15 20 25
$161.5M
$871 M
$2.5 B
venture funding, mergers, acquisi2ons and IPOs have fueled the marke!ng automa!on space
@paulroetzer www.pr2020.com
$270 M
$1.6 B
$1.9 B
90% of all data in the world has been created in the last 2 years
Source: IBM
marketers have access to data from dozens of sources: social monitoring, media monitoring, web analytics, email, call tracking, sales, advertising, remarketing, ecommerce, mobile apps. . .
We have a finite ability to process informa2on, build strategies, create content at scale, and achieve performance poten!al.
Algorithms, in contrast, have an almost infinite ability to process data, and deliver
predictions, recommendations and content better, faster and cheaper.
Image: Wikimedia Commons
@paulroetzer www.pr2020.com
And yet marke2ng remains largely human powered, with a bit of
automa2on mixed in.
Image: Franck Calzada/YouTube
The AP “writes” 10x more earnings reports using AI, specifically natural language generation
Define Founda2on Projects
Subjective analysis Internal stakeholders 10 sections 27 profile fields 132 factors
Sample Marke5ng Score Factor Slider Scale
A strong marke!ng technology founda2on is cri2cal to driving performance. Core technologies, when integrated, improve efficiencies, maximize produc2vity and ROI, and create compe22ve advantages. The company should priori2ze CMS (5), CRM (4), email marke2ng (3), marke2ng analy2cs (2) and marke2ng automa2on (2).
sample key finding
A strong marke!ng technology founda2on is cri2cal to driving performance. Core technologies, when integrated, improve efficiencies, maximize produc2vity and ROI, and create compe22ve advantages. The company should priori2ze CMS (5), CRM (4), email marke2ng (3), marke2ng analy2cs (2) and marke2ng automa2on (2).
* Requires human writers to develop and enhance templates.
Using Natural Language Genera!on (aka Machine Assisted)*: 50 briefs x 15 minutes per brief = 12.5 hours/month
The Diff: 37.5 hours (at a cost of $250/month for the license.)
Tradi!onal Way: 50 briefs x 1 hour per brief = 50 hours/month
The Benefits
More accurate (eliminates human error) More briefs published (enables content at scale) More cost efficient (shi;s 2me to edi2ng only) More engagement More value crea!on for members More new business opportuni!es
Private investment in the AI sector has grown from $1.7B in 2010 to $14.9B in 2014
The market for AI based analytics could grow from $8.2B to $70B by 2020.
— Source: BofA Merrill Lynch: Robot Revolution — Global Robot & AI Primer
There are dozens of AI-powered marketing tools that you can use to predict, plan, create, optimize, personalize, promote, measure and analyze.
Source: Timothy Neesom
$29.4 M
$36.0 M
$9.5 M
Source: Crunchbase
Artificial Intelligence + Marketing
$279+ M $80.0 M*
$66.0 M
$14.5 M
$13.9 M
$11.0 M
$5.4 M
$14.2 M
“We’re in an AI spring. For our company, and I think for every company, the revolution in data science will fundamentally change how we run our business because we’re going to have computers aiding us in how we’re interacting with our customers.”
— Marc Benioff
Source: FortuneImage: Wikipedia
Source: Social Media Frontiers
Facebook uses “deep learning,” an AI subfield, to filter your Newsfeed and recognize faces in photos you upload,
but that’s only the beginning . . .
Source: Social Media FrontiershKps://research.facebook.com/ai
“We’re commiKed to advancing the field of machine intelligence and developing technologies that give
people beger ways to communicate. In the long term, we seek to understand intelligence and make intelligent
machines.”
Image: Wikimedia CommonsSource: Business Insider
The story of ar2ficial intelligence can’t be told without IBM , which possesses an es!mated 500 AI-‐related patents.
IBM Watson is a technology plaUorm that uses natural language processing and machine learning to reveal insights
from large amounts of unstructured data
Source: IBM
Source: Popular Science
“IBM used machine learning and experimental Watson APIs, parsing out the trailers of 100 horror movies. It did visual, audio, and composi2on analysis of individual scenes. . . . Watson was then fed the full film, and it chose scenes for the trailer. . . . A process that would normally take weeks was reduced to hours.”
"Cogni2ve technology is there to extend and amplify human exper!se, not replace it.” — Rob High, Chief Technology Officer, IBM Watson
Rather than simply automa2ng manual tasks, ar!ficial intelligence adds a cogni!ve layer that infinitely expands marketers’ ability to process data, iden2fy paKerns, predict outcomes, and build intelligent strategies and content beger, faster and cheaper.
dra;ing social media updates * discovering keywords * planning blog post topics * wri!ng content * op!mizing content * cura!ng content * personalizing content * automa!ng content * building email workflows * crea2ng ad copy * managing digital ad campaigns * tes2ng headlines, landing pages, ads * scheduling/publishing social shares * predic2ng opens, clicks, conversions * reviewing analy!cs * wri2ng performance reports * recommending strategies * alloca2ng resources
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, Ar2ficial Intelligence Goes To The Arcade
“It is programmed to find a score rewarding, but is given no instruction 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 actions led to better
scores, and change its future behavior accordingly.”Source: The New Yorker, Ar2ficial Intelligence Goes To The Arcade
“It is programmed to find a score rewarding, but is given no instruction 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 actions led to better
scores, and change its future behavior accordingly.”Source: The New Yorker, Ar2ficial Intelligence Goes To The Arcade
What inevitably comes next are marketing intelligence engines
that process data and recommend actions to improve performance based on
probabilities of success.
“The ability to create algorithms that imitate, better, and eventually replace humans is the paramount skill of the next one hundred years. As the people who can do this multiply, jobs will disappear, lives will change, and industries will be reborn.”
#2
Assess opportunities to get more out of your data—discover insights, predict outcomes, devise strategies, personalize content across channels, and tell stories at scale.
#3
Consider the AI capabilities of your existing marketing technology, and explore the potential of emerging AI solutions.
paul roetzer [email protected] @paulroetzer
CEO | PR 20/20 creator | Marke2ng Ar2ficial Intelligence Ins2tute author | The Marke5ng Performance Blueprint (Wiley, 2014) & The Marke5ng Agency Blueprint (Wiley, 2012)
www.pr2020.com