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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: SAS Institute Inc
- Sprache: Englisch
A straightforward, non-technical guide to the next major marketing tool Artificial Intelligence for Marketing presents a tightly-focused introduction to machine learning, written specifically for marketing professionals. This book will not teach you to be a data scientist--but it does explain how Artificial Intelligence and Machine Learning will revolutionize your company's marketing strategy, and teach you how to use it most effectively. Data and analytics have become table stakes in modern marketing, but the field is ever-evolving with data scientists continually developing new algorithms--where does that leave you? How can marketers use the latest data science developments to their advantage? This book walks you through the "need-to-know" aspects of Artificial Intelligence, including natural language processing, speech recognition, and the power of Machine Learning to show you how to make the most of this technology in a practical, tactical way. Simple illustrations clarify complex concepts, and case studies show how real-world companies are taking the next leap forward. Straightforward, pragmatic, and with no math required, this book will help you: * Speak intelligently about Artificial Intelligence and its advantages in marketing * Understand how marketers without a Data Science degree can make use of machine learning technology * Collaborate with data scientists as a subject matter expert to help develop focused-use applications * Help your company gain a competitive advantage by leveraging leading-edge technology in marketing Marketing and data science are two fast-moving, turbulent spheres that often intersect; that intersection is where marketing professionals pick up the tools and methods to move their company forward. Artificial Intelligence and Machine Learning provide a data-driven basis for more robust and intensely-targeted marketing strategies--and companies that effectively utilize these latest tools will reap the benefit in the marketplace. Artificial Intelligence for Marketing provides a nontechnical crash course to help you stay ahead of the curve.
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Veröffentlichungsjahr: 2017
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Table of Contents
Cover
Title Page
Foreword
Preface
AI IN A NUTSHELL
THIS IS YOUR MARKETING ON AI
Acknowledgments
CHAPTER 1: Welcome to the Future
WELCOME TO AUTONOMIC MARKETING
WELCOME TO ARTIFICIAL INTELLIGENCE FOR MARKETERS
WHOM IS THIS BOOK FOR?
THE BRIGHT, BRIGHT FUTURE
IS AI SO GREAT IF IT'S SO EXPENSIVE?
WHAT'S ALL THIS AI THEN?
THE AI UMBRELLA
THE MACHINE THAT LEARNS
ARE WE THERE YET?
AI‐POCALYPSE
MACHINE LEARNING'S BIGGEST ROADBLOCK
MACHINE LEARNING'S GREATEST ASSET
ARE WE REALLY CALCULABLE?
NOTES
CHAPTER 2: Introduction to Machine Learning
THREE REASONS DATA SCIENTISTS SHOULD READ THIS CHAPTER
EVERY REASON MARKETING PROFESSIONALS SHOULD READ THIS CHAPTER
WE THINK WE'RE SO SMART
DEFINE YOUR TERMS
ALL MODELS ARE WRONG
USEFUL MODELS
TOO MUCH TO THINK ABOUT
MACHINES ARE BIG BABIES
WHERE MACHINES SHINE
STRONG VERSUS WEAK AI
THE RIGHT TOOL FOR THE RIGHT JOB
MAKE UP YOUR MIND
ONE ALGORITHM TO RULE THEM ALL?
ACCEPTING RANDOMNESS
WHICH TECH IS BEST?
FOR THE MORE STATISTICALLY MINDED
WHAT DID WE LEARN?
NOTES
CHAPTER 3: Solving the Marketing Problem
ONE‐TO‐ONE MARKETING
ONE‐TO‐MANY ADVERTISING
THE FOUR
P
S
WHAT KEEPS A MARKETING PROFESSIONAL AWAKE?
THE CUSTOMER JOURNEY
WE WILL NEVER REALLY KNOW
HOW DO I CONNECT? LET ME COUNT THE WAYS
WHY DO I CONNECT? BRANDING
MARKETING MIX MODELING
ECONOMETRICS
CUSTOMER LIFETIME VALUE
ONE‐TO‐ONE MARKETING—THE MEME
SEAT‐OF‐THE‐PANTS MARKETING
MARKETING IN A NUTSHELL
WHAT SEEMS TO BE THE PROBLEM?
NOTES
CHAPTER 4: Using AI to Get Their Attention
MARKET RESEARCH: WHOM ARE WE AFTER?
MARKETPLACE SEGMENTATION
RAISING AWARENESS
SOCIAL MEDIA ENGAGEMENT
IN REAL LIFE
THE B2B WORLD
NOTES
CHAPTER 5: Using AI to Persuade
THE IN‐STORE EXPERIENCE
ON THE PHONE
THE ONSITE EXPERIENCE—WEB ANALYTICS
MERCHANDISING
CLOSING THE DEAL
BACK TO THE BEGINNING: ATTRIBUTION
NOTES
CHAPTER 6: Using AI for Retention
GROWING CUSTOMER EXPECTATIONS
RETENTION AND CHURN
MANY UNHAPPY RETURNS
CUSTOMER SENTIMENT
CUSTOMER SERVICE
PREDICTIVE CUSTOMER SERVICE
NOTES
CHAPTER 7: The AI Marketing Platform
SUPPLEMENTAL AI
MARKETING TOOLS FROM SCRATCH
A WORD ABOUT WATSON
BUILDING YOUR OWN
NOTES
CHAPTER 8: Where Machines Fail
A HAMMER IS NOT A CARPENTER
MACHINE MISTAKES
HUMAN MISTAKES
THE ETHICS OF AI
SOLUTION?
WHAT MACHINES HAVEN'T LEARNED YET
NOTES
CHAPTER 9: Your Strategic Role in Onboarding AI
GETTING STARTED, LOOKING FORWARD
AI TO LEVERAGE HUMANS
COLLABORATION AT WORK
YOUR ROLE AS MANAGER
KNOW YOUR PLACE
AI FOR BEST PRACTICES
NOTES
CHAPTER 10: Mentoring the Machine
HOW TO TRAIN A DRAGON
WHAT PROBLEM ARE YOU TRYING TO SOLVE?
WHAT MAKES A GOOD HYPOTHESIS?
THE HUMAN ADVANTAGE
NOTES
CHAPTER 11: What Tomorrow May Bring
THE PATH TO THE FUTURE
MACHINE, TRAIN THYSELF
INTELLECTUAL CAPACITY AS A SERVICE
DATA AS A COMPETITIVE ADVANTAGE
HOW FAR WILL MACHINES GO?
YOUR BOT IS YOUR BRAND
MY AI WILL CALL YOUR AI
COMPUTING TOMORROW
NOTES
About the Author
Index
End User License Agreement
List of Tables
Chapter 4
Table 4.1 A spreadsheet was the start of database marketing
List of Illustrations
Chapter 1
Figure 1.1 A McKinsey survey finds advertising and marketing highly ranked for disruption.
Figure 1.2 Elon Musk expresses his disquiet on Twitter.
Figure 1.3 Marketing and sales managers get to keep their jobs a little longer than most.
Figure 1.4 The spectrum of probability (Math Is Fun)
Figure 1.5 So many types of data, so little time
Chapter 2
Figure 2.1 Lionel Helicopter Car
Figure 2.2 A prioritization model in Excel
Figure 2.3 Guys who park motorcycle indoors are more likely to buy insurance.
Figure 2.4 The more attributes, the more high‐level math is required.
Figure 2.5 Looking at data in 3‐D can make classification easier.
Figure 2.6 CVS uses what you buy to discount things you are most likely to buy.
Figure 2.7 Going out to the movies
Figure 2.8 A simple neural net
Figure 2.9 Three‐layered deep learning network
Figure 2.10 Dataiku offers advice on how to fit the algorithm to the task.
Chapter 3
Figure 3.1 Shiny new tools have always been cool. Finding a use for them is hard.
Figure 3.2 General store interior, Moundville, Alabama, 1936
Figure 3.3 Sears Catalog, 1909.
Figure 3.4 New Marketing Ecosystem poster
Figure 3.5 Bronze branding‐stamp: aegis of Sekhmet (British Museum)
Chapter 4
Figure 4.1 Quid analyzes the digital health marketplace.
Figure 4.2 Even in the twenty‐first century, simple data cleansing is tough.
Figure 4.3 The Milwaukee Fluorescent Lamp and Ballast Tester Kit
Figure 4.4 Pinterest recognized the recliner without trouble.
Figure 4.5 Not a robotic hairdryer
Figure 4.6 Genetics algorithm choosing ad components for electronic billboard.
Figure 4.7 Radius tracks more bits than you can keep in a file cabinet.
Chapter 5
Figure 5.1 The Customer Life Cycle Funnel
Figure 5.2 The Dynamic Customer Decision Journey
Figure 5.3 Macy's On Call app is still learning.
Figure 5.4 Macy's On Call identity crisis
Figure 5.5 Mapping contextual intelligence into an app
Figure 5.6 Instant classic bots joke
Figure 5.7 1‐800‐Flowers' GWYN memories are adjustable.
Figure 5.8 Honest Café tracks people it can't see.
Figure 5.9 Sentient Ascend's mutation generator decides who gets to reproduce.
Figure 5.10 The North Face offered the least expensive as the highest match to my needs.
Figure 5.11 Islam's algorithm framework
Figure 5.12 Ye olde attribution models
Chapter 6
Figure 6.1 When your rate of churn exceeds acquisition, your business is dying.
Figure 6.2 Amy sounds very human when scheduling meetings.
Figure 6.3 Taco Bell chats you through your meal planning…
Figure 6.4 … and then tells you where and how much, with a side order of humor.
Chapter 7
Figure 7.1 Staple's Easy Button takes orders.
Chapter 8
Figure 8.1 Nicolas Cage films are no more responsible for drownings than ice cream. And yet…
Figure 8.2 Kill the kid and the crook crossing on a red light or the cat and the dog crossing on the green?
Figure 8.3 Microsoft's Tay didn't last a day.
Figure 8.4 Subtle messaging based on personality models in the Republican primary
Chapter 9
Figure 9.1 Find your place to get to the next level.
Figure 9.2 InfoSys has a few questions for you.
Figure 9.3 Scott Brinker warns of the increasing rate of change.
Figure 9.4 The Cognitive and Immersive Systems Laboratory knows where you're standing.
Figure 9.5 Accenture's look at managers' time allotment
Figure 9.6 Complex problem solving wins the decade.
Figure 9.7 The Integrated Strategy Machine
Guide
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The Wiley & SAS Business Series presents books that help senior‐level managers with their critical management decisions.
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Artificial Intelligence for Marketing
Practical Applications
Jim Sterne
Copyright © 2017 by Rising Media, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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This book is dedicated to Colleen.
Foreword
Thomas H. Davenport
Distinguished Professor, Babson College and Research Fellow, MIT Author of Competing on Analytics and Only Humans Need Apply
Forewords to books can play a variety of roles. One is to describe in more general terms what the book is about. That's not really necessary, since Jim Sterne is a master at communicating complex topics in relatively simple terms.
Another common purpose is to describe how the book fits into the broader literature on the topic. That doesn't seem necessary in this case, either, since there isn't much literature on artificial intelligence (AI) for marketing, and even if there were, you've probably turned to this book to get one easy‐to‐consume source.
A third possible objective for forewords is to persuade you of the importance and relevance of the book, with the short‐term goal of having you actually buy it or read onward if you already bought it. I'll adopt that goal, and provide external testimony that AI already is important to marketing, that it will become much more so in the future, and that any good marketing executive needs to know what it can do.
It's not that difficult to argue that marketing in the future will make increasing use of AI. Even today, the components of an AI‐based approach are largely in place. Contemporary marketing is increasingly quantitative, targeted, and tied to business outcomes. Ads and promotions are increasingly customized to individual consumers in real time. Companies employ multiple channels to get to customers, but all of them increasingly employ digital content. Company marketers still work with agencies, many of which have developed analytical capabilities of their own.
As Sterne points out, data is the primary asset for AI‐based marketing approaches. Data for marketing comes from a company's own systems, agencies, third‐party syndicators, customer online behaviors, and many other sources—and certainly comprises “big data” in the aggregate. About 25 percent of today's marketing budgets are devoted to digital channels, and almost 80 percent of marketing organizations make technology‐oriented capital expenditures—typically hardware and software—according to a recent Gartner survey. Clearly some of that capital will be spent on AI.
Companies still try to maintain a consistent brand image, but the annual marketing strategy is increasingly a relic of the past. Instead of making a few major decisions each year, companies or their agencies make literally thousands of real‐time decisions a day about which ads to run on which sites, which search terms to buy, which version of a website to adopt, and so forth. Even the choice of what service providers and marketing software vendors to work with is complex enough to deserve a decision‐making algorithm.
Already there are simply too many decisions involving too many complex variables and too much data for humans to make all of them. Marketing activities and decisions are increasing far more rapidly than marketing budgets or the numbers and capabilities of human marketers. An increasing number of marketing decisions employ some sort of AI, and this trend will only increase.
Companies are typically trying to define and target specific customers or segments, and if there are thousands or millions of customers, AI is needed to get to that level of detail. Companies also want to customize the experience of the customer, and that also requires machine learning or some other form of AI. AI can also help to deliver value across omnichannel customer relationships, and to ensure effective communications at all customer touchpoints. Finally, AI can help companies make decisions with similar criteria across the digital and analog marketing worlds.
Today, AI in marketing supports only certain kinds of decisions. They are typically repetitive decisions based on data, and each decision has low monetary value (though in total they add up to large numbers). AI‐based decisions today primarily involve digital content and channels or online promotions. Of course, almost all content is becoming digitized, so it makes for a pretty big category. This set of AI‐supported activities includes digital advertising buys (called programmatic buying), website operation and optimization, search engine optimization, A/B testing, outbound e‐mail marketing, lead filtering and scoring, and many other marketing tasks.
And it seems highly likely that this list will continue to grow. Television advertising—the mainstay of large companies' marketing activities for many years—is moving toward a programmatic buying model. Creative brand development activities are still largely done by humans, but the decisions about which images and copy will be adopted are now sometimes made through AI‐based testing. High‐level decisions about marketing mix and resource allocation are still ultimately made by marketing executives, but they are usually done with software and are often performed more frequently than annually. It would not surprise me to see tasks such as selecting agency partners and making employee hiring decisions made through the use of AI in the future.
These AI‐based marketing activities have yet to displace large numbers of human marketers, in part because AI supports individual tasks, rather than entire jobs. But they are likely to have a substantial impact on marketing roles in the future. At a minimum, most marketers will need to understand how these systems work, to identify whether they are doing a good job, and to determine how they can add value to the work of smart machines. Leaders of marketing organizations will need to strategize effectively about the division of labor between humans and machines. They'll have to redesign marketing processes to take advantage of the speed and precision that AI‐based decision making offers.
In short, we face a marketing future in which artificial intelligence will play a very important role. I hope that these introductory comments have provided you with the motivation to commit to this book—to buying it, to reading it, and to putting its ideas to work within your organization. I believe there is a bright future for human marketers, but only if they take the initiative to learn about AI and how it can affect and improve their work. This book is the easiest and best way you will find to achieve that objective.
Preface
If you're in marketing, AI is a powerful ally.
If you're in data science, marketing is a rich problem set.
Artificial Intelligence (AI) had a breakthrough year in 2016, not only with machine learning, but with public awareness as well. And it's only going to continue. This year, most marketers believe consumers are ready for the technology.
“Artificial Intelligence Roundup,” eMarketer, February 2017
AI IN A NUTSHELL
Artificial intelligence (AI) is the next, logical step in computing: a program that can figure out things for itself. It's a program that can reprogram itself.
The Three Ds of Artificial Intelligence
The shorthand for remembering what's special about AI is that it can detect, deliberate, and develop—all on its own.
Detect
Artificial intelligence can discover which elements or attributes in a bunch of data are the most predictive. Even when there is a massive amount of data made up of lots of different kinds of data, AI can identify the most revealing characteristics, figuring out which to pay attention to and which to ignore.
Deliberate
AI can infer rules about the data, from that data, and weigh the most predictive attributes against each other to answer a question or make a recommendation. It can ponder the relevance of each and reach a conclusion.
Develop
AI can grow and mature with each iteration. It can alter its opinion about the environment as well as how it evaluates that environment based on new information or the results of experimentation. It can program itself.
An individual's search terms are more important than her location, which is more important than her age (detect). When people use six or more words in a search, their propensity to purchase is so high that a discount is counterproductive (deliberate). Once it is noted that women under the age of 24 are not likely to purchase, regardless of words in a search, an experiment can be run to offer them free shipping (develop).
THIS IS YOUR MARKETING ON AI
The tools are not supernatural. They are not beyond the understanding of mortals. You owe it to yourself to understand how they are about to rock your world.
Intelligence is the ability to adapt to change.
—Stephen Hawking
The companion website for Artificial Intelligence for Marketing: Practical Applications can be found at: AI4Marketing.com.
Acknowledgments
I am forever grateful to the many people who have blogged, tweeted, published videos on, and answered my questions about artificial intelligence and machine learning.
Specifically, thanks go to Barry Levine, Bob Page, Brent Dykes, Brian Solis, Christopher Berry, Dan McCarthy, Dave Smith, David Raab, Dean Abbott, Dennis Mortensen, Doc Searls, Eric Siegel, Gary Angel, Himanshu Sharma, Ian Thomas, Kaj van de Loo, Mark Gibbs, Matt Gershoff, Matthew Todd, Michael Rappa, Michael Wu, Michelle Street, Pat LaPointe, Peter Fader, Rohit Rudrapatna, Ron Kohavi, Russ Klein, Russell McAthy, Scott Brinker, Scott Litman, Tim Wilson, Tom Cunniff, Tom Davenport, Tom Mitchell, Tyler Vigen, Vicky Brock, and Vincent Granville.
And, as always, Matt Cutler.
CHAPTER 1Welcome to the Future
The shovel is a tool, and so is a bulldozer. Neither works on its own, “automating” the task of digging. But both tools augment our ability to dig.
Dr. Douglas Engelbart, “Improving Our Ability to Improve”1
Marketing is about to get weird. We've become used to an ever‐increasing rate of change. But occasionally, we have to catch our breath, take a new sighting, and reset our course.
Between the time my grandfather was born in 1899 and his seventh birthday:
Theodore Roosevelt took over as president from William McKinley.
Dr. Henry A. Rowland of Johns Hopkins University announced a theory about the cause of the Earth's magnetism.
L. Frank Baum's
The Wonderful Wizard of Oz
was published in Chicago.
The first zeppelin flight was carried out over Lake Constance near Friedrichshafen, Germany.
Karl Landsteiner developed a system of blood typing.
The Ford Motor Company produced its first car—the Ford Model A.
Thomas Edison invented the nickel‐alkaline storage battery.
The first electric typewriter was invented by George Canfield Blickensderfer of Erie, Pennsylvania.
The first radio that successfully received a radio transmission was developed by Guglielmo Marconi.
The Wright brothers flew at Kitty Hawk.
The Panama Canal was under construction.
Benjamin Holt invented one of the first practical continuous tracks for use in tractors and tanks.
The Victor Talking Machine Company released the Victrola.
The Autochrome Lumière, patented in 1903, became the first commercial color photography process.
My grandfather then lived to see men walk on the moon.
In the next few decades, we will see:
Self‐driving cars replace personally owned transportation.
Doctors routinely operate remote, robotic surgery devices.
Implantable communication devices replace mobile phones.
In‐eye augmented reality become normalized.
Maglev elevators travel sideways and transform building shapes.
Every surface consume light for energy and act as a display.
Mind‐controlled prosthetics with tactile skin interfaces become mainstream.
Quantum computing make today's systems microscopic.
3‐D printers allow for instant delivery of goods.
Style‐selective, nanotech clothing continuously clean itself.
And today's youngsters will live to see a colony on Mars.
It's no surprise that computational systems will manage more tasks in advertising and marketing. Yes, we have lots of technology for marketing, but the next step into artificial intelligence and machine learning will be different. Rather than being an ever‐larger confusion of rules‐based programs, operating faster than the eye can see, AI systems will operate more inscrutably than the human mind can fathom.
WELCOME TO AUTONOMIC MARKETING
The autonomic nervous system controls everything you don't have to think about: your heart, your breathing, your digestion. All of these things can happen while you're asleep or unconscious. These tasks are complex, interrelated, and vital. They are so necessary they must function continuously without the need for deliberate thought.
That's where marketing is headed. We are on the verge of the need for autonomic responses just to stay afloat. Personalization, recommendations, dynamic content selection, and dynamic display styles are all going to be table stakes.
The technologies seeing the light of day in the second decade of the twenty‐first century will be made available as services and any company not using them will suffer the same fate as those that decided not to avail themselves of word processing, database management, or Internet marketing. And so, it's time to open up that black box full of mumbo‐jumbo called artificial intelligence and understand it just well enough to make the most of it for marketing. Ignorance is no excuse. You should be comfortable enough with artificial intelligence to put it to practical use without having to get a degree in data science.
WELCOME TO ARTIFICIAL INTELLIGENCE FOR MARKETERS
It is of the highest importance in the art of detection to be able to recognize, out of a number of facts, which are incidental and which vital.
Sherlock Holmes, The Reigate Squires
This book looks at some current buzzwords to make just enough sense for regular marketing folk to understand what's going on.
This is no deep exposé on the dark arts of artificial intelligence.
This is no textbook for learning a new type of programming.
This is no exhaustive catalog of cutting‐edge technologies.
This book is not for those with advanced math degrees or those who wish to become data scientists. If, however, you are inspired to delve into the bottomless realm of modern systems building, I'll point you to “How to Get the Best Deep Learning Education for Free”2 and be happy to take the credit for inspiring you. But that is not my intent.
You will not find passages like the following in this book:
Monte‐Carlo simulations are used in many contexts: to produce high quality pseudo‐random numbers, in complex settings such as multi‐layer spatio‐temporal hierarchical Bayesian models, to estimate parameters, to compute statistics associated with very rare events, or even to generate large amount of data (for instance cross and auto‐correlated time series) to test and compare various algorithms, especially for stock trading or in engineering.
“24 Uses of Statistical Modeling” (Part II)3
You will find explanations such as: Artificial intelligence is valuable because it was designed to deal in gray areas rather than crank out statistical charts and graphs. It is capable, over time, of understanding context.
The purpose of this tome is to be a primer, an introduction, a statement of understanding for those who have regular jobs in marketing—and would like to keep them in the foreseeable future.
Let's start with a super‐simple comparison between artificial intelligence and machine learning from Avinash Kaushik, digital marketing evangelist at Google: “AI is an intelligent machine and ML is the ability to learn without being explicitly programmed.”
Artificial intelligence is a machine pretending to be a human. Machine learning is a machine pretending to be a statistical programmer. Managing either one requires a data scientist.
An ever‐so‐slightly deeper definition comes from E. Fredkin University professor at the Carnegie Mellon University Tom Mitchell:4
The field of Machine Learning seeks to answer the question, “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”
A machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E. Depending on how we specify T, P, and E, the learning task might also be called by names such as data mining, autonomous discovery, database updating, programming by example, etc.
Machine learning is a computer's way of using a given data set to figure out how to perform a specific function through trial and error. What is a specific function? A simple example is deciding the best e‐mail subject line for people who used certain search terms to find your website, their behavior on your website, and their subsequent responses (or lack thereof) to your e‐mails.
The machine looks at previous results, formulates a conclusion, and then waits for the results of a test of its hypothesis. The machine next consumes those test results and updates its weighting factors from which it suggests alternative subject lines—over and over.
There is no final answer because reality is messy and ever changing. So, just like humans, the machine is always accepting new input to formulate its judgments. It's learning.
The “three Ds” of artificial intelligence are that it can detect, decide, and develop.
Detect
AI can discover which elements or attributes in a subject matter domain are the most predictive. Even with a great deal of noisy data and a large variety of data types, it can identify the most revealing characteristics, figuring out which to heed to and which to ignore.
Decide
AI can infer rules about data, from the data, and weigh the most predictive attributes against each other to make a decision. It can take an enormous number of characteristics into consideration, ponder the relevance of each, and reach a conclusion.
Develop
AI can grow and mature with each iteration. Whether it is considering new information or the results of experimentation, it can alter its opinion about the environment as well as how it evaluates that environment. It can program itself.
WHOM IS THIS BOOK FOR?
This is the sort of book data scientists should buy for their marketing colleagues to help them understand what goes on in the data science department.
This is the sort of book marketing professionals should buy for their data scientists to help them understand what goes on in the marketing department.
This book is for the marketing manager who has to respond to the C‐level insistence that the marketing department “get with the times” (management by in‐flight magazine).
This book is for the marketing manager who has finally become comfortable with analytics as a concept, and learned how to become a dexterous consumer of analytics outputs, but must now face a new educational learning curve.
This book is for the rest of us who need to understand the big, broad brushstrokes of this new type of data processing in order to understand where we are headed in business.
This book is for those of us who need to survive even though we are not data scientists, algorithm magicians, or predictive analytics statisticians.
We must get a firm grasp on artificial intelligence because it will be our jobs to make use of it in ways that raise revenue, lower costs, increase customer satisfaction, and improve organizational capabilities.
THE BRIGHT, BRIGHT FUTURE
Artificial intelligence will give you the ability to match information about your product with the information your prospective buyers need at the moment and in a format they are most likely to consume it most effectively.
I came across my first seemingly self‐learning computer system when I was selling Apple II computers in a retail store in Santa Barbara in 1980. Since then, I've been fascinated by how computers can be useful in life and work. I was so interested, in fact, that I ended up explaining (and selling) computers to companies that had never had one before, and programming tools to software engineers, and consulting to the world's largest corporations on how to improve their digital relationships with customers through analytics.
Machine learning offers so much power and so much opportunity that we're in the same place we were with personal computers in 1980, the Internet in 1993, and e‐commerce when Amazon.com began taking over e‐commerce.
In each case, the promise was enormous and the possibilities were endless. Those who understood the impact could take advantage of it before their competitors. But the advantage was fuzzy, the implications were diverse, and speculations were off the chart.
The same is true of AI today. We know it's powerful and we know it's going to open doors we had not anticipated. There are current examples of marketing departments experimenting with some good and some not‐so‐good outcomes, but the promise remains enormous.
In advertising, machine learning works overtime to get the right message to the right person at the right time. The machine folds response rates back into the algorithm, not just the database. In the realm of customer experience, machine learning rapidly produces and takes action on new data‐driven insights, which then act as new input for the next iteration of its models. Businesses use the results to delight customers, anticipate needs, and achieve competitive advantage.
Consider the telecommunications company that uses automation to respond to customer service requests quicker or the bank that uses data on past activity to serve up more timely and relevant offers to customers through e‐mail or the retail company that uses beacon technology to engage its most loyal shoppers in the store.
Don't forget media companies using machine learning to track customer preference data to analyze viewing history and present personalized content recommendations. In “The Age of Analytics: Competing in a Data‐Driven World,”5 McKinsey Global Institute studied the areas in a dozen industries that were ripe for disruption by AI. Media was one of them. (See Figure 1.1.)6
Figure 1.1 A McKinsey survey finds advertising and marketing highly ranked for disruption.
IS AI SO GREAT IF IT'S SO EXPENSIVE?
As you are an astute businessperson, you are asking whether the investment is worth the effort. After all, this is experimental stuff and Google is still trying to teach a car how to drive itself.
Christopher Berry, Director of Product Intelligence for the Canadian Broadcasting Corporation, puts the business spin on this question.7
Look at the progress that Google has made in terms of its self‐driving car technology. They invested years and years and years in computer vision, and then training machines to respond to road conditions. Then look at the way that Tesla has been able to completely catch up by way of watching its drivers just use the car.
The emotional reaction that a data scientist is going to have is, “I'm building machine to be better than a human being. Why would I want to bring a machine up to the point of it being as bad as a human being?”
The commercial answer is that if you can train a generic Machine Learning algorithm well enough to do a job as poorly as a human being, it's still better than hiring an expensive human being because every single time that machine runs, you don't have to pay its pension, you don't have to pay its salary, and it doesn't walk out the door and maybe go off to a competitor.
And there's a possibility that it could surpass a human intelligence. If you follow that argument all the way through, narrow machine intelligence is good enough for problem subsets that are incredibly routine.
We have so many companies that are dedicated to marketing automation and to smart agents and smart bots. If we were to enumerate all the jobs being done in marketing department and score them based on how much pain caused, and how esteemed they are, you'd have no shortage of start‐ups trying to provide the next wave of mechanization in the age of information.
And heaven knows, we have plenty of well‐paid people spending a great deal of time doing incredibly routine work.
So machine learning is great. It's powerful. It's the future of marketing. But just what the heck is it?
WHAT'S ALL THIS AI THEN?
What are AI, cognitive computing, and machine learning? In “The History of Artificial Intelligence,”8 Chris Smith introduces AI this way:
The term artificial intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on the subject. But the journey to understand if machines can truly think began much before that. In Vannevar Bush's seminal work As We May Think (1945) he proposed a system which amplifies people's own knowledge and understanding. Five years later Alan Turing wrote a paper on the notion of machines being able to simulate human beings and the ability to do intelligent things, such as play Chess (1950).
In brief—AI mimics humans, while machine learning is a system that can figure out how to figure out a specific task. According to SAS, multinational developer of analytics software, “Cognitive computing is based on self‐learning systems that use machine‐learning techniques to perform specific, humanlike tasks in an intelligent way.”9
THE AI UMBRELLA
We start with AI, artificial intelligence, as it is the overarching term for a variety of technologies. AI generally refers to making computers act like people. “Weak AI” is that which can do something very specific, very well, and “strong AI” is that which thinks like humans, draws on general knowledge, imitates common sense, threatens to become self‐aware, and takes over the world.
We have lived with weak AI for a while now. Pandora is very good at choosing what music you might like based on the sort of music you liked before. Amazon is pretty good at guessing that if you bought this, you might like to buy that. Google's AlphaGo beat Go world champion Lee Sedol in March 2016. Another AI system (DeepStack) beat experts at no‐limit, Texas Hold'em Poker.10 But none of those systems can do anything else. They are weak.
Artificial intelligence is a large umbrella. Under it, you'll find visual recognition (“That's a cat!”), voice recognition (you can say things like, “It won't turn on” or “It won't connect to the Internet” or “It never arrived”), natural language processing (“I think you said you wanted me to open the garage door and warm up your car. Is that right?”), expert systems (“Based on its behavior, I am 98.3% confident that is a cat”), affective computing (“I see cats make you happy”), and robotics (I'm acting like a cat).
THE MACHINE THAT LEARNS
The magic of machine learning is that it was designed to learn, not to follow strict rules. This is the most fundamental aspect to understand and the most important to remember when you hit that inevitable frustration when things start going slightly off‐track. A rules‐based system does exactly what it's told and nothing more. We are comforted by that. A command to send out a gazillion e‐mails with the “<first_name>” after the salutation does precisely that. That's good.
Of course, when the database has something fishy in the first_name field, then somebody gets an e‐mail that begins, “Hello, Null, how are you?” Once humans know to look for those sorts of mistakes, we create processes to check and correct the data before hitting Send the next time.
When a batch of e‐mails goes out that all say, “Hello, <first_name>, how are you?” and the e‐mails all include those brackets and that underline, we know to flail the programmers until they find the errant semicolon that caused the problem. In both cases, we can backtrack, find the problem, and fix it.
Machine learning has data and is given a task. It performs the task and waits for feedback. If the feedback is positive, it tries to duplicate the same actions the next time for a similar task. If the feedback is negative, it requires information on where it went wrong.
Guess the Animal
In 1980, at the Personal Electronics store in Santa Barbara, California, I was shown a program that could guess the animal you had in mind. I was flabbergasted. It was a simple case of Asimov's “Any sufficiently advanced technology is indistinguishable from magic.”
It: Think of an animal…
Is it an elephant? (y or n)
Me: n
It: You win. Help me learn from my mistake before you go … What animal were you thinking of?
Me: a rabbit
It: Give me a question to distinguish a rabbit from an elephant.
Me: Is it a small animal?
It: For a rabbit, what is the answer to your question? (y or n)
Me: y
It: Thanks.
Play again? (y or n)
Me: y
It: Think of an animal …
Is it a small animal? (y or n)
Me: y
It: Is it a rabbit? (y or n)
Me: n
It: You win. Help me learn from my mistake before you go … What animal were you thinking of?
Me: a frog
It: Give me a question to distinguish a frog from rabbit and an elephant.
Me: Is it green?
It: Thanks.
Play again? (y or n)
Me: No, I see how this works now.
It: I don't understand.
Play again? (y or n)
Me: n
It: Goodbye
Is this program rudimentary? You bet. Was this machine learning? Almost.
After running again and again, the game could guess exactly what animal you had in mind after only a few questions. It was impressive, but it was just following programmed logic. It was not learning. Guess the Animal could update its rules‐based database and appear to be getting smarter as it went along, but it did not change how it made decisions.
The Machine that Programs Itself
Machine learning systems look for patterns and try to make sense of them. It all starts with the question: What problem are you trying to solve?
Let's say you want the machine to recognize a picture of a cat. Feed it all the pictures of cats you can get your hands on and tell it, “These are cats.” The machine looks through all of them, looking for patterns. It sees that cats have fur, pointy ears, tails, and so on, and waits for you to ask a question.
“How many paws does a cat have?”
“On average, 3.24.”
That's a good, solid answer from a regular database. It looks at all the photos, adds up the paws, and divides by the number of pictures.
But a machine learning system is designed to learn. When you tell the machine that most cats have four paws, it can “realize” that it cannot see all of the paws. So when you ask,
“How many ears does a cat have?”
“No more than two.”
the machine has learned something from its experience with paws and can apply that learning to counting ears.
The magic of machine learning is building systems that build themselves. We teach the machine to learn how to learn. We build systems that can write their own algorithms, their own architecture. Rather than learn more information, they are able to change their minds about the data they acquire. They alter the way they perceive. They learn.
The code is unreadable to humans. The machine writes its own code. You can't fix it; you can only try to correct its behavior.
It's troublesome that we cannot backtrack and find out where a machine learning system went off the rails if things come out wrong. That makes us decidedly uncomfortable. It is also likely to be illegal, especially in Europe.
“The EU General Data Protection Regulation (GDPR) is the most important change in data privacy regulation in 20 years” says the homepage of the EU GDPR Portal.11 Article 5, Principles Relating to Personal Data Processing, starts right out with:
Personal Data must be:
* processed lawfully, fairly, and in a manner transparent to the data subject
* collected for specified, explicit purposes and only those purposes
* limited to the minimum amount of personal data necessary for a given situation
* accurate and where necessary, up to date
* kept in a form that permits identification of the data subject for only as long as is necessary, with the only exceptions being statistical or scientific research purposes pursuant to article 83a
* Parliament adds that the data must be processed in a manner allowing the data subject to exercise his/her rights and protects the integrity of the data
* Council adds that the data must be processed in a manner that ensures the security of the data processed under the responsibility and liability of the data controller
Imagine sitting in a bolted‐to‐the‐floor chair in a small room at a heavily scarred table with a single, bright spotlight overhead and a detective leaning in asking, “So how did your system screw this up so badly and how are you going to fix it? Show me the decision‐making process!”
This is a murky area at the moment, and one that is being reviewed and pursued. Machine learning systems will have to come with tools that allow a decision to be explored and explained.
ARE WE THERE YET?
Most of this sounds a little over‐the‐horizon and science‐fiction‐ish, and it is. But it's only just over the horizon. (Quick—check the publication date at the front of this book!) The capabilities have been in the lab for a while now. Examples are in the field. AI and machine learning are being used in advertising, marketing, and customer service, and they don't seem to be slowing down.
But there are some projections that this is all coming at an alarming rate.12
According to researcher Gartner, AI bots will power 85% of all customer service interactions by the year 2020. Given Facebook and other messaging platforms have already seen significant adoption of customer service bots on their chat apps, this shouldn't necessarily come as a huge surprise. Since this use of AI can help reduce wait times for many types of interactions, this trend sounds like a win for businesses and customers alike.
The White House says it's time to get ready. In a report called “Preparing for the Future of Artificial Intelligence” (October 2016),13 the Executive Office of the President National Science and Technology Council Committee on Technology said:
The current wave of progress and enthusiasm for AI began around 2010, driven by three factors that built upon each other: the availability of big data from sources including e‐commerce, businesses, social media, science, and government; which provided raw material for dramatically improved Machine Learning approaches and algorithms; which in turn relied on the capabilities of more powerful computers. During this period, the pace of improvement surprised AI experts. For example, on a popular image recognition challenge14 that has a 5 percent human error rate according to one error measure, the best AI result improved from a 26 percent error rate in 2011 to 3.5 percent in 2015.
Simultaneously, industry has been increasing its investment in AI. In 2016, Google Chief Executive Officer (CEO) Sundar Pichai said, “Machine Learning [a subfield of AI] is a core, transformative way by which we're rethinking how we're doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we're in early days, but you will see us—in a systematic way—apply Machine Learning in all these areas.” This view of AI broadly impacting how software is created and delivered was widely shared by CEOs in the technology industry, including Ginni Rometty of IBM, who has said that her organization is betting the company on AI.
The commercial growth in AI is surprising to those of little faith and not at all surprising to true believers. IDC Research “predicts that spending on AI software for marketing and related function businesses will grow at an exceptionally fast cumulative average growth rate (CAGR) of 54 percent worldwide, from around $360 million in 2016 to over $2 billion in 2020, due to the attractiveness of this technology to both sell‐side suppliers and buy‐side end‐user customers.”15
Best to be prepared for the “ketchup effect,” as Mattias Östmar called it: “First nothing, then nothing, then a drip and then all of a sudden—splash!”
You might call it hype, crystal‐balling, or wishful thinking, but the best minds of our time are taking it very seriously. The White House's primary recommendation from the above report is to “examine whether and how (private and public institutions) can responsibly leverage AI and Machine Learning in ways that will benefit society.”
Can you responsibly leverage AI and machine learning in ways that will benefit society? What happens if you don't? What could possibly go wrong?
AI‐POCALYPSE
Cyberdyne will become the largest supplier of military computer systems. All stealth bombers are upgraded with Cyberdyne computers, becoming fully unmanned. Afterwards, they fly with a perfect operational record. The Skynet Funding Bill is passed. The system goes online August 4th, 1997. Human decisions are removed from strategic defense. Skynet begins to learn at a geometric rate. It becomes self‐aware at 2:14 a.m. Eastern time, August 29th. In a panic, they try to pull the plug.
The Terminator, Orion Pictures, 1984
At the end of 2014, Professor Stephen Hawking rattled the data science world when he warned, “The development of full artificial intelligence could spell the end of the human race…. It would take off on its own, and re‐design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded.”16
In August 2014, Elon Musk took to Twitter to express his misgivings:
“Worth reading Superintelligence by Bostrom. We need to be super careful with AI. Potentially more dangerous than nukes,” (Figure 1.2) and “Hope we're not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable.”
Figure 1.2 Elon Musk expresses his disquiet on Twitter.
In a clip from the movie Lo and Behold, by German filmmaker Werner Herzog, Musk says:
I think that the biggest risk is not that the AI will develop a will of its own, but rather that it will follow the will of people that establish its utility function. If it is not well thought out—even if its intent is benign—it could have quite a bad outcome. If you were a hedge fund or private equity fund and you said, “Well, all I want my AI to do is maximize the value of my portfolio,” then the AI could decide, well, the best way to do that is to short consumer stocks, go long defense stocks, and start a war. That would obviously be quite bad.
While Hawking is thinking big, Musk raises the quintessential Paperclip Maximizer Problem and the Intentional Consequences Problem.
The AI that Ate the Earth
Say you build an AI system with a goal of maximizing the number of paperclips it has. The threat is that it learns how to find paperclips, buy paperclips (requiring it to learn how to make money), and then work out how to manufacture paperclips. It would realize that it needs to be smarter, and so increases its own intelligence in order to make it even smarter, in service of making paperclips.
What is the problem? A hyper‐intelligent agent could figure out how to use nanotech and quantum physics to alter all atoms on Earth into paperclips.
Whoops, somebody seems to have forgotten to include the Three Laws of Robotics from Isaac Asimov's 1950 book, I Robot:
A robot may not injure a human being, or through inaction, allow a human being to come to harm.
A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
Max Tegmark, president of the Future of Life Institute, ponders what would happen if an AI
is programmed to do something beneficial, but it develops a destructive method for achieving its goal: This can happen whenever we fail to fully align the AI's goals with ours, which is strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for. If a superintelligent system is tasked with a(n) ambitious geoengineering project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it as a threat to be met.17
If you really want to dive into a dark hole of the existential problem that AI represents, take a gander at “The AI Revolution: Our Immortality or Extinction.”18
Intentional Consequences Problem
Bad guys are the scariest thing about guns, nuclear weapons, hacking, and, yes, AI. Dictators and authoritarian regimes, people with a grudge, and people who are mentally unstable could all use very powerful software to wreak havoc on our self‐driving cars, dams, water systems, and air traffic control systems. That would, to repeat Mr. Musk, obviously be quite bad.
That's why the Future of Life Institute offered “Autonomous Weapons: An Open Letter from AI & Robotics Researchers,” which concludes, “Starting a military AI arms race is a bad idea, and should be prevented by a ban on offensive autonomous weapons beyond meaningful human control.”19
In his 2015 presentation on “The Long‐Term Future of (Artificial) Intelligence,” University of California, Berkeley professor Stuart Russell asked, “What's so bad about the better AI? AI that is incredibly good at achieving something other than what we really want.”
Russell then offered some approaches to managing the it's‐smarter‐than‐we‐are conundrum. He described AIs that are not in control of anything in the world, but only answer a human's questions, making us wonder whether it could learn to manipulate the human. He suggested creating an agent whose only job is to review other AIs to see if they are potentially dangerous and admitted that was a bit of a paradox. He's very optimistic, however, given the economic incentive for humans to create AI systems that do not run amok and turn people into paperclips. The result will inevitably be the development of community standards and a global regulatory framework.
Setting aside science fiction fears of the unknown and a madman with a suitcase nuke, there are some issues that are real and deserve our attention.
Unintended Consequences
The biggest legitimate concern facing marketing executives when it comes to machine learning and AI is when the machine does what you tell it to do rather than what you wanted it to do. This is much like the paperclip problem, but much more subtle. In broad terms, this is known as the alignment problem. The alignment problem wonders how to explain to an AI system goals that are not absolute, but take all of human values into consideration, especially considering that values vary widely from human to human, even in the same community. And even then, humans, according to Professor Russell, are irrational, inconsistent, and weak‐willed.
The good news is that addressing this issue is actively happening at the industrial level. “OpenAI is a non‐profit artificial intelligence research company. Our mission is to build safe AI, and ensure AI's benefits are as widely and evenly distributed as possible.”20
