Artificial Intelligence for Marketing

Practical Applications
Wiley (Verlag)
  • erschienen am 2. August 2017
  • |
  • 368 Seiten
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-1-119-40636-5 (ISBN)
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.
1. Auflage
  • Englisch
  • Newark
  • |
  • USA
John Wiley & Sons
  • 9,18 MB
978-1-119-40636-5 (9781119406365)
weitere Ausgaben werden ermittelt
JIM STERNE is founder of the eMetrics Summit and cofounder and Board Chair of the Digital Analytics Association. An internationally known speaker and consultant, he is the author of numerous books, including 101 Things You Should Know About Marketing Optimization Analysis, Social Media Metrics, and The Devil's Data Dictionary.
Foreword by Tom Davenport xiii
Preface xvii
Acknowledgments xix
Chapter 1 Welcome to the Future 1
Welcome to Autonomic Marketing 3
Welcome to Artificial Intelligence for Marketers 3
Whom Is This Book For? 5
The Bright, Bright Future 6
Is AI So Great if It's So Expensive? 7
What's All This AI Then? 9
The AI Umbrella 9
The Machine that Learns 10
Are We There Yet? 14
AI-pocalypse 15
Machine Learning's Biggest Roadblock 23
Machine Learning's Greatest Asset 24
Are We Really Calculable? 56
Chapter 2 Introduction to Machine Learning 59
Three Reasons Data Scientists Should Read This Chapter 59
Every Reason Marketing Professionals Should Read
This Chapter 60
We Think We're So Smart 60
Define Your Terms 61
All Models Are Wrong 62
Useful Models 64
Too Much to Think About 66
Machines Are Big Babies 68
Where Machines Shine 69
Strong versus Weak AI 71
The Right Tool for the Right Job 72
Make Up Your Mind 88
One Algorithm to Rule Them All? 89
Accepting Randomness 92
Which Tech Is Best? 94
For the More Statistically Minded 94
What Did We Learn? 101
Chapter 3 Solving the Marketing Problem 103
One-to-One Marketing 105
One-to-Many Advertising 107
The Four Ps 108
What Keeps a Marketing Professional Awake? 109
The Customer Journey 111
We Will Never Really Know 111
How Do I Connect? Let Me Count the Ways 114
Why Do I Connect? Branding 117
Marketing Mix Modeling 119
Econometrics 121
Customer Lifetime Value 121
One-to-One Marketing--The Meme 122
Seat-of-the-Pants Marketing 123
Marketing in a Nutshell 124
What Seems to Be the Problem? 126
Chapter 4 Using AI to Get Their Attention 128
Market Research: Whom Are We After? 128
Marketplace Segmentation 131
Raising Awareness 141
Social Media Engagement 155
In Real Life 158
The B2B World 158
Chapter 5 Using AI to Persuade 165
The In-Store Experience 168
On the Phone 178
The Onsite Experience--Web Analytics 179
Merchandising 186
Closing the Deal 188
Back to the Beginning: Attribution 193
Chapter 6 Using AI for Retention 200
Growing Customer Expectations 200
Retention and Churn 202
Many Unhappy Returns 204
Customer Sentiment 208
Customer Service 209
Predictive Customer Service 216
Chapter 7 The AI Marketing Platform 218
Supplemental AI 218
Marketing Tools from Scratch 221
A Word about Watson 224
Building Your Own 230
Chapter 8 Where Machines Fail 232
A Hammer Is Not a Carpenter 232
Machine Mistakes 235
Human Mistakes 241
The Ethics of AI 247
Solution? 258
What Machines Haven't Learned Yet 260
Chapter 9 Your Strategic Role in Onboarding AI 262
Getting Started, Looking Forward 264
AI to Leverage Humans 272
Collaboration at Work 274
Your Role as Manager 276
Know Your Place 282
AI for Best Practices 286
Chapter 10 Mentoring the Machine 289
How to Train a Dragon 290
What Problem Are You Trying to Solve? 291
What Makes a Good Hypothesis? 294
The Human Advantage 297
Chapter 11 What Tomorrow May Bring 305
The Path to the Future 307
Machine, Train Thyself 308
Intellectual Capacity as a Service 308
Data as a Competitive Advantage 310
How Far Will Machines Go? 316
Your Bot Is Your Brand 319
My AI Will Call Your AI 321
Computing Tomorrow 325
About the Author 327
Index 329

Welcome 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.


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.


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.


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.


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.


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.


This is the sort of book data scientists should buy for their marketing colleagues to help them understand what goes...

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