Welcome to AI marketing world


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


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 mar- keting, but the next step into artificial intelligence and machine learn- ing 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 func- tion 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, recom- mendations, 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 com- pany 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)


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 intel- ligence 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 program- mer. 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:


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 pre- dictive 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 consider- ing new information or the results of experimentation, it can alter its opinion about the environment as well as how it evaluates that envi- ronment. It can program itself.


If you want to konw more, please buy this book online: https://amzn.to/2P53Mag

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