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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

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