Artificial Intelligence for Marketing. Sterne Jim

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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

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