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       Library of Congress Cataloging‐in‐Publication Data is Available:

      ISBN 9781119406334 (Hardcover)

      ISBN 9781119406372 (ePDF)

      ISBN 9781119406365 (ePub)

      Cover Design: Wiley

      Cover Image: © Kngkyle2/Getty Images

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

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