Big networks (Amazon, Google, Facebook, Netflix, etc.) have reset customer expectations through increasing personalization of the customer experience. This has caused a shift in business models from supply (analog) to on-demand (digital). To survive, marketers must find a way to collect first-party data that will allow them to meet these new consumer expectations. To achieve this, management must make a commitment to investing for the long term and be prepared to tolerate a temporary chasm of low or no profits. Otherwise, their brands may face bankruptcy—and they may not come out alive.
There is one way to win in this new economy, and that is with AI and machine learning, deployed against a rock-solid marketing strategy—one that is grounded in proven principles for how a firm acquires, retains, and grows its customer relationships and promotes customer advocacy. These strategies also must be driven by marketing leaders whose obsession is to find ways to use AI and machine learning to personalize the customer relationship at every juncture, providing consumers with the one-to-one personalized experience they need to find your product, buy it, buy it again, and then tell others about it. This book provides a framework called the AI Marketing Canvas, which is a road map for building an effective marketing plan for applying AI and machine learning to your marketing—so that you can win.
The book is divided into four parts comprising sixteen chapters, which lay out all the concepts and structures we think you'll need to be productive and successful as a marketer leading AI and machine-learning initiatives in your organization.
Facebook, Netflix, Amazon, and Google are examples of "networks" that provide value to consumers via their proprietary technology platforms—powered by AI. Those who make and sell tangible products, such as baked goods or tractors, through a network such as Amazon, and whose primary business model is not driven by a technology platform that connects them directly with consumers or connects consumers with each other, are "nodes." If all the big networks had done was to raise customer expectations by streamlining the buying experience, nodes probably could have continued to cope with it using upgrades in technology. To survive now, however, nodes must build their own networks, collaborate with networks, or team up with other nodes to gain parity.
The AI Marketing Canvas framework is based on a four-step Customer Relationship Moments "mental model" (Acquisition, Retention, Growth, and Advocacy), a widely known and universal customer decision journey model. Three forces are at work that have made the customer relationship difficult if not impossible to navigate without the assistance of AI and machine learning: advances in technology, customer connections, and information abundance. AI and machine learning allow marketers of nodes (brands) to curate the information customers receive, allowing them to harness all three forces to achieve a level of personalization that fosters intimacy and loyalty. How marketers respond to the current wave of one-to-one personalized marketing information and manage these three forces will determine whether they will be able to create the AI Moments required to win it all.
Data-driven AI is already at work improving customer experiences through personalization—sometimes even without the customer noticing it. And the machine-learning dimension of AI is rapidly approaching a level at which it makes better predictions on its own constantly, while you are sleeping. Knowledge of the key principles of machine learning is critical, because most of the marketing opportunities that will be available to you likely will be led by the machine-learning dimension of AI. Enjoy a clear and concise journey through the relevant history and definition of terms such as algorithms, image recognition, classification, clustering, neural networks, deep learning, application to data and analytics, and more—arming yourself with the concepts and vocabulary you need to succeed.
The AI Marketing Canvas is a one-page strategic road map you can use to add AI and machine learning to your marketing tool kit, inspired by the well-known business model canvas created by Alexander Osterwalder. It is composed of five distinct stages (foundation, experimentation, expansion, transformation, and monetization) that many top brands have gone through to successfully use AI to supercharge their customer relationship moments. Each stage offers several key questions you should be able to answer before energizing initiatives of that stage. Now, when your CEO or your team asks, "What's the plan for AI and marketing?" you can say you are following the five stages of the AI Marketing Canvas to increasingly implement AI deeper across your marketing tool kit.
The Foundation stage is about developing the ability to see what consumers are doing at every moment of truth over time, and organizing that data so that a machine can use it to learn and make predictions about what consumers want. To do this, you must get your data house—that is, your automated basic processes, structured analytics, centralized data processes, data quality processes, and solid digital infrastructure with connected databases—in order! A focus on collecting first-party data across the business, and collecting an adequate amount of quality data (customer-focused and traditional data schemas) to begin training machine learning models and supercharge customer relationship moments, is discussed. Also covered are ways to connect with the consumer directly, connecting first-party data with second- and third-party data to complete the profiles of individual consumers, and the need for good privacy practices.
First-party data in hand, you're ready to jump in and begin learning how experimenting with AI and machine-learning applications can help you with your marketing initiatives in Stage 2. Key questions include: What marketing activities could be empowered with AI for some quick learning? How can you use AI-powered tools from third parties and vendors to get some quick learning and outcomes in a few marketing activities? The key is to start small, and to focus on quick learning and the exploiting of "value pockets." Look for bright spots, and then try to replicate them. This is a new way of working that requires different skills, so be prepared to manage that change. Note: The stages are not a report card; they are part of a game board "canvas" intended to help you make the most of the resources you have.
The mission in Stage 3 is to expand the application of the machine learning you piloted and proved in Stage 2—more deeply into the same relationship moment or to an adjacent moment, where data-driven predictions and personalization will add value for consumers. How you approach Stage 3 will depend on the size and structure of your firm and your role's span of control. At this point you'll identify and appoint an AI Marketing Champion and an AI marketing team. You'll quantify impact and budget requirements, continue internal AI and machine learning activities; identify and develop an AI/ML cross-functional team; and design, test, and learn from campaigns and algorithms. When you start maxing out the existing capabilities of third-party AI providers, it will be time to create a plan to bring it in-house (with or without a strategic partner) or buy a company to help.
Stage 4 is about using AI to automate across a complete set of marketing activities, and to begin identifying strategic capabilities and move them in-house in a way that makes most sense for your firm. You're now consistently deploying AI and machine learning, but your focus is now on increasing the returns curve to compete; in the end, winner takes all. You're also looking for proprietary AI capabilities that, if developed, will set your marketing practice and business apart, or become a new source of revenue. Now is the time to create a plan to bring capabilities in-house, to partner with a vendor whose advanced development product is under marketing's control, or to buy a company—maybe even the vendor! Whether to buy a company or build in-house is a matter of examining combinations of cost, timing, and degree of difficulty, as well as company culture.
With most of the customer relationship now automated, you're ready to maximize the ways the firm can use the AI models you've created to drive profitable growth and generate new revenue streams—the core of Stage 5. At this level, AI and machine learning is used to create new business models and sources of revenue, either in the core business or by extending the model or capabilities to other businesses—thereby creating a source of revenue that can fuel additional development (the flywheel effect). Most development at this point will likely be in-house, because the models you are now creating represent a strategic and competitive advantage. Whether or not you decide to energize Stage 5, you now know how to build AI and machine learning into your marketing tool kit by energizing the five stages of AI and machine learning, using the AI Marketing Canvas as your roadmap.
When we are asked by marketing leaders for an example of a great brand example to emulate, one that is successfully combining AI, machine learning, big data, and personalized marketing to drive business results, and doing it in a way that tracks with our AI Marketing Canvas, the brand we point to is Starbucks. To give you a feel for how you might approach implementation of the five stages, we've taken a close look at Starbucks's progression using publicly available information. You'll see that, though we've represented the five stages as discrete and sequential for the purposes of explanation, the way you'll actually work through them will likely involve working within some stages concurrently—just as Starbucks does!
Becoming an AI- and machine learning–driven organization is a tough pivot for any organization—even Google. You'll learn how Google made the shift to being a machine learning–first organization, so that you can understand the changes your organization will also need to go through. Like Google, you will need to lead the shift from hand-curated marketing to machine-led marketing across four specific dimensions: people, process, culture, and profit. You'll need to assemble a different kind of marketing team with new skill sets and philosophies, with a first step being the appointment of an AI Marketing Champion "translator." You'll need to adopt new more "agile" processes that reward a "test-and-learn" mindset. You'll need to foster a values-based culture that embraces data, experimentation, probabilities, and speed. And finally, marketing and finance leaders alike will need to shift their focus from profit margin to total profit.
It's now time to choose key roles in a room for an AI in Marketing Assessment Meeting, to arrive at what stage you occupy on the AI Marketing Canvas, what you need to do to move to the next stage, who will own that effort, and on what schedule. To help you guide this discussion, we provide a quiz that touches on each AI Marketing Canvas stage, which you can use as a diagnostic tool. As you undertake these efforts, be cognizant of four significant gaps: building a digital foundation, finding the right talent, buying or building AI capabilities in-house, and the question of whether to monetize all that you've created. Then you can begin using the AI Marketing Canvas to guide strategy in the service of creating more powerful, enduring, and profitable one-to-one customer relationships.
Though the primary purpose of this book is to assist you in your role as a marketing leader by providing you with a solid strategic framework you can use to succeed with AI and marketing, we want to acknowledge what is at stake for you personally in this new world. You must decide now whether you will engage and become an expert AI marketer. In fact, this is your personal AI "moment of truth." The most important and powerful thing you can do right now to address that is to look for ways to experience how AI and machine learning work, beyond just reading about them (this book included). Finally, be aware of the trend among brands—Starbucks among them—to lean into the huge potential of AI to nurture human beings and enhances their brands' overall humanity. We hope you'll aspire to do the same.