This article originally appeared on The European Business Review on January 21, 2021
Successful marketing depends on delivering the appropriate information at just the right moment to move the user to the next step in their journey. However, agile digital marketing requires orchestrating messages across diverse systems, not all of which are controlled by marketers. Digital marketers must therefore become knowledge enablers, champions of data quality, architects of digital systems, and keepers of the ontology that powers it all.
Artificial intelligence (AI) is a powerful tool for leveraging these complex elements because it enables scaling up at the same time as it allows more granular analysis of data. So not only must marketers understand and work within their new digital roles, they must also master at least the concepts if not the practices entailed in using AI to empower the enterprise.
This is a tall order, but taking it step by step will get you there.
Marketer as Knowledge Enabler
Being a knowledge enabler covers a wide range of potential actions in an organization. It may mean exposing the knowledge and expertise of engineers in a B2B organization, or it may mean enabling customers to have the insights they need to choose your product in a B2C context.
Many AI programs attempt to deal with unstructured information and replicate how humans perform certain tasks, such as answering support questions or personalizing a customer experience. That may require pulling information from multiple systems and weaving together multiple processes, including some that have historically been done manually.
Systems are often deployed in isolation or with a nod to integration using web services; however, very few marketing leaders are in a position to develop the foundational data infrastructure that is needed for success. If the enterprise is to have any hope of a positive outcome from all of the investments being made in advanced marketing technologies that are meant to smooth the customer journey, marketing leaders have to streamline marketing operations and evolve supporting processes across all of their tools. They must pursue this effort in a holistic way that includes a framework so all of these systems can communicate.
Marketing is now about scaling the machinery of communication, collaboration, and content processes. It is about enrolling deeper levels of the organization in the process. This means marketers must be involved in various aspects of governance and change management to get meaningful content created, managed, organized effectively, and presented to target customers in a consumable way. Marketers also need to be intimately involved in IT processes. They need to work closely with the CIO (chief information officer), and, if the role exists, the CDO(s) (chief data officer and/or chief digital officer).
In short, marketers need to facilitate the capture and processing of information throughout the enterprise in order to do their job. Wherever knowledge relevant to the customer experience resides, they need to be aware of it, understand how it interfaces with other enterprise systems, and surface what is needed at the right point in the customer journey.
Marketer as Data Quality Champion
Marketing is increasingly data-driven, and data quality will be essential to marketing success. Digital quality translates into data agility. Marketers need to show the organization what can be done with high-quality data as well as demonstrate the negative impact of poor quality, difficult to access, or missing data. Once leadership understands how capabilities will better serve the customer and lead to increased revenue, the organization will conclude that investment in data quality is worthwhile.
The best way for marketing leaders to champion data quality to show the impact on the bottom line through metrics linked to customer acquisition and revenue growth. A metrics-driven framework for managing decision-making and resource allocation removes opinion from decision making and ensures that the investments will produce value.
Ecommerce is just one aspect of the customer journey, but it generates revenue directly. As a result, it can be used to justify investments that in fact improve all aspects of the customer experience. For example, if ecommerce content is improved by more detailed attributes, a more understandable taxonomy, or better images, the results can be measured and those improvements can be leveraged in other parts of the enterprise to improve multiple upstream, downstream and adjacent processes. Ecommerce can create the foundation for metrics-driven governance, the decision-making playbook that is the cornerstone of a data-driven organization.
The only way to ensure good quality data is to establish a solid governance program. A governance program will provide the proper attributes as new products are on-boarded, monitor the impact of changes in products to adjust those attributes, and include content performance metrics in governance processes.
Any big company is likely to have an abundance of technology. It has systems for customers, inventory, and products, along with websites and mobile apps. These systems are spitting out data all day long. Within that data is exactly the information needed to make a business more responsive. The problem is, the data is often not used as it could (and should) be. In many cases, the technology may have been potentially capable of functionality, but the data, locked in siloed systems, was inaccessible, poorly structured, or improperly curated. To succeed in digital marketing, companies must be prepared to address foundational issues and build a coherent information management ecosystem.
Marketer as Architect of Digital Systems
Ultimately, digital marketers need to become digital architects. The marketing function leverages data assets from many parts of the enterprise—from customer purchase histories to call center feedback, survey responses, social media data, clickstream behaviors, campaign responses, external data feeds, mobile usage data, and search metrics. Marketers must understand all these functions and be able to communicate effectively with IT to create the information flow they need for decision-making.
Deriving value from these sources means translating this “digital body language” (i.e., what the person’s online behavior is telling us) into meaningful content, campaigns, and offers. Increasingly, this means translating data models from various systems into attributes managed in the ontology. Those attributes become inputs into personalization engines, web content management tools, collateral creation processes, campaign management systems, and various outbound demand-generation activities.
Personalization on a large scale requires a process of continuously testing and recombining elements of design, messaging, and offerings. Marketers cannot manually customize messages across hundreds or thousands of different audiences. Even doing so on the scale of a handful of audiences requires “acts of heroics” to apply brute force to such a task. “Acts of heroics” do not scale and lead to team burnout. This is where AI comes in, so that analyses can become automated and the right content can be selected and presented to each group or even each individual. To do this, a messaging architecture (like Lego building blocks) is needed, so that the AI can optimize across these audiences by trying out different combinations of elements.
Just like organisms in an ecosystem, businesses consume energy and resources and then create solutions and structures from those resources. The resources and results primarily take the form of information. Businesses are in fact living organisms that consume and produce information. Their agility and adaptability depend on how effectively they metabolize that information.
For example, consider how our brains and bodies act on signals from the environment and interact with the world based on integrated information systems and feedback loops. When the amygdala (the part of the brain that registers fear or desire) identifies a threat, our sympathetic nervous system (which controls the “fight or flight” response) reacts in a highly orchestrated way. Another part of the brain—the hypothalamus—instantly sends a signal throughout the body.
This signal triggers the adrenal glands to release adrenaline, which causes a cascade of responses that we are all familiar with from instances when we are startled, such as if a car speeds toward us as we step into a crosswalk. The heartbeat increases, breathing becomes more rapid, and we feel a surge of energy. The brain also executes a new computational task—coming up with the appropriate expletives to hurl at the driver—and anticipates likely outcomes. Everything works holistically to respond efficiently and effectively to the stimulus, with very little friction.
It’s easy to see why holistic and synchronized information flows are essential to survival. It would not do us much good if the brain had to rummage around our past memories and try to decide what to do. The same kind of holistic, synergistic, and simultaneously integrated flow of information is also what’s needed to create transformative AI solutions that support today’s marketing operations.
Marketer as “Ontoloogist”
All of this leads to one conclusion: the ontology is very much the responsibility of the senior leaders in digital marketing. Executives are getting advice from all quarters about what they need to do to have a successful digital marketing agency strategy. What is conspicuously missing from this advice is any reference to the foundational role of ontologies.
An ontology is a consistent representation of data and data relationships that can inform and power AI technologies. In different contexts, it can include or become expressed as a data model, a content model, an information model, a data/content/information architecture, master data, or metadata. However you describe it, the ontology is essential to and at the heart of AI-driven technologies. To be clear, an ontology is not a single, static thing; it is never complete, and it changes as the organization changes and as it is applied throughout the enterprise.
In order to function, AI needs the correct “training data,” including content, metadata (descriptions of data), and operational knowledge. If that data and corresponding outcomes are not available in a way that the system can process, then the AI will fail. Those data and outcomes only become accessible when an ontology has been developed and integrated into the marketing stack.
AI works only when it understands your business in a way that allows it to process information. It needs the key that unlocks that understanding. The key that unlocks that understanding is an ontology: a representation of what matters within the company and makes it unique, including products and services, solutions and processes, organizational structures, protocols, customer characteristics, manufacturing methods, knowledge, content and data of all types. It’s a concept that, correctly built, managed, and applied, makes the difference between the promise of AI and delivering sustainably on that promise.
The Marketer’s Mandate
The marketer’s role has and will continue to rapidly evolve as their responsibilities extend throughout the full customer journey. Emerging AI tools and technologies have tremendous promise but business leadership need to get back to the basics, and the marketer plays a critical role (or multiple roles as we’ve discussed). Perhaps your organization has experimented with AI, and perhaps it worked out well. But more often than not, it fails to live up to expectations. An executive at a major life insurance company recently told me, “Every one of our competitors and most of the organizations of our size in other industries have spent at least a few million dollars on failed AI initiatives.” In some cases, technology vendors have sold “aspirational capabilities”—functionality that was not yet in the current software.
But in most cases, the cause of the failure was too much reliance on technology—overestimation of what was truly “out-of-the-box” functionality, overly ambitious “moonshot” programs that were central to major digital transformation efforts but unattainable in practice, or existing organizational processes incompatible with new AI approaches. Leadership may have bought into the promise of AI without adequate support from the front lines of the business.
Technology organizations may not have been adequately prepared to take on new tools and significant process changes. In many cases, the technology may have been potentially capable of functionality, but the data, locked in siloed systems, was inaccessible, poorly structured, or improperly curated.
So it really comes down to the basics of having good data, a deep understanding of organizational processes, and a governance plan. But questioning and examining the basics does not get people excited, especially in the marketing department. If the process for managing data is broken, the objective often just “just fix it,” without an understanding of why it is broken.
Marketers must be involved in all aspects of governance and change management to ensure that content is created, managed, organized effectively, and presented to target customers in a consumable way. AI can be a strong enabler, but its power is only as strong as the data it rests on and the processes that drive the flow of information. The core premise of the dependency and relationship of information architecture (IA) to artificial intelligence (AI) can be summed up in one pithy phrase: “There’s no AI without IA.”
The online experience is comprised entirely of data. We need the data to be correctly curated and structured and of sufficient quality to power customer experience, ecommerce, collaboration and every traditional and emerging application in the enterprise. Internet pioneer Marc Andreessen stated that “software is eating the world,” and data powers that software. Today’s marketer needs to embrace these changing and varied roles in the ongoing evolution of the digital experience and its vast enabling technology ecosystem.