Effective marketing centers on delivering relevant information precisely when needed to advance customer progress. Yet contemporary digital marketing demands message orchestration across disparate systems, many outside marketer control. Digital marketing professionals must evolve into knowledge facilitators, data quality advocates, system architects, and stewards of the ontological frameworks powering everything.
Artificial intelligence provides powerful leverage for managing these complex elements, enabling simultaneous scaling and granular data analysis. Marketers must both understand and execute within these new digital roles while mastering concepts and practices for deploying AI to empower enterprises.
This represents substantial demands, yet systematic approaches make achievement feasible.
Knowledge facilitation encompasses diverse organizational actions. In B2B contexts, this might mean exposing engineering expertise. In B2C environments, it could mean enabling customer insights supporting product selection.
Numerous AI programs address unstructured information while replicating human task performance like answering support inquiries or personalizing customer experiences. This may require drawing information from multiple systems and integrating various processes, including historically manual activities.
Systems frequently deploy in isolation or with minimal integration through web services. However, few marketing leaders possess positions enabling development of foundational data infrastructure essential for success. If enterprises hope for positive outcomes from advanced marketing technology investments meant to smooth customer journeys, marketing leaders must streamline operations and evolve supporting processes across all tools. They must pursue this holistically through frameworks enabling system communication.
Marketing now centers on scaling communication, collaboration, and content process machinery. It involves engaging deeper organizational levels. Marketers must participate in governance and change management to ensure meaningful content creation, management, effective organization, and consumable presentation to target customers. Marketers require intimate IT process involvement, working closely with chief information officers and, where roles exist, chief data officers and chief digital officers.
Marketers must facilitate information capture and processing throughout enterprises to accomplish their work. Wherever customer-relevant knowledge resides, they require awareness, understanding of enterprise system interfaces, and ability to surface needed information at appropriate customer journey points.
Marketing increasingly depends on data, making quality essential to success. Digital quality translates into data agility. Marketers must demonstrate organizational possibilities with high-quality data while illustrating negative impacts of poor quality, difficult-to-access, or missing information. Once leadership understands how capabilities better serve customers and drive revenue, organizations conclude that data quality investment proves worthwhile.
Marketing leaders champion data quality most effectively by demonstrating bottom-line impact through metrics linked to customer acquisition and revenue growth. Metrics-driven frameworks for managing decision-making and resource allocation remove opinion from decisions and ensure investments produce value.
Ecommerce represents one customer journey aspect but generates direct revenue. Consequently, it can justify investments improving all customer experience aspects. For example, improving ecommerce content through more detailed attributes, comprehensible taxonomies, or superior images produces measurable results that can be leveraged throughout enterprises to improve multiple upstream, downstream, and adjacent processes. Ecommerce can establish foundations for metrics-driven governance—the decision-making playbook representing data-driven organization cornerstones.
Ensuring quality data demands solid governance programs. Governance programs provide proper attributes as new products onboard, monitor product change impacts to adjust attributes, and incorporate content performance metrics into governance processes.
Large companies likely possess abundant technology: systems for customers, inventory, and products, alongside websites and mobile applications. These systems continuously generate data. Within that data exists precisely the information needed to make businesses more responsive. The problem: data often isn't used as it could and should be. In many cases, technology potentially possessed functionality capabilities, but data locked in siloed systems proved inaccessible, poorly structured, or improperly curated. Digital marketing success requires companies to address foundational issues and build coherent information management ecosystems.
Digital marketers ultimately must become digital architects. Marketing functions leverage data assets from numerous enterprise areas—customer purchase histories, 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 communicate effectively with IT to create information flows needed for decision-making.
Deriving value from these sources means translating digital body language—what online behavior reveals—into meaningful content, campaigns, and offers. Increasingly, this means translating data models from various systems into attributes managed within ontologies. Those attributes become inputs into personalization engines, web content management tools, collateral creation processes, campaign management systems, and various outbound demand-generation activities.
Large-scale personalization requires continuously testing and recombining design elements, messaging, and offerings. Marketers cannot manually customize messages across hundreds or thousands of different audiences. Even accomplishing this across a handful of audiences requires extraordinary individual efforts applying brute force to such tasks. Extraordinary efforts don't scale and produce team burnout. This is where AI enters, enabling automated analyses and right content selection and presentation to each group or individual. This demands messaging architecture—like modular building blocks—enabling AI optimization across audiences by experimenting with different element combinations.
Like organisms in ecosystems, businesses consume energy and resources then create solutions and structures from those resources. Resources and results primarily take information form. Businesses function as living organisms consuming and producing information. Their agility and adaptability depend on how effectively they metabolize that information.
Consider how brains and bodies act on environmental signals and interact with the world through integrated information systems and feedback loops. When the amygdala—the brain region registering fear or desire—identifies threats, our sympathetic nervous systems controlling fight-or-flight responses react in highly orchestrated ways. Another brain region—the hypothalamus—instantly signals throughout bodies.
This signal triggers adrenal glands releasing adrenaline, causing familiar cascading responses when startled, such as when cars speed toward us in crosswalks. Heartbeats increase, breathing accelerates, and we feel energy surges. Brains also execute new computational tasks—generating appropriate expletives for drivers—and anticipate likely outcomes. Everything works holistically responding efficiently and effectively to stimuli with minimal friction.
Holistic and synchronized information flows prove essential to survival. It wouldn't help if brains had to search past memories deciding what to do. The same holistic, synergistic, simultaneously integrated information flow creates transformative AI solutions supporting contemporary marketing operations.
This leads to one conclusion: ontologies represent senior digital marketing leader responsibilities. Executives receive advice from all directions about successful digital marketing strategy requirements. Conspicuously missing from this advice: any reference to ontological foundational roles.
Ontologies represent consistent data and data relationship representations that inform and power AI technologies. In different contexts, they can include or become expressed as data models, content models, information models, data architectures, master data, or metadata. However described, ontologies prove essential to and central within AI-driven technologies. To clarify, ontologies aren't single, static things. They're never complete, changing as organizations change and as they're applied throughout enterprises.
For functionality, AI requires correct training data, including content, metadata describing data, and operational knowledge. If that data and corresponding outcomes aren't available in system-processable ways, then AI fails. Those data and outcomes only become accessible when ontologies have been developed and integrated into marketing stacks.
AI works only when understanding your business in ways allowing information processing. It needs keys unlocking that understanding. The keys unlocking that understanding are ontologies: representations of what matters within companies and makes them 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 AI promise and delivering sustainably on that promise.
Marketer roles have and will continue rapidly evolving as responsibilities extend throughout full customer journeys. Emerging AI tools and technologies hold tremendous promise but business leadership needs to return to basics, and marketers play critical roles—or multiple roles as discussed. Perhaps your organization experimented with AI, and perhaps it succeeded. More often than not, it fails meeting expectations. A major life insurance company executive recently told me that every competitor and most organizations their size in other industries spent at least a few million dollars on failed AI initiatives. In some cases, technology vendors sold aspirational capabilities—functionality not yet in current software.
But in most cases, failure causes were excessive technology reliance—overestimating truly out-of-box functionality, overly ambitious moonshot programs central to major digital transformation efforts but unattainable practically, or existing organizational processes incompatible with new AI approaches. Leadership may have embraced AI promise without adequate business front-line support.
Technology organizations may not have been adequately prepared for new tools and significant process changes. In many cases, technology potentially possessed functionality capabilities, but data locked in siloed systems proved inaccessible, poorly structured, or improperly curated.
It really comes down to basics: good data, deep organizational process understanding, and governance plans. But questioning and examining basics doesn't excite people, especially in marketing departments. If data management processes are broken, objectives often become simply fixing them without understanding why they're broken.
Marketers must participate in all governance and change management aspects to ensure content creation, management, effective organization, and consumable presentation to target customers. AI can strongly enable, but its power only matches the data it rests on and the processes driving information flow. The core premise of information architecture dependency and relationship to artificial intelligence can be summarized in one phrase: there's no AI without IA.
Online experiences comprise entirely of data. We need data correctly curated and structured with sufficient quality to power customer experiences, ecommerce, collaboration, and every traditional and emerging enterprise application. Internet pioneer Marc Andreessen stated that software is eating the world, and data powers that software. Today's marketers need to embrace these changing and varied roles in ongoing digital experience evolution and its vast enabling technology ecosystem.
This article by Seth Earley was originally published on The European Business Review on January 21, 2021.