This article was based on the interview with Andrew Frawley, CEO at Data Axle by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
We’ve all been in the room. The one where the promise of true one-to-one, omnichannel marketing is painted in vibrant colors on a PowerPoint slide. The vision is compelling: a seamless customer journey where every interaction is relevant, personalized, and builds upon the last. It’s the north star we’ve been navigating towards for years. Yet, for many enterprise organizations, the reality on the ground is far messier. Campaigns operate in silos, the customer view is fragmented, and the data, the very bedrock of this vision, is often more of a liability than an asset. The pursuit of agility can feel like a frantic chase, reacting to market shifts rather than anticipating them.
The disconnect between the vision and the reality almost always traces back to a foundational, often-underappreciated discipline: identity resolution. Before we can dream of leveraging generative AI to create bespoke content or deploy sophisticated predictive models, we must answer a deceptively simple question: who is our customer? Not just who are they in our CRM, or who are they on our website, but who are they as a whole person, across every digital and physical touchpoint? This isn’t just a technical exercise; it’s the strategic imperative that unlocks everything else. It’s the difference between building a brand on solid ground versus shifting sands.
Beyond the Basics: The Three Dimensions of Modern Identity
For many, identity resolution has long been synonymous with deduplicating records in a database—a necessary but unglamorous bit of data hygiene. This terrestrial view, while crucial, is only the first piece of a much larger puzzle. The real challenge, and where many brands falter, lies in connecting that known, first-party data to the sprawling, fragmented digital footprint of the modern consumer. This is where the concept of identity resolution evolves from a simple matching exercise to a complex, strategic capability.
Andrew Frawley, who has spent decades helping brands navigate this terrain, frames the challenge in three distinct dimensions. It’s not enough to simply know your customer’s address; you must connect that to their digital self and, most critically, to their complete persona.
“There’s a couple of different dimensions to identity resolution. There’s sort of what most brands have already accomplished, is sort of…terrestrial data identity resolution where we know where our customers are and we’ve got first party data that describes our relationship with them. The next piece comes in connecting that with the digital world…being able to stitch that together into an identity spine or identity graph that then links back to the terrestrial world is critical. And then the third dynamic or dimension of it is really understanding the full person.”
This multi-dimensional view is fundamental. The first dimension is table stakes. The second—connecting terrestrial to digital—is where the technical complexity spikes, navigating the uncertain world of cookies, device IDs, and various channel-specific identifiers. But it’s the third dimension, understanding the “full person,” that represents the next frontier of competitive advantage. It requires moving beyond the siloed thinking that has defined marketing for decades.
Marketing to the “Whole Person”: Unifying the B2B and B2C Self
The traditional marketing playbook has a clear dividing line. You market to a consumer based on their lifestyle, interests, and demographic data. Or, you market to a business professional based on their title, industry, and firmographic data. The problem, of course, is that these are not two different people. They are two facets of the same individual. The executive researching enterprise software on a Tuesday is the same person who is an avid golfer researching new clubs on a Saturday. Ignoring this connection is a massive missed opportunity.
Frawley argues that one of the most significant missing pieces in modern identity strategy is the failure to link these two personas. Brands that operate in both B2B and B2C markets are often the worst offenders, with entirely separate teams, technologies, and data stacks that never communicate. Breaking down this wall is not just an efficiency play; it’s a strategy for creating deeper relevance and uncovering powerful new avenues for engagement.
“Historically, people have either marketed to people as consumers based on their consumer identity or persona, or professionally based on their business persona. We think you need to market to the whole person, whether they’re the business persona plus consumer persona or vice versa. And we think that’s sort of a missing piece to identity in most brands that we talk to today.”
Consider the practical applications. A financial services firm could identify that a high-value consumer banking client is also the owner of a small business, presenting a perfect opportunity for a cross-sell into business banking services. A telecommunications company could see that a residential cable customer works in IT for a mid-sized company and tailor messaging around business internet solutions. This isn’t about being intrusive; it’s about having the context to make every interaction more valuable. As Frawley notes, it’s a “hard problem to solve,” but leveraging AI-based matching technology to create these links at scale is now a reality, fundamentally changing the calculus of customer acquisition and lifetime value.
The Pendulum Swing: From a Hunger for More Data to a Demand for Quality
For the past decade, the prevailing mantra in marketing has been “more.” More data, bigger audiences, wider reach. We filled our data lakes and CDPs with every signal we could capture, operating under the assumption that more inputs would inevitably lead to better outputs. In the age of programmatic advertising, where the cost per impression was low, this approach was tenable. The noise could be filtered out, and the sheer volume often compensated for the lack of precision.
However, the rise of sophisticated AI changes the equation entirely. AI is a powerful engine, but it runs on data. And if the fuel is contaminated with inaccurate, incomplete, or irrelevant information, the engine will sputter, stall, or worse, drive you in the wrong direction. The old adage of “garbage in, garbage out” has never been more relevant. This reality is forcing a much-needed course correction in the industry, shifting the focus from the sheer quantity of data to its quality, accuracy, and reliability.
“There has been a tendency over the years for marketers to say, ‘I want more data. Just give me a bigger audience.’ And I think now the sort of pendulum needs to swing back to, ‘I need more high quality data.’…I think now when that data is not just delivering a targeted audience, it’s actually delivering content potentially, offers potentially. The game has to go back to quality.”
This shift is a strategic one. It means prioritizing ethically sourced, validated, and observable data. It means investing in the unglamorous work of data curation and hygiene. For marketing leaders, this requires a change in mindset and a change in metrics. Success is no longer measured by the size of the addressable audience, but by the performance lift driven by a smaller, higher-quality, and more accurate data set. When AI is not just selecting an audience but actively generating the creative and the offer for that audience, the cost of getting it wrong skyrockets. Quality is no longer a “nice-to-have”; it is the core prerequisite for effective, AI-driven marketing.
Realizing the Promise: Where Predictive AI and GenAI Converge
The buzz around generative AI is deafening, and for good reason. The ability to create personalized content, imagery, and messaging at scale is a genuine breakthrough. However, this capability has historically existed in a vacuum, separated from the predictive analytics that power targeting and decisioning. We had the ability to identify the right person and the nascent ability to create the right message, but connecting the two in a seamless, automated, and omnichannel fashion has remained elusive.
We are now at an inflection point where these two powerful streams of AI are converging. This convergence, powered by a robust and reliable identity foundation, is what will finally deliver on the long-held promise of one-to-one marketing. It’s a step function change that moves us from a multi-channel approach (where each channel operates independently) to a truly omnichannel one, where each touchpoint has the full context of all previous interactions.
“I think as we see more traditional predictive analytics come together with Gen AI, there’s an opportunity to really take all the promises of digital marketing, one-to-one marketing, whatever sort of name you want to put on it. We’re actually doing that now and doing it on an omni-channel basis, not a multi-channel basis where each touch has the context of the prior touch.”
Imagine a system where predictive models identify a customer at risk of churn. That signal then triggers a generative AI engine to craft a personalized retention offer, delivered in the customer’s preferred channel, with messaging that reflects their known interests (perhaps referencing their status as a small business owner). This closed loop of prediction, creation, and execution, informed by a holistic view of the customer, is no longer theoretical. For marketing leaders, the challenge is no longer a lack of technological capability, but a lack of the foundational data and identity framework required to power it.
The path forward for enterprise marketing leaders is clear, though not necessarily easy. The allure of the latest AI-powered tool is strong, but true, sustainable progress will come from a relentless focus on the fundamentals. Building a reliable, high-quality data foundation and a sophisticated identity resolution capability is not a side project; it is the central strategic pillar upon which all future marketing success will be built. It requires investment, cross-functional collaboration, and a cultural shift away from a fixation on data quantity to an obsession with data quality.
This isn’t about chasing perfection or boiling the ocean. It’s about taking deliberate, strategic steps to better understand who your customers are in their entirety. By bridging the digital and physical worlds, unifying the B2C and B2B personas, and committing to quality, we can finally move from merely reacting to the market to shaping it. We can build the agile, intelligent, and truly customer-centric brands that we’ve been talking about for years, and finally make the vision on that PowerPoint slide a reality.
