The Data Was Always There - Most Marketers Just Forgot How to Look
A Perspective on Behavioral Data, Marketing Illusions, and What Leaders Should Actually Be Paying Attention To
Marketing conversations have become obsessed with dashboards, engagement metrics, and real or near-time attribution. Entire strategies are built around what can be measured instantly; clicks, impressions, and interaction signals that feel precise and actionable.
But, beneath this layer of visibility lies a more durable truth: consumer behavior has always been measurable in ways that run deeper than digital engagement. Understanding that distinction can provide real strategic clarity. And remembering how marketers once modeled behavior can reveal what many organizations are overlooking today.
The Catalina Moment
In the mid-1990s, I was working on the Purina account when I first saw what real consumer behavioral data looked like.
We weren’t talking about clicks, impressions, or digital dashboards as those didn’t exist yet in any meaningful way. We were looking at grocery purchase behavior. Household-level buying patterns. Category crossover. Frequency modeling. Signals that told a remarkably precise story about who someone was, not what they said, but what they actually did.
One of the key players at the time was Catalina Marketing, a company that aggregated point-of-sale grocery data. Through that lens, you could identify pet owners, map their buying cycles, understand brand switching, and even infer broader household behaviors. Not from surveys. Not from declared preferences. From receipts.
One of the most powerful aspects of that data layer was the ability to segment pet owners by relationship type, which allowed us to tailor messaging and test copy against behavior, not assumption.
Fast forward nearly thirty years, and most marketers talk about data as if behavioral insight were born alongside cookies, pixels, and martech dashboards.
It wasn’t. The technology changed. The principle didn’t.
We’ve always been able to model behavior. We just replaced deep signals with louder ones, and over time, many marketers forgot the difference.
The Invisible Consumer Economy
Today, nearly every non-cash transaction leaves a behavioral trail. Online purchases. In-store card payments. Loyalty programs. Subscription ecosystems. Aggregated commerce signals that flow through a vast infrastructure of processors, brokers, platforms, and analytics systems. And it’s not some conspiracy theory. It’s just commerce.
Modern consumer markets run on the ability to understand purchasing behavior at scale. Retailers optimize inventory. Brands model segmentation. Financial institutions assess risk. Marketing organizations attempt to predict intent.
The difference is not whether data exists. It always has. The difference is how visible the machinery has become, and how narrowly many marketing conversations define “data.”
Ask a typical marketing team what drives strategy, and you’ll hear about engagement metrics:
· Clicks
· Opens
· Impressions
· Scroll depth
· Attribution dashboards
Is that data useful? Absolutely.
But these are signals of activity, not identity. They tell you what someone touched, not what they consistently choose with their money. And purchasing behavior is still the most honest form of consumer expression.
The Dashboard Illusion
Modern marketing culture is built around dashboards. Real-time reporting. Performance loops. Attribution modeling. A constant stream of feedback that feels precise, scientific, and actionable.
The danger isn’t that dashboards are wrong. The danger is mistaking visibility for understanding.
Engagement metrics measure interaction, but behavioral data measures commitment.
Someone clicking an ad reveals curiosity. Someone repeatedly purchasing a product reveals identity, priority, and habit. Those are not necessarily interchangeable signals.
Yet many organizations optimize heavily toward engagement performance while underinvesting in true behavioral modeling. The result is a strategy driven by surface activity instead of durable consumer patterns. And this creates a subtle illusion: If the dashboard moves, we assume the strategy is working.
But revenue, retention, and lifetime value are built on behavior, not just impressions.
Dashboards tell you what is happening, but rarely why. Dashboards show you who is reacting. Strategy asks who should matter.
The marketers who understand this distinction don’t reject dashboards. They contextualize them. They recognize that engagement is an input, not a verdict.
ICP Theater vs Behavioral Reality
The phrase “ideal customer profile” gets thrown around frequently in marketing circles. Slide decks fill with demographic attributes, personas, and segmentation frameworks that feel rigorous.
But too often, these ICP models are built from declared signals:
survey responses
brand engagement
social behaviors
inferred intent
What gets lost is purchase-driven identity. Behavioral data answers harder, more valuable questions:
What categories does this household consistently prioritize?
What tradeoffs do they make?
What cycles define their spending?
What patterns repeat over time?
That’s where real segmentation lives.
When marketers rely exclusively on digital interaction signals, they risk optimizing for attention instead of alignment. Campaigns look busy. Dashboards glow. Yet the connection to actual consumer behavior weakens. To be clear, this isn’t a failure of technology, it just lacks proper framing.
The infrastructure to model behavior is more powerful than ever. But it requires a strategic mindset that prioritizes durable patterns over short-term signals.
The Ethics Conversation, Calmly
Whenever data conversations surface, they tend to swing toward alarm or dismissal, and neither is particularly useful.
Behavioral data isn’t inherently unethical. It’s a reflection of how modern commerce functions. The question isn’t whether data should exist, it does, but how organizations use it.
Transparency, consent frameworks, and responsible governance matter. So does realism.
Consumers participate in a behavioral economy every time they transact digitally. Brands operate within that ecosystem to serve, predict, and optimize.
The opportunity, and responsibility, lies in using data to improve relevance, reduce friction, and create genuine value, not simply extract attention. In reality the mature conversation revolves around stewardship, not fear.
What Leaders Should Actually Be Asking
For executives and marketing leaders, the takeaway isn’t to abandon dashboards or chase data nostalgia.
It’s to re-anchor strategy around behavioral truth.
Questions worth asking:
Are we optimizing for engagement signals or purchase-driven patterns?
Do we understand how our customers actually behave, not just how they interact?
Is our segmentation grounded in durable data or marketing theater?
Are we aligning strategy with real consumer economics?
Do we treat dashboards as decision inputs or decision substitutes?
The organizations that win long-term are the ones that recognize a simple reality:
Behavior precedes narrative. Always has.
The future of marketing isn’t louder dashboards or shinier attribution models. It’s deeper behavioral understanding; the same principle that powered early purchase modeling decades ago, now scaled to modern commerce.
The data was always there. The question is whether we remember how to look.
The New Accessibility: Why Size No Longer Limits Sight
There is a lingering myth in the mid-market that the kind of deep behavioral modeling we did for Purina is still locked behind a seven-figure gate.
In the 90s, that was true. To “look” at the data, you needed enterprise contracts and rooms full of servers.
Today, that gate has been kicked open.
Through modern data brokers, retail media networks, and cloud-based clean rooms, mid-sized companies can access the same behavioral “slices” once reserved for global giants. You can now buy the “truth” for a fraction of what we once paid for a “hunch.”
But here’s the catch: The democratized cost of data has created a deficit of discernment.
As software became more user-friendly, we mistook ease of access for ease of understanding. We’ve hired a generation fluent in dashboard mechanics who were never trained to interrogate consumer motive.
The barrier has shifted. It’s no longer access. It’s interpretation.
From Dashboard to Decision
If you’re leading a mid-sized organization, you don’t need a multi-million dollar stack to compete. You need two things:
Strategic curiosity: the discipline to look past the “weather report” of real-time performance and ask, What does this person’s wallet say about who they are?
And a human translator: someone who understands that data is the digital footprint of a human choice. Someone who asks “Why” before “What.” Someone who slices purchase behavior not to improve click-through rates, but to uncover durable customer patterns.
Competitive advantage isn’t database size. It’s the person who knows how to look beyond the dashboard and see the underlying consumer story.
The great thing is that the tools are cheaper, and the visibility is unprecedented.
The question isn’t whether the data exists. It’s whether you have the capability to interpret it.
The data was always there. The only question left is whether we still know how to look.
Perspective by Clint Allen | President & Founder, CLINTONSCOTT
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