- Tension: Marketers are told that more data means better campaigns—but treating data as the answer often disconnects us from the real goal: making people feel seen.
- Noise: Frameworks, tech stacks, and automation hype oversimplify the relationship between data and relevance, turning marketing into an exercise in precision rather than resonance.
- Direct Message: Data-driven marketing only works when relevance stays personal—not just accurate.
Read more about our approach → The Direct Message Methodology
We like to think of marketing as a science now.
Campaigns are built on platforms, not instincts. Personalization is programmatic. Metrics flow in real time. If we know the age, zip code, and device type of a lead, we assume we know who they are. And in that assumption, something slips.
We forget that relevance isn’t the same as precision. That knowing someone’s data isn’t the same as understanding their story.
And this is where modern data-driven marketing faces its quiet reckoning.
Because behind every segmentation strategy, every clustering model, every “next best offer” engine—there’s a person simply wondering: Does this feel like it’s for me?
That’s the only question that matters.
And data, for all its promise, doesn’t always help us answer it.
Joel Lockwood, president and partner at Ozone, understands this tension. He’s worked with companies who’ve built beautiful campaigns—targeted by industry, interest, purchase history, even psychographic profiles—only to miss the mark emotionally. The copy was right. The channel was optimized. But it didn’t connect.
Why?
Because the segmentation assumed too much. It knew what the prospect had done, but not why. It knew what bucket they belonged to, but not what mattered to them in that moment.
This is the core dilemma in data-driven marketing: you can get closer and closer to someone demographically and still be far from relevance emotionally.
That’s not to say data doesn’t matter. It does. But only if it serves the message—not replaces it.
Lockwood puts it simply: “Relevance drives performance. And relevance comes from combining what you know with why it matters.”
Let’s break that open.
Getting the data together—internal CRM records, behavioral signals, external overlays like Nielsen PRIZM or firmographics—matters. But raw data doesn’t deliver value. Structure does. Interpretation does. Narrative does.
Progressive profiling, short surveys, form fills—these help marketers layer context over content. They enable nuance. But the goal isn’t complexity. It’s clarity.
Once you have the data, you have to shape it into segments that feel real.
Not just “Females 25–34.” Not just “IT leaders at mid-sized firms.”
But: people who buy in bursts and then go quiet. People who self-research but still call a rep before committing. People who act impulsively—until you ask for money.
These behaviors are what create emotional entry points.
This is where static and dynamic clustering comes in.
Static clusters, like those from Claritas or Experian, organize people by location, shared traits, or market resemblance. It’s helpful—but generic.
Dynamic clustering, built in-house or via AI-assisted modeling, allows marketers to group people by actual behavior, purchase history, and evolving engagement—without assuming geography equals identity.
And here’s where things get interesting: often, dynamic clusters produce fewer segments. Why? Because they’re anchored in real differentiation, not broad demographic assumptions.
Fewer segments mean more focused creative. More tailored journeys. Fewer diluted campaigns.
But here’s the trap: once we’ve got the segmentation, the targeting, and the profiles, we assume our job is done.
That’s the oversimplification that quietly erodes campaign impact.
- We build personas—but treat them as static.
- We map out “next best offers”—but don’t adjust when timing changes.
- We test subject lines—but not the emotional logic beneath them.
As Lockwood emphasizes, campaigns don’t fail because the data was wrong. They fail because the message never made anyone care.
Persona-based messaging has to evolve with the person. Baby Boomers aren’t a monolith. Gen Z isn’t a moodboard. And even within a firmographic segment, emotional context changes everything: are they curious? Stressed? Skeptical? Ready to decide? Or just looking busy on a Thursday?
Data can’t always tell you that. But it can help you listen better—if you’re willing to adjust.
The Direct Message
Data-driven marketing only works when relevance stays personal — not just accurate.
This is where integrative balance comes in. The balance between data and judgment. Between what the dashboard says and what your gut suspects. Between automating for scale and writing for one person, clearly, honestly.
Use the data—but don’t outsource your voice.
Refresh your segments—but stay curious about what they’re not telling you.
A/B test your offers—but ask why the B variant worked. Was it timing? Tone? Or did it simply speak to something real?
Above all, understand that relevance is a moving target.
People change. Preferences shift. Contexts evolve.
This is why refreshing your data isn’t a maintenance task—it’s a creative one. It’s not just about keeping fields updated. It’s about asking new questions:
- What else do we need to know to serve them better?
- What behavior did we misread?
- What silence are we not noticing?
Lockwood’s teams keep returning to one idea: use data to open a door—not to build a wall of assumptions.
It’s easy to hide behind numbers. To run reports. To optimize clickthroughs. But relevance—the kind that makes someone say, Yes, this is for me—only emerges when marketing becomes a conversation again.
Not a blast.
Not a sequence.
A real, empathetic conversation.
So yes, collect the data. Build the models. Segment, cluster, personalize.
But don’t forget what you’re actually doing.
You’re not pushing a product. You’re pulling someone in.
And that doesn’t happen because you know their ZIP code. It happens because you knew what to say—when it mattered most.
