Relevancy Is a Behavioral Problem (Not a Personalization Problem)

Most email programs talk about “relevancy” like it’s a content-matching exercise: pick the right product, insert the right name, send at the right time. But relevancy isn’t a property of the message. It’s a judgement the reader makes in context, with limited attention and competing priorities.

Behavioral analysis helps because it starts where relevancy actually happens: in the recipient’s mind, under uncertainty, trying to decide what’s worth engaging with right now.

Observation or Tension: “Relevancy” Keeps Getting Treated Like a Feature

Email teams tend to inherit a quiet assumption: if we can just know more about the customer, we can be more relevant. More fields. More segments. More “personalization.” More confidence that the next send will feel like it was made for them.

And to be fair, sometimes it works, especially when the behavior is straightforward (a replenishment cycle, an abandoned task, an obvious preference). But most of the time, relevancy erodes in ways that don’t show up in the data model. Open rates wobble. Clicks get weird. Conversion looks fine one week and inexplicable the next. Everyone agrees the program needs to be “more relevant,” which is marketing’s version of saying “it should be better, somehow.”

The underlying issue is that relevancy isn’t a static match between a person and a message. It’s a moment-to-moment evaluation: Is this worth my attention, right now, given what I’m trying to do and who I’m trying to be? That’s not a CRM question. That’s a behavioral one.

Behavioral Reframing: Relevancy Is an Inference People Make

Behavioral science is useful here because it treats human action as an output of context, cognition, and motivation, not as a stable expression of identity.

When someone sees an email, they don’t consult their “preferences” like a database lookup. They make fast inferences:

• What is this asking of me? (time, effort, money, attention)

• What does it imply about me? (am I the kind of person who wants this?)

• What happens if I ignore it? (usually: nothing, which is part of the problem)

• Is this for “now me” or “future me”? (future me is always more ambitious and rarely checks the inbox)

  
  

These inferences run on heuristics because they have to. Email arrives into a limited-attention environment. The brain’s job isn’t to be fair to your messaging strategy; it’s to triage.

Behavioral analysis helps because it shifts the focus from “Who is this person?” to “What is this person doing, repeatedly, in this environment—and what does that suggest about their current constraints and motivations?”

In other words: behaviors are often more honest than attributes. Not because behaviors are perfect, but because they’re closer to the decision.

Implications for Marketing: Email Is a Behavioral Environment, Not a Content Feed

If you treat email as a behavioral environment, relevancy becomes less about “the right offer” and more about “the right interpretation.”

A few implications fall out of that:

Engagement is not interest. It’s friction management.

Clicks (and non-clicks) often reflect effort, uncertainty, and timing more than desire. Someone can want the outcome and still avoid the step. Someone can click out of curiosity and still feel no intent. Behavioral analysis looks for patterns of follow-through rather than isolated signals.

Relevancy improves when you stop over-reading single events and start reading trajectories: Does this person escalate effort over time? Do they return after lapses? Do they explore broadly or narrow quickly? That’s motivation in motion.

“Same segment” is not “same state.”

Two customers can look identical on paper and be in entirely different motivational states. One is browsing with low commitment. The other is trying to resolve uncertainty. Same category interest, same lifecycle stage, completely different psychological job-to-be-done.

Behavioral analysis tends to surface state-based differences—like deliberation versus impulse, exploration versus commitment, confidence versus ambiguity. Those states are what make an email feel relevant or tone-deaf.

Relevancy is often about timing, but not “send time.”

Timing in the behavioral sense is about readiness. The question isn’t “When are they most likely to open?” It’s “When does this message align with what they’re trying to accomplish—emotionally and practically?”

Readiness shows up indirectly: shorter paths to action, decreased back-and-forth, fewer “sampling” behaviors, more consistency. When your program responds to readiness, messages start feeling like they arrive at the right point in the story, not just at the right hour.

Deeper Insight or Constraint: Behavioral Data Doesn’t Magically Explain Behavior

Here’s the part that makes behavioral analysis valuable—and also easy to misuse: behavior is informative, but it is not self-interpreting.

A click can mean: “I want this,” “I’m uncertain,” “I’m killing time,” “I need to justify this,” “This subject line felt urgent,” or “I mis-tapped on mobile.” Behavioral analysis isn’t about treating behavior as truth. It’s about treating behavior as evidence.

That means a few constraints matter:

People are inconsistent on purpose

Humans aren’t noisy versions of rational agents. We have competing goals. We protect optionality. We avoid regret. We procrastinate. We indulge. We plan. We do all of it, often in the same week, sometimes in the same hour.

So the goal isn’t to eliminate inconsistency by segmenting harder. It’s to design interpretations that can tolerate it—by focusing on repeatable patterns, not perfect classification.

More signals can create more false confidence

One of the quiet dangers of modern marketing analytics is narrative overreach: the sense that because you can label behavior, you understand the person.

Behavioral analysis is most powerful when it stays humble: it helps you make better bets, not definitive conclusions. Relevancy increases when you treat models as provisional and your customers as context-sensitive.

The inbox punishes misread intent

Email has a unique downside: it’s persistent and cumulative. A single off-note message doesn’t just underperform; it can change how the next message is interpreted. People don’t evaluate each email in isolation. They evaluate the relationship implied by the stream.

Behavioral analysis helps here because it encourages you to think relationally: What expectation is your program setting? What pattern is it teaching the recipient to anticipate? Relevancy is as much about trust in your future messages as it is about fit in the current one.

Reflective Close: Relevancy Is What Happens When You Stop Guessing at Identity and Start Respecting Context

The promise of behavioral analysis isn’t that it lets you target people with uncanny precision. It’s that it forces a more realistic question: Given what this person is doing, and what the inbox environment does to attention, what would feel legitimately worth engaging with?

If you want email to feel more relevant, you don’t necessarily need more personalization. You need better behavioral hypotheses about motivation, friction, readiness, and the shifting mental math recipients do when they decide whether something belongs in their day.