Marketing for Scientists Who Hate Marketing

The relationship between science communication and marketing has always been a bit awkward. Mention “marketing” in a research setting and you can almost see shoulders stiffen. People hear advertising. Manipulation. Spin. Someone selling their soul to a slogan.

Which is ironic, because most of the time what they are reacting to is not marketing at all, but a caricature of it.

Advertising is loud. It’s a megaphone. It takes a message and pushes it out into the world, hoping it sticks somewhere.

Marketing, in its adult, less embarrassing form, does something very different. It listens first. It watches. It maps. It asks who people are, what they already know, what they fear, what confuses them, what they care about, and—crucially—what they don’t yet realise is relevant to them.

Only after all this does marketing start speaking.

This is why marketing begins with research. Not with slogans, not with visuals, not with calls to action. It studies the terrain before opening its mouth. It looks at audiences, barriers, motivations, competing narratives, context. Communication comes later. Sometimes much later.

There is a sentence that often makes scientists uneasy: “marketing creates needs.” It sounds manipulative, almost immoral. But in practice, creating a need is rarely about inventing desire out of thin air. It is about making relevance visible.

People don’t feel a spontaneous need to know about glacial melt in the abstract. They feel a need to understand why water becomes scarce, why food costs more, why insurance premiums rise, why certain areas become uninhabitable, why their electricity bill keeps climbing. The need is already there. What’s missing is the bridge.

And building bridges between distant facts and lived life is exactly where science communication and marketing quietly overlap—especially in popular science writing and media outreach.

If you translate marketing logic into science communication, something subtle but important happens. The starting question shifts.

Instead of asking:
“What do I want to say about my research?”

you begin to ask:
“What do people need, want, or fear enough to stop scrolling?”

That shift changes everything. You no longer start from the paper. You start from the public’s information needs.

Those needs are rarely abstract. They sound more like:
“Is this dangerous for me or my family?”
“Does this change what I should do tomorrow?”
“Who benefits, and who pays the price?”
“Is this solid evidence or just hype?”
“What’s the one thing I need to understand so I’m not fooled?”

Sometimes the need is already felt. A heatwave, a flood, a disease outbreak does the job for you. Sometimes it isn’t—because the topic feels distant, technical, or emotionally outsourced to “future generations” and polar bears. Climate science lives here most of the time: acknowledged, accepted, and neatly parked somewhere far away from everyday decisions.

This is where science communicators end up doing a very marketing-like job. Not manipulating. Translating.

“Glacial melt” stops being an icy abstraction and becomes water availability, food prices, insurance costs, coastal infrastructure, migration, geopolitical instability, and the very unromantic question of how expensive life gets when the climate stops behaving.

That translation is not a rhetorical trick. It is the work.

A lot of communication failures happen because researchers, without meaning to, write for themselves.

Inside the research community, expectations are clear and legitimate. You explain where the work sits in the field, what’s new compared to previous studies, how it was done, what materials and methods were used, how robust the design is, what the limitations are, and how cautious the conclusions should be.

Outside that community, people are operating with a different set of questions. They usually start from “Why should I care?” and “What does this mean for me?”

This is not a clash of intelligence. It’s a clash of needs.

When researchers communicate by default, they tend to offer background, methodological detail, and careful framing. When the public listens by default, they are looking for the point, the consequence, the takeaway, and a sense of what is reliable and what is still uncertain.

This is not dumbing down. It is sequencing.

A well-written popular science article can absolutely include methods and context. But you earn that attention after you establish relevance. You don’t open with the equivalent of “Once upon a time, in a dataset far, far away…”

Marketing teaches this lesson mercilessly: people don’t buy features, they buy outcomes. In the same way, many readers don’t engage with methods, they engage with meaning.

I often use a very unscientific example to make this point: washing machines.

Imagine a company designing a new washing machine. Before building anything, they study the market. They look at competitors, at what already exists, at what differentiates them. They talk to users and discover what people actually struggle with: high energy bills, limited capacity, long cycles, machines that break too often.

So they design a product around those needs.

Now look at how they communicate it.

They don’t start by explaining the history of washing machine engineering. They don’t lead with a full inventory of internal components or a detailed description of the production process. They lead with conclusions, because that’s what customers are buying:

Uses less water.
Fits a full weekly load.
Fifteen-minute quick cycle.
Quiet at night.
Lower energy bill.

If a material choice matters to the user—say, a filter that reduces microfibres or a more durable drum—it becomes part of the story. But only because it maps onto a real-world value.

Science communication works in the same way.

In the lab, researchers already do something very close to marketing research. They scan the literature, identify gaps, compare approaches, and design studies that add something new. That internal logic is rigorous and non-negotiable.

But when communicating with the public, you don’t default to that internal logic. You start from the external logic of the reader.

You translate your research into the problem it addresses, the outcome it changes, the risk it reduces, and what it means for decisions, policy, health, environment, or everyday life. Then—if the story requires it—you bring in methods as a tool for trust.
“Here’s how we know.”
Not “Here’s how we suffered.”

The phrase “creating the need to know” still makes many scientists uncomfortable. Understandably so. In science communication, the ethical version of that idea is not manufacturing urgency, but revealing relevance.

Some audiences are already worried but confused. Others are calm because the topic feels remote. In both cases, the task is the same: connect the dots responsibly.

Take glacial melt again. You create a need to know by showing local links, economic consequences, timeframes that are already measurable, and the decisions currently on the table—who makes them, and with what evidence.

You are not selling a product. You are selling attention in a crowded ecosystem, so that evidence has a fighting chance against vibes.

In practice, adopting marketing thinking doesn’t turn science communication into promotion. It makes it more deliberate.

It pushes communicators to research audiences before writing, to distinguish between what people think they need and what they actually need, to lead with the “so what” and then earn the “how,” and to treat attention as a scarce resource rather than an entitlement.

Paradoxically, this often makes communication more honest, not less. Because instead of dumping information and calling it public engagement, you are answering the only question that really matters to the reader: why should I care, and what does this change?

At its best, marketing is empathy supported by evidence.
At its best, science communication is exactly the same thing—just with fewer slogans and the footnotes kept politely in the background, ready when needed.