What life science marketing can learn from cognitive biases

Cognitive biases in Marketing
Saturday evening negotiations with my father taught me early on how decisions are won. Years later, I learnt that what really moves people when they decide has a name: cognitive biases. Sunk cost, confirmation bias, anchoring and overconfidence shape more of our marketing decisions than we like to admit. Here is how they show up in life science marketing, and how to catch them.

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What life science marketing can learn from cognitive biases

Rhetoric and communication theory have interested me for as long as I can remember.
My father was an old-school salesman who loved winning people over with a good argument, and I loved listening to him talking to his clients; to me, it was fascinating to watch him at work. So I started practising myself, because presenting well and convincing people seemed like a real skill to have, and it certainly came in handy when negotiating with Dad.
My proudest victories were the extra hours I won on Saturday evenings. Years later I understood that he could easily have argued me down, and that he only let me win when I had really convinced him. He liked a good challenge, my father.

My sales trainings came later, when I started out in sales myself, and after them the marketing lectures.
Both touched on the same kind of question: how do I get people to choose my solution over the competition?

Somewhere along the way my curiosity shifted towards what actually moves people when they make such a decision, and yes,
I wanted to understand how to make it work in my favour.

It was in one of those lectures that it finally had a name: cognitive biases, or rather a whole family of them, with a solid body of research behind each one. What I had half-noticed as a quirk turned out to be systematic and predictable, something psychologists have been demonstrating since the 1970s.
I have been fascinated by it ever since, and it changed the question I was asking.

Why am I telling you this? Because understanding how people form judgements is a peculiar kind of knowledge: you can use it in two directions. You can use it to steer other people’s decisions without them realising, or you can turn it on yourself and look at how your own decisions are being made. In scientific marketing, where customers have to defend every purchase internally and scientifically, I only find the second one worth doing.

So this article is about us: four biases that come up again and again in life science marketing decisions, and the methods research suggests for catching them.

 

Sunk cost: when your past becomes the new argument

Let’s start with THE congress, and you probably know THE ONE.

You have been going there for years: great booth, good location, the teams fly in (yes, teams, including the ones nobody asked for, but how to turn an event into a real success is a topic for another day). Nobody is quite sure any more whether it brings business, but cancelling somehow feels worse than going. “We’ve always been there.”

That sentence is the sunk cost effect speaking. We keep investing in something because of what we have already put into it, rather than what it is still worth, and research suggests part of the reason is disarmingly simple: we don’t want to be wasteful, or to look it. Holding on is sometimes the right call, even when you can’t see a clear return. I remember one congress like that: the event of the year, where all the stakeholders went, the one place to see all your customers at once. It was fantastic, even though in the short term it cost more than it brought in.
The trouble starts when the past investment becomes your only argument.

Two things help here. The first is a question I keep coming back to: if I were deciding today, with what I know now, would I start this? The second is more practical: before the next congress, agree on what it needs to deliver for you to go again, and write it down. When the moment comes, you compare against that target instead of against the years already spent. In one well-known study on escalating commitment, setting minimum targets in advance was among the most effective ways to help people step back from a losing course.

And here’s the reassuring part: this is a bias people can learn to catch. In experiments, a short training in cost-benefit reasoning changed how students handled sunk costs, and the effect was still there two weeks later.

 

Confirmation bias: when you only see what you hoped to see

This one is quieter, and it lives in our dashboards. LinkedIn impressions go up, and we conclude the strategy is working; they stay flat, and we conclude LinkedIn is dead. Same kind of data, and both times it confirms exactly what we thought before we opened it.

Confirmation bias is our tendency to look for what supports our beliefs and to pass lightly over what doesn’t, and it is one of the best-documented biases in psychology.

One of the best-studied remedies is surprisingly simple: consider the opposite. Before deciding what the numbers mean, ask yourself what could make your first interpretation wrong. Say your LinkedIn post reaches 500 impressions. Before you celebrate, turn the question around: what if those 500 mean very little? Who were these people, were they your target audience, and did any of them comment, click or get in touch?

It works better than good intentions. In a classic experiment, one group was told to consider the opposite, another simply to be as fair and unbiased as possible. The first group showed noticeably less bias, so wanting to be fair was not enough; a concrete technique made the difference. More recently, a single training session made business students noticeably less likely to choose the answer that merely confirmed their first hunch, in a management case they hadn’t been told was part of a study.

There is a small catch, though: one or two counter-arguments are enough. Try to come up with ten, run out of ideas halfway, and you may end up feeling more confident than you should. If that sounds like too much effort, this is a good place to let AI help: ask it explicitly to argue against your conclusion.

My own version is a small question I ask before I look at any numbers: what am I hoping to see? If the answer comes too quickly,
I have usually already decided what the data means.

 

Anchoring: when you believe the number that came first above all else

Think of the market size on slide three. It came from a report someone bought when the company was founded, and it has travelled from document to document ever since: into the business plan, the investor pitch, the launch targets. Each time the plan is updated, the figure gets nudged a little, but it is rarely calculated again.

If that feels far from your own desk, think of buying a house. In a classic field study, students and experienced estate agents toured the same property and valued it, and both groups were pulled towards whatever asking price they had been shown, although they had plenty of other information to go on.

That is anchoring: the first number becomes the reference point for everything that follows. Even when we revisit it, we tend to adjust from it instead of starting afresh, and the adjustment usually falls short. It is one of the most studied findings in decision research.

Considering the opposite works here too, applied to numbers. Before building on a figure, ask what would argue for a very different one. If we sized the market again today, from scratch, would we really arrive at the same number? In experiments, this reduced the pull of the anchor, though it didn’t remove it entirely. The anchor is sticky; the best we can do is loosen it.

Anyone who has worked in sales knows this from the other side of the table. In negotiations, the first number named tends to shape where the deal ends up, which is a good reason never to let a figure slip out before you have done the homework to stand behind it.

 

Overconfidence: when certainty feels like competence

Overconfidence is the hardest one to spot, because from the inside it feels like competence. The new test is clearly better than the technology labs use today, so the forecast assumes they will switch within the first year. The plan rests on everything we know about our product, which is a great deal, and on very little about how such switches have gone for others. It’s a trap start-ups fall into particularly easily, and no wonder: their product clearly is cutting-edge. The critical question is whether their market studies give an objective picture of the market, because that is where the bias tends to creep in.

Psychologists call this the inside view: our project, our strengths, our plan. Forecasts built this way tend to be too optimistic, about time, cost and results alike.

The counterweight is the outside view. Instead of asking how our launch will go, ask how launches like it have gone. How long did labs take to adopt comparable technologies, and what happened to others who promised a similar shift? Then move your estimate towards that picture, even if your product really is better.

A second, lighter tool is the pre-mortem. Before the plan is final, imagine it is a year later and the launch has disappointed, and ask what went wrong. Picturing an outcome as already settled makes it easier to find reasons for it. The research behind the pre-mortem itself is still thin, but I find it a useful way of making doubt speakable in a team, and it helps to have a plan B ready and to think through the worst-case scenario before it ever happens.

 

Isn’t there more?

Yes, plenty. There is one in particular that I left out on purpose: the availability heuristic, our habit of judging how important something is by how easily an example comes to mind.

I have far too many stories about it from my years in product management to squeeze them into a paragraph, so it will get an article of its own.

Looking back

Those Saturday evening negotiations turned out to be a good school. My father could have talked me down every time, and I think I knew it, so I learned to make my case properly, because a good argument was the only thing that worked. Knowing about biases hasn’t made me immune to them, but it has made me a little quicker to notice when something other than the argument is doing the deciding.

 

 

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